The production and usage of Computing products is currently accountable for 4% of the global Carbon dioxide (CO2) emissions, which according to the United Nations (UNFCCC) is the main driver for global warming. CO2 emissions are expected to rise to 8% by 2025 with pessimistic forecasts noting that Computing will require over 100% of the current global energy production by 2040 (translated to approximately 30% of global CO2 emissions)! On the other hand, computing can play a pivotal role in the reduction of CO2 emissions in variety of other sectors (i.e., mobility, industry, buildings, agriculture and electricity/heat production). Sustainable Computing in particular, is concerned with the application of computer science principles, methods, and tools to problems of environmental and societal sustainability. In this talk, I will provide an overview of the Sustainable Computing domain and highlight our results to the renewable self-consumption problem, which refers to the process of intelligently consuming energy at the time it is available. This stabilizes the energy grid, minimizes energy dissipation on power lines, does not require the production and deployment of expensive battery storage. I will detail solutions to the renewable self-consumption problem using algorithms and systems from the smart-home domain (https://greencap.cs.ucy.ac.cy/) as well as the domain of electric vehicles (https://ecocharge.cs.ucy.ac.cy/), showcasing how the alignment of energy production and consumption can lead to a sustainable future.
Internet-of-Things (IoT) describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet. These objects are projected to outpace the number of humans on the Internet, namely, by 2030 there will be 8.6B humans according to UN DESA and over 500B IoT devices according to Cisco. Huawei predicts that this will create yottabytes (YB) of machine-generated spatio-temporal data every year requiring a complete rethink on how we collect and store data, how we process it in a timely manner and how we generate value and utility out of it. In this talk, I will present three pillars of IoT data management research in the scope of systems related to indoor localization, telecommunication big data and smart spaces. I will start out with the presentation of data-driven localization algorithms for mobile devices in systems with: (i) no telecommunication infrastructure; (ii) disconnected handheld operation; and (iii) inherent privacy constraints of users. I will proceed with the presentation of data decaying operators for telco big data systems, which abstract IoT influx data into compact machine learning models that can be stored and queried when necessary. I will conclude with scheduling operators of IoT devices inside smart spaces to reduce the environmental impact of human activity. I will conclude with an outlook to my current and future research agenda.
The early suppression of fires on ro-ro vessels requires rapid fire identification as a fire of medium growth exponentially reaches 50kW only 1 minute. Fire patrol members (e.g., able seamen) are asked to act as first responders in such fire incident cases. They do lack the necessary digital technology for immediate localization, verification and coordination with the bridge and other first Indoor localization requires dense referencing systems (such as Wi-Fi, UWB, Bluetooth antennas), but these technologies expensive installations and maintenance. Also, Satellite-based indoor localization is obstructed by the bulky steel structures of so this doesn’t work either. In this work we develop a ground-breaking localization technology that requires zero using computer vision on commodity smartphone devices attached to the gear of first responders. The developed solution of three steps: (i) Training, where vessel owners supply video recordings that are processed on a deep learning data center to an accurate computer vision machine learning model; (ii) Logging, where a mobile app allows referencing non-movable objects the (x,y,deck) coordinates of a vessel; and (iii) Localization, where first responders localize on a digital map. Additionally, in case a communication network is available, first responders can share their location, emergency messages and heat scan images with first responders and the bridge. Our proposed algorithm, coined Surface, is shown to be 80% and 90% accurate for localization and tracking respectively, in both a remote study and an on-board study we carried out on a real ro-ro vessel. The overall developed Smart Alert System streamlines the lengthy fire verification, coordination, and reaction process in the early stages of a fire, improving fire safety. talk will conclude with a summary of other relevant work in the scope of our open-source indoor localization system named Anyplace. More: https://anyplace.cs.ucy.ac.cy/
The rise of temperatures on Earth have alerted communities around the globe to devise immediate solutions to help curb the severe effects of Climate Change, which is attributed to greenhouse gas emissions caused by human activities. Even though the Computing field is a cause of emissions in its own right, it also has the potential to increase the efficiency of human workflows in all sectors, such as transportation, buildings, energy & heat production, industry, agriculture and livestock, etc. In this panel discussion, we start out with a general overview of terminology, factors, metrics, and objectives related to climate change, and then survey: (i) Green Conferences; (ii) Green Mobility; (iii) Green Cities and (iv) Green Smart Spaces. The participants are expected to bring into the discussion their own perspectives from the academic, governmental, and industrial sector to report on how they perceive the future of the Computing Field in a future shaped by Climate Change, and how we can all help achieve the goals of the Paris Agreement.
The COVID-19 global pandemic emerged in the 2019 causing so far a massive global health disruption with fatalities and huge economy impact, enforcing most if not governments to a global lockdown. Besides the battle on the front-line, governments and the industry also massively the deployment of information and communication to track and curb the spread of the virus. On front-line of these efforts, have been the so-called Mobile Tracing Applications (MCTA). These refer to mobile apps exploit the rich ecosystem of mobile sensors (e.g., location, as well as social networks to facilitate the process identification of persons who may have been previously into with a covid infected person and subsequent collection of information about these contacts. Although MCTA can theory help governments fight the rapid spread of diseases COVID-19, there are important privacy considerations and claim that these technologies will put in place a massive surveillance infrastructure that will survive even when vaccine for the COVID-19 disease has been found. This panel to discuss the major challenges and open topics surrounding The panelists are expected to bring wealth of experience vision from the academic governmental and industrial sector answer a set of challenging questions that are currently open public debate as well as the global benefits one can expect when fighting the COVID-19 spread.
The goal of this session is to discuss the ongoing climate crisis and the role of EBDT/ICDT in this crisis. It will consist of a short introductory presentation by the chairs followed by an open discussion about the carbon footprint of the EBDT/ICDT conference, and the research opportunities that can help address the crisis.
Big data architectures have transformed the way enterprises collect, store and analyze massive amounts of spatio-temporal data from the mobile workforce. In this talk, I will present operators and systems for querying and managing such Mobile Big Data (MBD), that address challenges of performance, privacy, utility and network efficiency. I will start out by overviewing Anyplace, our open-source Internet-based Indoor Navigation service that won several international research awards for its accuracy and utility. Particularly, I will discuss the challenges in prefetching and privacy-aware data processing of Indoor MBD (e.g., Wi-Fi and magnetic signals). My talk will be succeeded by a summary of Rayzit, which is an award-winning location-based crowd messaging service that allows a mobile crowd to instantly connect to its k Nearest Neighbors (kNN) as they move in space. I will particularly focus on Spitfire, which is a distributed algorithm that provides a scalable and high-performance All k Nearest Neighbor processing operator to Rayzit. I will then overview Spate, which is a novel spatio-temporal data processing and analytic system for telecommunication data (e.g., CDR and MR), which makes compression and decaying a first-class citizen. Particularly, I will overview a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Finally, I will be concluding with an outlook to our current and future research agenda.
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. In this talk, I will present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work. More: https://anyplace.cs.ucy.ac.cy.
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. In this talk, I will present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.
A Telecommunication company (Telco) is tradition- ally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. The ability to perform analytics on the generated big data within a tolerable elapsed time and share it with key Smart City Enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future Smart Cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. The ability to perform analytics on the generated big data within a tolerable elapsed time and share it with key Smart City Enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future Smart Cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.
Geographic Information Systems (GIS) have enabled a vast range of applications in outdoor spaces, but these systems are bound to accurate localization technologies that are not available inside buildings where people carry 90% of their activities. Additionally, GIS don 't address the unique characteristics of complex indoor environments off-the-shelf. At the same time, we witness the uptake of a new class of Indoor Information Systems (IIS), which store indoor spatial models along with sensor signals (e.g., wireless, light and magnetic) used to localize users. Such IIS might be considered as specialized GIS applications that are tailored to the unique challenges pertinent to indoor spaces, namely new indoor data management operators, new indexes, new data privacy schemes, built-in data-driven localization algorithms, models to crowdsource IIS data and these might even use NoSQL architectures. This panel will explore how the academia and industry are tackling the future challenges that rise in the scope of IIS. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management and mobile data mining communities should contribute to.
Big data architectures have transformed the way enterprises collect, store and analyze massive amounts of spatio-temporal data from the mobile workforce. In this talk, I will present operators and systems for querying and managing such Mobile Big Data (MBD), that address challenges of performance, privacy, utility and network efficiency. I will start out by overviewing Anyplace, our Internet-based Indoor Navigation service that won several international research awards for its accuracy and utility. Particularly, I will discuss the challenges in prefetching and privacy-aware data processing of Indoor MBD (e.g., Wi-Fi and magnetic signals). My talk will be succeeded by a summary of Rayzit, which is an award-winning location-based crowd messaging service that allows a mobile crowd to instantly connect to its k Nearest Neighbors (kNN) as they move in space. I will particularly focus on Spitfire, which is a distributed algorithm that provides a scalable and high-performance All k Nearest Neighbor processing operator to Rayzit. I will then overview Spate, which is a novel spatio-temporal data processing and analytic system for telecommunication data (e.g., CDR and MR), which makes compression and decaying a first-class citizen. Finally, I will be concluding with an outlook to our current and future research agenda.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. Most traditional techniques for computing AkNN queries are centralized, lacking both scalability and efficiency. Only recently, distributed techniques for shared-nothing cloud infrastructures have been proposed to achieve scalability for large datasets. These batch-oriented algorithms are sub-optimal due to inefficient data space partitioning and data replication among processing units. In this talk I will present Spitfire, a distributed algorithm that provides a scalable and high performance AkNN processing framework. The proposed algorithm deploys a fast load-balanced partitioning scheme along with an efficient replication-set selection algorithm, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. I will also overview Rayzit, an experimental and open-source mobile AkNN service we established and operate that reached over 45,000 real user interactions to this date.
The advances of Internet-of-Things (IoT) technology in recent years is leading to the uptake of a new class of Internet-based navigation services, which might soon diminish the need of Satellite-based technologies in urban environments. These services rely on geolocation databases that store spatial models along with wireless, light and magnetic signals used to localize users. Developing IoT-based navigation services creates a new spectrum of information management challenges ranging from crowdsourcing indoor models, acquiring and fusing big-data velocity signals, localization algorithms, location privacy of custodians and others. In this talk, I will overview the current landscape of academic and industrial such services using a multi-dimensional taxonomy of emerging topics in this domain, including location, crowdsourcing, privacy and modeling. I present the dimensions of our taxonomy through the lens of an open, modular, extensible and scalable navigation architecture, coined Anyplace, concluding with open challenges.
Spatial big data architectures have transformed the way enterprises collect, store and analyze massive amounts of velocity data that features a spatial extend. In this talk, I will summarize an array of research problems and applications that our laboratory has investigated or aims to investigate in this scope. I will start out by overviewing Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house Internet-based Indoor Navigation service that won several international research awards for its accuracy and utility. Particularly, I will discuss the challenges in processing and visualizing indoor big data (e.g., Wi-Fi and magnetic signals). My talk will be succeeded by a summary of Rayzit (https://rayzit.com/), which is our award-winning location-based crowd messaging service that allows a mobile crowd to instantly connect to their k Nearest Neighbors (kNN) as they move in space. I will particularly summarize Spitfire, which is a distributed algorithm that provides a scalable and high-performance All k Nearest Neighbor processing framework to Rayzit. I will then overview Spate, which is a novel spatio-temporal time machine for telecommunication data (e.g., CDR, NMS, PCHR). Spate supports a range of visual analytic cues (heatmaps, POI clusters, etc.) that enables a Telco operator to quickly compare abstract models to real velocity data but also to monitor and replay network traces using a declarative SQL language that breaks down to SPARK jobs. I will finally also introduce GreenCharge, which is a big data GIS architecture to guide electric vehicles to green energy excess. GreenCharge is expected to maximize self-consumption of electricity by prosumers, contributing in that way to the stability of power grids and a sustainable future. GreenCharge is anticipated to be pilot at the University of Cyprus, a self-sufficient 17GWh/annum producer of solar electricity.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which can provide the location of a user upon request. Unfortunately, those technologies are both inaccurate and additionally raise important location privacy concerns, as the IIN can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests both of which are shown to be resilient to a variety of privacy attacks. In the third part of this talk, I will focus on an innovative framework for accurate fast indoor localization over an intermittently connected WiFi coined Prefetching Localization (PreLoc).
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which provide the location of a user upon request using structures called RadioMaps (RMs). In this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an innovative framework for accurate fast indoor localization over an intermittently connected WiFi coined Prefetching Localization (PreLoc).
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which can provide the location of a user upon request. Unfortunately, those technologies are both inaccurate and additionally raise important location privacy concerns, as the IIN can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests both of which are shown to be resilient to a variety of privacy attacks.
Research entails a deep systematic investigation of sources in order to reach new, previously unknown conclusions. In Computer Science, these conclusions are often in the form of algorithmic developments or in the form of software artifacts tackling unconventional, real-life challenges. In both cases, the conclusions have to be publishable at high caliber journals, conferences or competitions, such that the credibility of the developments is externalized and recognized by the wider research community. While the above type of research is a fundamental attribute of postgraduate studies, undergraduate students are often not part of this process for a variety of reasons, including: i) lack of advanced domain knowledge (usually taught at the post-graduate level); ii) lack of mathematical or laboratory skills (e.g., proving a theorem or managing a complex software stack); and iii) lack of writing and presentation skills (i.e., the dissertation is written at the end of their studies). During the last decade, I have applied a wide range of empirical methods to engage undergraduate students in research as soon as they are admitted to the University. Particularly, students have been encouraged through a variety of methods to familiarize themselves with research through our courses and become active contributors to our quest for academic excellence. The applied methods have had some very promising results, particularly: i) several summer research internships since 2012 (for 1st year BSc students); ii) several research interviews available on Youtube since 2014 (for 2nd year BSc students); iii) 5 distinctions in the Intl. ACM SIGMOD programming competition since 2010 (for 3rd year BSc students); iv) 21 journal and conference proceedings since 2007 (for 4th year BSc students); and v) 3 international awards for innovative research systems since 2007 (again for 3rd and 4th year students). In the scope of my case study, I will detail the applied methodology for each different type of activitity and also summarize the lessons learnt. I claim that the provided achievements have helped our undergraduate students to reach their personal goals and our Data Management Systems Laboratory (DMSL) to meet its research agenda. Particularly, the above engagement allowed 6 students to obtain fellowships for graduate studies (e.g., Fulbright, Oxford), 5 students to advance to PhD programs, while all of them have moved on with MSc studies in the US, UK, Canada and Switzerland.
Wi-Fi (or WLAN) based indoor navigation applications mobiles rely on cloud-based services (s) that take of a user’s (u) localization task using structures called (RMs). It is imperative for u to have a stable WiFi in order to either continuously receive location from s or to download RMs a priori for offline navigation. networks however, suffer from intermittent connectivity due poor network planning that results in sparse deployment access points and effectively areas where Wi-Fi coverage be guaranteed. This inherently affects the localization and therefore the navigation experience of users. In paper, we propose an innovative framework for accurate fast indoor localization over an intermittently connected WiFi coined Prefetching Localization (PreLoc). In Preloc, prioritize the download of RM records based on knowledge from historic traces of other users inside the same Instead of downloading the complete RM from s to we propose a Probabilistic Group Selection (PGS) strategy, identifies RM records that have a higher probability of necessary to a user moving inside a target area. We have our framework using a real prototype developed in as well as realistic Wi-Fi traces we collected at the of Cyprus. Our experimental study reveals that PreLoc PGS and conventional fingerprint-based indoor positioning can yield accuracy that is as good as using the same with a complete RM even under scenarios of weak Wi-Fi coverage.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which can provide the location of a user upon request. Unfortunately, those technologies are both inaccurate and additionally raise important location privacy concerns, as the IIN can know where the user is at all times. this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will be succeeded by a summary of Rayzit which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.
This advanced seminar presents the fundamental mobile data management concepts behind the realization of innovative indoor information services that deal with all aspects of handling indoor data as a valuable resource, including data modeling, data acquisition, query processing, privacy and energy consumption. The goal is to provide an overview of the emerging field of indoor data management with a particular emphasis on mobile systems. We tackle the topic from a wide range of perspectives: fundamentals, definitions, current state, academic & industrial perspective, reality & visionary scenarios as well as future challenges. The seminar captures the big picture, such that interested researchers and practitioners can expand their study by following the references. Our presentation will be carried out through the lens of an experimental Indoor Information System we developed at the University of Cyprus, coined Anyplace, which has obtained three international awards and was ranked the second most accurate indoor localization technology by Microsoft Research at IEEE/ACM IPSN '14.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on cloud-based Indoor Positioning Services (IPS), which can provide the location of a user upon request but those are both inaccurate and additionally raise important location privacy concerns, as the IPS can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace, our in-house IPS that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IPS, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests both of which are shown to be resilient to a variety of privacy attacks.
The explosive number of smartphones with ever growing computing and sensing capabilities have brought a paradigm shift to many traditional domains of the computing field. In this talk, I will present three (3) ongoing testbeds and applications we are developing in-house in the space of smartphone and human-centric systems: In will start out with SmartLab (smartlab.cs.ucy.ac.cy), a Mobile Infrastructure-as-a-Service cloud we have developed and deployed at the University of Cyprus. In SmartLab, an intuitive web-based interface supplies a variety of complex mobile management utilities that provide fine-grained and low-level control over real smartphones, e.g., usage of networking, storage and sensors as well as automated mockup executions. We present our research experiences from using SmartLab in different research settings as well as our envisioned future scenarios for urban-scale deployment, federation issues and security studies. I will continue with Anyplace (anyplace.cs.ucy.ac.cy), our in-house Indoor Positioning Service that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. I will conclude with Rayzit (rayzit.com) which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on cloud-based Indoor Positioning Services (IPS), which can provide the location of a user upon request but those are both inaccurate and additionally raise important location privacy concerns, as the IPS can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace, our in-house IPS that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IPS, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will be succeeded by a summary of related research efforts, namely SmartLab, which is a novel in-house programming cluster of smartphones that we use in our experimental studies; and Rayzit which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.
The explosive number of smartphones with ever growing computing and sensing capabilities have brought a paradigm shift to many traditional domains of the computing field. Smartphone users nowadays gain access to unprecedented possibilities, knowledge and power due to a diverse landscape of applications. Developers on the other hand are challenged with a fragmented mobile landscape that is extremely dynamic to changes in both hardware and software. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. In this talk, we present the abstractions comprising SmartLab, a Mobile Infrastructure as a Service cloud we have developed and deployed at the University of Cyprus. In SmartLab, an intuitive web-based interface supplies a variety of complex mobile management utilities that provide fine-grained and low-level control over real smartphones, e.g., usage of networking, storage and sensors as well as automated mockup executions. We present our research experiences from using SmartLab in different research settings as well as our envisioned future scenarios for urban-scale deployment, federation issues and security studies. My talk will be succeeded by a summary of related mobile data management research efforts, namely Anyplace, which is our in-house indoor localization and navigation service; and Rayzit which is our award-winning location-based crowd messaging service.
Crowdsourcing refers to a distributed problem-solving model in which a crowd of undefined size is engaged to solve a complex problem through an open call. This novel problem-solving model found its way into numerous applications on the web for voting, fund-raising, micro-works and wisdom-of-the-crowd scenarios. Multi-sensing capabilities and multi-modal connectivity means of smartphones offer a great platform for extending and diversifying web-based crowdsourcing applications to a larger contributing crowd, making contribution easier and omnipresent. Unfortunately, numerous new challenges ranging from big data stream volume and velocity, to location privacy and energy consumption as well as device diversity issues among other, arise in this new context. In this talk, I will summarize mobile crowdsourcing challenges and applications related to Microblogging Urban Sensing and Indoor Information Systems.
Crowdsourcing refers to a distributed problem-solving model in which a crowd of undefined size is engaged to solve a complex problem through an open call. This novel problem-solving model found its way into numerous applications on the web for voting, fund-raising, micro-works and wisdom-of-the-crowd scenarios. Multi-sensing capabilities and multi-modal connectivity means of smartphones offer a great platform for extending and diversifying web-based crowdsourcing applications to a larger contributing crowd, making contribution easier and omnipresent. Unfortunately, numerous new challenges ranging from big data stream volume and velocity, to location privacy and energy consumption as well as device diversity issues among other, arise in this new context. this talk, I will start out by a discussion on how primitive crowdsourcing challenges emerge and evolve into urban data collection scenarios. I will present these new challenges through the lens of some in-house systems we’ve developed over the last few years, particularly: i) a smartphone programming cluster coined SmartLab, which provides means for general-purpose urban-scale sensing scenarios and smart cities; ii) an indoor information system coined Anyplace, which addresses big-data, privacy and energy consumption for building WiFi Radiomaps of indoor places; and iii) a location-based crowd messaging service coined Rayzit which addresses big data velocity with parallel algorithms and distributed nosql databases.
One of the popular research trends at present focuses on the use of sensor data generated/collected by consumer mobile devices to infer the ‘urban state’. There are a fairly large number of research initiatives that view such a citizen-centric distributed and mobile sensing platform as one of the most promising ways to gather data about various aspects of cities, such as environmental parameters & pollution levels, traffic congestion, popularity of events at various public spaces, etc. there are many skeptics who doubt that this “decentralized, bottom-up” approach can be an effective & commercially viable approach in the long run, due to open challenges in many aspects, such as resource limitations, privacy data quality and incentives. This panel will explore how academia and industry are tackling these challenges and debate on what types of applications are likely to be sustainable under this crowd-sourced paradigm.
Crowdsourcing refers to a distributed problem-solving model in which a crowd of undefined size is engaged to solve a complex problem through an open call. This novel problem-solving model found its way into numerous applications on the web for voting, fund-raising, micro-works and wisdom-of-the-crowd scenarios. On the other hand, the shift of desktop users to mobile platforms in the post-PC era, along with the unique multi-sensing capabilities of modern mobile devices are expected to eventually unfold the full potential of Crowdsourcing. This is true, as smartphones offer a great platform for extending and diversifying web-based crowdsourcing applications to a larger contributing crowd, making contribution easier and omnipresent. This advanced seminar presents the fundamental concepts behind crowdsourcing and its applications to mobile data management. In the first part of the seminar, we will overview the crowdsourcing landscape from a variety of perspectives, with a particular emphasis on the latest data management trends. In the second and more extended part of the seminar, we will focus on an in-depth coverage of emerging mobile crowdsourcing architectures and systems, through a multi-dimensional taxonomy that will address location, sensing, power, performance, big-data and privacy among others. Furthermore, we will overview a number of in-house crowdsourcing prototypes we have developed and deployed over the last few years. The seminar concludes with challenges opportunities and new directions in the field.
Big data refers to data sets whose size and structure strains the ability of commonly used relational DBMSs to capture, manage, and process the data within a tolerable elapsed time. Big data sizes commonly range from a few dozen terabytes to many petabytes in a single database and their underlying data model might be anything from structured (relational or tabular) to semi-structured (XML or JSON) or even unstructured (Web text and log files). Big data architectures are highly parallel and distributed in order to cope with the inherent I/O and CPU limitations. Such systems typically perform on mid-scale private clouds, offering higher privacy, to large-scale public clouds, both exposing operational and analytic functionality stand-alone or as-a-Service. This talk aims to overview the current big-data management landscape, the underlying technologies and their provenance, the latest NoSQL and NewSQL trends, possible applications of big-data management systems for online and offline processing of sensor data, text data, social data and medical data in enterprise environments. The talk will also overview ongoing big-data research and teaching activities at the University of Cyprus.
Smartphone devices have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of such devices connected to the Internet yield Smartphone Networks, which can be utilized for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and others. In this talk, I will present a collection of ongoing testbeds and applications we are developing in-house for this new era of ubiquituous smartphone computing. In particular, I will be presenting SmartLab, which an innovative open programming cloud of 40+ Android devices deployed at our Department over the last three years. SmartLab is the first open Smartphone IaaS (Infrastructure-as-a-Service) cloud that enables fine-grained, low-level interactions over static or moving smartphones via an intuitive web-based interface. Such a testbed provides means for general-purpose urban-scale sensing scenarios and personal gadget management. This talk will also briefly cover other smartphone sensing testbeds and applications we 've developed: a localization engine for fine-grained positioning without GPS, a trajectory comparinson framework with privacy gurranttees, a P2P smartphone searching framework and a neighborhood sensing framework.
Smartphones have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of smartphones connected to the Internet are nowadays proposed for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and many other domains, prompting undeniably the post-PC era. In this talk, I will present distributed architectures for querying and managing such sensor data by taking into account energy, data disclosure and networking aspects. I will particularly focus on SmartTrace, a powerful query processing framework for finding similar smartphone trajectories without disclosing the traces of participating users. I will also present SmartLab, a first-of-a-kind programmable cloud of 40+ smartphones deployed at our department enabling a new line of systems-oriented research on smartphones. Finally, I will also overview other related smartphone data management frameworks we 've developed for peer-to-peer search, crowdsourcing and indoor positioning, concluding with an outlook to our future research agenda.
Smartphone devices have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of such devices connected to the Internet yield Smartphone Networks, which can be utilized for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and others. The uptake of applications in this domain, is currently severely hampered by the fact that these devices have: i) a limited energy budget (i.e., smartphone devices still operate on batteries), ii) limited connectivity (i.e., not all regions offer unlimited Internet connectivity at the same cost); and iii) high privacy constraints (i.e., these devices might reveal the identity and habits of their custodians.) In this talk, I will present a collection of data management techniques that deal with Smartphone Networks. In particular, I will start out with SmartTrace, a powerful framework for finding similar trajectories in a smartphone network without disclosing the traces of the participating users. SmartTrace relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and data-disclosure reasons. SmartTrace then deploys an efficient top-K query-processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers quickly and efficiently. I will then introduce SmartOpt, a multi-objective query optimizer that enables efficient content searches in smartphone networks. I will also introduce Proximity, a spatial neighborhood computation framework for smartphone networks. My talk will be succeeded by the presentation of SmartNet, our in-house programming cloud for smartphone networks
Smartphone computational platforms equipped with multitude of sensors and capable of generating vast amounts of data (geo-location, audio, video, etc.) On the other hand, these devices operate on a strict energy budget, thus have a limited lifetime on a single charge. Consequently, we need to identify new energy-aware algorithms and techniques to provide innovative, feature-rich applications and services. In this white paper, we start out by providing recent trends in Smartphone technology and Smartphone networks. Our description is succeeded by an anatomy of the energy costs associated with data processing in a Smartphone Network. We conclude with prominent research directions in energy-aware data management for Smartphone networks.
Smartphone devices have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of such devices connected to the Internet yield Smartphone Networks, which can be utilized for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and others. The uptake of applications in this domain, is currently severely hampered by the fact that these devices have: i) a limited energy budget (i.e., smartphone devices still operate on batteries), ii) limited connectivity (i.e., not all regions offer unlimited Internet connectivity at the same cost); and iii) high privacy constraints (i.e., these devices might reveal the identity and habits of their custodians). In this talk I will present SmartTrace, a powerful framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, coined SmartTrace, quickly answers queries of the form: “Report the users that move more similar to Q, where Q is some query trace.” SmartTrace relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and data-disclosure reasons. SmartTrace then deploys an efficient top-K query-processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. We assess our propositions with realistic and real workloads from Microsoft Research Asia and other sources. Our study reveals that SmartTrace computes the desired results with 74% less energy consumption and 13% faster than its centralized and decentralized counterparts. My talk will be succeeded by a summary of related research efforts, namely SmartNet, an innovative programming cloud for smartphone networks; and SmartOpt, a multi-objective query optimizer that enables efficient content searches in smartphone networks.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, sensors are typically organized in structures coined Query Routing Trees (QRTs). Our study reveals that predominant data acquisition systems construct QRTs in ad-hoc manners leading to a significant waste of energy. In this talk I will present MicroPulse+, a framework for minimizing the consumption of energy during data collection in Sensor Networks. MicroPulse+ eliminates a variety of data transmission and data reception inefficiencies using a collection of in-network algorithms. In particular, MicroPulse+ introduces: i) the Workload-Aware Routing Tree (WART) algorithm, which is established on profiling recent data collection activity and on identifying the bottlenecks using an in-network execution of the critical path method; and ii) the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions. The talk will conclude with an outlook into current and future research work.
In this talk I will present a family of algorithms for Top-k ranking of query results in a distributed environment. A Top-K query focuses on the subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of Top-K query processing algorithms for centralized and middleware systems. I will then highlight the limitations of these algorithms and focus on three novel algorithms we developed designated for networked environments (i.e., Peer-to-Peer Networks, Wireless Sensor Networks and Smartphone Networks). I will also present evaluation studies of these algorithms on: i) a Wireless Sensor Network testbed of 54 sensor devices; ii) a Peer-to-Peer testbed of 1000 peers deployed on 75 linux workstations; and iii) A smartphone network deployment on Android-based smartphone devices. The talk will conclude with an overview of related research problems that I am currently working on and an outlook to future work.
Panel Chair: Anastasia Ailamaki. Other Panelists: Marios Dikaiakos, Evangelia Pitoura, Peter Triantafillou and Akrivi Vlachou
In this talk I present the fundamental concepts behind distributed Top-K query processing algorithms. A Top-K query focuses on the subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of state-of-the-art Top-K query processing algorithms for centralized and middleware systems. I will then highlight the limitations of these algorithms and focus on two novel algorithms we developed designated for networked environments (i.e., Wireless Sensor Networks, Peer-to-Peer Networks, Vehicular Networks, etc.) I will also present evaluation studies conducted on: i) a Peer-to-Peer testbed of 1000 peers deployed on 75 workstations; ii) a Wireless Sensor Network testbed of 54 sensor devices and iii) A Smartphone Network, deployed on a number of Android-based smartphone devices. The talk will conclude with an overview of related research problems that I am currently working on and an outlook to future applications of the presented ideas.
In this presentation I will present a powerful and distributed spatio-temporal query processing framework, coined HUB-K. Our framework can be utilized to promptly answer queries of the form: ' 'Report the objects (i.e., trajectories) that follow a similar spatio-temporal motion to Q, where Q is some query trajectory. ' ' HUB-k, relies on an in-situ data storage model, where spatio-temporal data remains on the smartphone that generated the given data, as well a state-of-the-art top-k query processing algorithms, which exploit distributed trajectory similarity measures in order to identify the correct answers promptly. We present preliminary design choices, an outline of our preliminary implementation and an outlook to future challenges.
The widespread deployment of mobile phones along with the massive production of sensors for every aspect of modern life provides evidence that Computer Science research and education will evolve dramatically over the next few years. The boundaries of Mobile Devices and Sensor Devices are nowadays blurring as the former devices are already equipped with a multitude of sensing capabilities, including GPS (which enables the derivation of geospatial coordinates), accelerometers (which enable the derivation of orientation, vibration and shock) and an exciting set of other sensors (e.g., proximity sensors, ambient light sensors, while more traditional sensors such as temperature, acoustic, magnetometers and others will be integrated in these devices very soon). That creates the notion of Mobile Sensor Devices that will become even more ubiquitous than their predecessor 'smart-phone ' devices. In this talk, I will provide an overview and definitions of Mobile-Sensor-Network (MSN) related platforms and applications. In particular, I will show how applications in environmental monitoring, body sensor networks, vehicular sensor networks and intelligent transportation systems have brought a dramatic shift on how spatio-temporal data is nowadays generated. I will then outline some semantic challenges that arise in this context including: vastness, uncertainty, data integration, query processing and privacy. I will also address some more general challenges that currently hinder the evolution and uptake of semantic MSNs.
In this talk I will present the fundamental concepts of distributed Top-K query processing algorithms. A Top-K query focuses on a subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of state-of-the-art Top-K query processing algorithms for centralized DBMS systems. I will then highlight the limitations of these algorithms and focus on the Threshold Join Algorithm (TJA), our distributed top-k query processing algorithm designated for distributed computing networks (i.e., Wireless Sensor Networks, Peer-to-Peer Networks, Vehicular Networks, etc.) I will finally present an evaluation study conducted with our middleware system deployed over a network of 1000 peers on 75 workstations.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. Research in this area has to this day primarily focused on the trade-off between local computation and communication in order to minimize the transfer of data over the fundamentally expensive wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy in Wireless Sensor Networks as the communication channel is only accessed for answering on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each sensor has to be complemented by efficient access methods that will speed up the execution of queries when required. In this talk I will present MicroHash, an external memory index structure that is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. MicroHash exploits the asymmetric read/write characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of energy and storage media lifetime constraints.
In this talk I will present the fundamental concepts of distributed Top-K query processing algorithms. A Top-K query focuses on a subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of state-of-the-art Top-K query processing algorithms for centralized DBMS systems. I will then highlight the limitations of these algorithms and focus on the Threshold Join Algorithm (TJA), our distributed top-k query processing algorithm designated for distributed computing networks (i.e., Wireless Sensor Networks, Peer-to-Peer Networks, Vehicular Networks, etc.) I will finally present an evaluation study conducted with our middleware system deployed over a network of 1000 peers on 75 workstations.
Wireless Sensor Networks create an innovative technology that enables users to monitor and study the physical world at an extremely high resolution. Query processing in such ad-hoc environments is a challenging task due to the complexities imposed by the inherent energy and communication constraints. To this end, the research community has proposed to take into account user-defined parameters in order to derive the K most relevant (or Top-K) answers quickly and efficiently. A Top-K query returns the subset of most relevant answers, in place of all answers, for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the recall and the precision of the answer set, such that the user is not overwhelmed with irrelevant results. This tutorial presents the fundamental concepts behind distributed Top-K query processing and the adaptations of these algorithms to distributed and wireless sensor networks. It additionally provides a gentle overview of rudimentary and advanced techniques covering a significant body of research in this domain. The tutorial will start out with an overview of the most influential centralized and middleware Top-K query processing algorithms and then proceed with an elaborate description of distributed Top-K ranking algorithms for One-time Top-K Queries, Continuous Top-K Queries and Approximate Top-K Queries. Finally, it will provide an outlook to compelling future applications that can be constructed on the foundation of these algorithms. Although the tutorial is specifically geared towards Wireless Sensor Networks, many of the presented ideas find extensions in other mobile environments such as Adhoc Networks, Vehicular Networks and the Mobile Web.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. Research in this area has to this day primarily focused on the trade-off between local computation and communication in order to minimize the transfer of data over the fundamentally expensive wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy in Wireless Sensor Networks as the communication channel is only accessed for answering on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each sensor has to be complemented by efficient access methods that will speed up the execution of queries when required. In this talk I will present MicroHash, an external memory index structure that is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. MicroHash exploits the asymmetric read/write characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of energy and storage media lifetime constraints.
The emerging Peer-to-Peer (P2P) model has become a very powerful and attractive paradigm for developing Internet-scale services for sharing resources, including files and documents. The distributed nature of these systems, where nodes are typically located across different networks and domains, inherently hinders the efficient retrieval of information. In this talk I will present techniques to perform content-based search over data repositories that are geographically scattered over peers of different networks. Data repositories in this context contain documents of text, audio, video or other semi-structured data and the task is to locate a certain set of keywords or multimedia features. We present the components of the pFusion architecture, an open source system that builds on work in unstructured P2P systems and topologically-aware overlay construction techniques. Our empirical results using datasets from AKAMAI, NLANR and TREC, show that the architecture we propose is both efficient and practical. In this talk I will also overview other related research activities in Grid, P2P and Sensor systems that we are currently involved in.
Emerging applications in Sensor and Peer-to-Peer networks make the concept of data integration without centralization nowadays more meaningful than ever. In these environments, data is generated continuously and potentially automatically across geographically diverse locations. Organizing data in centralized repositories is becoming prohibitively expensive and in many occasions impractical. Storing data in-situ however, complicates query processing because data relations are fragmented over a number of remote sites. Furthermore, accessing these fragmented relations is only feasible by traversing a network of other nodes. This makes the execution of a query an even more complex task. We claim that in many occasions it might more beneficial to find the K highest ranked (or Top-K) answers, for some user defined parameter K, if this can minimize the query execution cost. In this talk, I will present techniques to efficiently answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric, such as the utilization of the communication medium, which is associated with the retrieval of the desired answer set. I will provide an overview of state-of-the-art algorithms that solve the Top-K problem in a centralized setting and show why these are not applicable to the distributed case. I will then focus on the Threshold Join Algorithm (TJA), which is a novel solution for executing Top-K queries in a distributed environment. I will also present results from our performance study with a real middleware testbed deployed over a network of 75 workstations.
In this overview talk, I will start out by explaining in an understandable language what Cybersecurity is and its role in thwarting Cybercrime. I will start out with foundations of Cybersecurity using scenaria and examples, explaining the terminology, the desiderata and methods used by engaged parties, European and national legal frameworks, the workflows adopted by users and organizations. I will then focus on an array of cybersecurity challenges, related to Quantum Computing Threats, Artificial Intelligence and Large Language Model Attacks, Internet of Things (IoT) Vulnerabilities and Cryptocurrencies, providing a glimpse of what lies ahead in the future.
Internet-of-Things (IoT) describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet. These objects are projected to outpace the number of humans on the Internet, namely, by 2030 there will be 8.6B humans according to UN DESA and over 500B IoT devices according to Cisco. Huawei predicts that this will create yottabytes (YB) of machine-generated spatio-temporal data every year requiring a complete rethink on how we collect and store data, how we process it in a timely manner and how we generate value and utility out of it. In this talk, Prof. Zeinalipour will present three pillars of IoT data management research in the scope of systems related to indoor localization, telecommunication big data and smart spaces. He will start out with the presentation of data-driven localization algorithms for mobile devices in systems with: (i) no telecommunication infrastructure; (ii) disconnected handheld operation; and (iii) inherent privacy constraints of users. I will proceed with the presentation of data decaying operators for telco big data systems which abstract IoT influx data into compact machine learning models that can be stored and queried when necessary. I will conclude with scheduling operators of IoT devices inside smart spaces to reduce the environmental impact of human activity. Prof. Zeinalipour will then conclude with an outlook to his current and future research agenda.
Internet-of-Things (IoT) describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet. These objects are projected to outpace the number of humans on the Internet, namely, by 2030 there will be 8.6B humans according to UN DESA and over 500B IoT devices according to Cisco. Huawei predicts that this will create yottabytes (YB) of machine-generated spatio-temporal data every year requiring a complete rethink on how we collect and store data, how we process it in a timely manner and how we generate value and utility out of it. In this talk, Prof. Zeinalipour will present three pillars of IoT data management research in the scope of systems related to indoor localization, telecommunication big data and smart spaces. He will start out with the presentation of data-driven localization algorithms for mobile devices in systems with: (i) no telecommunication infrastructure; (ii) disconnected handheld operation; and (iii) inherent privacy constraints of users. I will proceed with the presentation of data decaying operators for telco big data systems which abstract IoT influx data into compact machine learning models that can be stored and queried when necessary. I will conclude with scheduling operators of IoT devices inside smart spaces to reduce the environmental impact of human activity. Prof. Zeinalipour will then conclude with an outlook to his current and future research agenda.
Πραγματοποιήθηκε τη Δευτέρα, 27 Νοεμβρίου 2023 στο Πανεπιστήμιο Κύπρου η ημερίδα με θέμα «Τεχνητή Νοημοσύνη: Εξελίξεις και Προοπτικές στο Ακαδημαϊκό Περιβάλλον». Η ημερίδα διοργανώθηκε από το Γραφείο Αντιπρυτάνεως Ακαδημαϊκών Υποθέσεων, στο πλαίσιο της ευαισθητοποίησης της ακαδημαϊκής κοινότητας για την χρήση της τεχνητής νοημοσύνης στη διδασκαλία και την έρευνα και συγκέντρωσε επιστήμονες και ακαδημαϊκούς, φοιτητές και φοιτήτριες, καθώς επίσης και αξιωματούχους Υπουργείων και Πανεπιστημίων της Κύπρου. πρόγραμμα της ημερίδας ξεκίνησε με χαιρετισμό από την Αντιπρύτανη Ακαδημαϊκών Υποθέσεων του Πανεπιστημίου Κύπρου, Καθηγήτρια Τατιάνα Ελένη Συνοδινού, η οποία επεσήμανε ότι η δεοντολογική αξιοποίηση της τεχνητής νοημοσύνης αποτελεί βασικό διακύβευμα της εποχής μας, στο πλαίσιο της οποίας τα πανεπιστήμια καλούνται να παρέχουν τις απαραίτητες δεξιότητες και γνώσεις και να διαμορφώσουν νέα μοντέλα και πρακτικές έρευνας, διδασκαλίας και μάθησης, που θα λειτουργήσουν ως πυλώνες εξέλιξης, κατά και μετά από τις σπουδές, για το πανεπιστήμιο και την κοινωνία. Σε σχέση με τις ενέργειες του Πανεπιστημίου Κύπρου για αξιοποίηση της τεχνητής νοημοσύνης, η Αντιπρύτανης αναφέρθηκε στη σύσταση Ad-hoc Επιτροπής, αποτελούμενη από εμπειρογνώμονες, μέλη του Ακαδημαϊκού Προσωπικού, η οποία στοχεύει στην προετοιμασία κατευθυντήριων γραμμών και πολιτικών για την δεοντολογική και ηθική χρήση και εφαρμογή της τεχνητής νοημοσύνης, διασφαλίζοντας παράλληλα το υψηλό επίπεδο της έρευνας και της διδασκαλίας, με τις πρώτες συστάσεις να έχουν ήδη εφαρμοστεί στο Πανεπιστήμιο Κύπρου από το Χειμερινό Εξάμηνο 2023-2024. κεντρικός ομιλητής της ημερίδας, Καθηγητής Γιάννης Ιωαννίδης του Εθνικού Καποδιστριακού Πανεπιστημίου Αθηνών και Πρόεδρος της Association for Computing Machinery (ACM), παρουσίασε τις τεχνολογικές και κοινωνικές προκλήσεις και ευκαιρίες που επιφέρει η τεχνητή νοημοσύνη στην ανθρωπότητα με ιδιαίτερη μνεία στην αξιοποίηση της τεχνητής νοημοσύνης προς ενίσχυση της ανοιχτής επιστήμης. Συγκεκριμένα, ο Καθηγητής Ιωαννίδης επεσήμανε ότι τα εργαλεία τεχνητής νοημοσύνης αποτελούν συμπλήρωμα της επιστημονικής διαδικασίας και τόνισε την ανάγκη για την εφαρμογή και συνεχή αναθεώρηση επαγγελματικών και ηθικών προτύπων, τα οποία θα εκσυγχρονίζονται παράλληλα με τη ραγδαία ανάπτυξη της τεχνολογίας, των δυνατοτήτων και των εφαρμογών της τεχνητής νοημοσύνης. πρόγραμμα της ημερίδας συμπεριλάμβανε επίσης παρουσιάσεις και συζήτηση με ακαδημαϊκά στελέχη του Πανεπιστημίου Κύπρου με ειδίκευση σε θέματα τεχνητής νοημοσύνης. Συγκεκριμένα, ο Καθηγητής του Τμήματος Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών και IEEE Frank Rosenblatt Technical Field Award recipient Μάριος Πολυκάρπου, στην παρουσίαση του με θέμα «Τεχνητή Νοημοσύνη: Συνεργασία και Ασφάλεια» εστίασε στην ανάγκη για τη σύγκλιση απόψεων μεταξύ των κοινωνικών, κρατικών, επιστημονικών και ιδιωτικών εταίρων για την χρήση της τεχνητής νοημοσύνης, αφού από αυτή επηρεάζονται η εκπαίδευση, η επιστημονική έρευνα, η οικονομία, η ανεργία, η ασφάλεια και η ισότητα. Ο Καθηγητής του Τμήματος Πληροφορικής Δημήτρης Ζεϊναλιπούρ, παρουσίασε το θέμα «Ευκαιρίες και Προκλήσεις από τη Χρήση LLMs στη Διδασκαλία Μαθημάτων Πληροφορικής» αναδεικνύοντας την πρακτική εφαρμογή της τεχνητής νοημοσύνης, μέσω απτών παραδειγμάτων τα οποία δύναται να προσαρμοστούν και στη διδασκαλία μαθημάτων πέραν της Πληροφορικής. Ο αφυπηρετήσας Καθηγητής του Τμήματος Πληροφορικής Αντώνης Κάκας, στην παρουσίαση του με τίτλο «Η μόλυνση της τεχνητής νοημοσύνης» έθιξε, μεταξύ άλλων, τους κινδύνους που διέπουν την ανεξέλεγκτη ανάπτυξη της τεχνητής νοημοσύνης. Η Καθηγήτρια του Τμήματος Πληροφορικής, Ελπίδα Κεραυνού-Παπαηλιού, στην ομιλία της με θέμα «Σύγχρονα προγράμματα εξειδίκευσης στην Τεχνητή Νοημοσύνη: το έργο MAI4CAREU» παρουσίασε την δράση του Πανεπιστημίου Κύπρου για τον συντονισμό του έργου MAI4CAREU (Master Programmes in AI for Careers in Europe) το οποίο στοχεύει στην ανάπτυξη και προσφορά σύγχρονων προγραμμάτων εξειδίκευσης μεταπτυχιακού επιπέδου στην τεχνητή νοημοσύνη σε κοινοπραξία με τα πανεπιστήμια της Bologna και Ruse. Τέλος, ο Καθηγητής του Τμήματος Μηχανικών Μηχανολόγων και Κατασκευαστικής Σταύρος Κάσινος στην παρουσίαση του με θέμα «Προσφέροντας μέσα από την διδασκαλία και την έρευνα τα εφόδια για να μπορούν οι απόφοιτοι μας να εφαρμόζουν και να διαχειρίζονται την Τεχνητή Νοημοσύνη» ανέδειξε μέσω της δικής του εμπειρίας πως εργαλεία τεχνητής νοημοσύνης, όπως το ChatGPT, παρέχουν τη δυνατότητα ανάλυσης τεράστιων όγκων γνώσεων και δεδομένων για την γρήγορη, αποτελεσματική και αυτόματη εκτέλεση διεργασιών. Παρουσίασε, επίσης, το chatbot που δημιούργησε για προπτυχιακό μάθημα που διδάσκει στο Πανεπιστήμιο Κύπρου ως παράδειγμα της συμβολής της Τεχνητής Νοημοσύνης στην εξατομικευμένη εκπαίδευση.
In this paper, we present an innovative framework objective is to allow drivers to recharge their Electric (EVs) from the most environmentally friendly chargers an intelligent hoarding approach. These chargers maximize (e.g., solar) self-consumption, minimizing this way CO2 and also the need for expensive stationary batteries the electricity grid to store renewable energy that cannot used otherwise. We model our problem as a Continuous kNearest Neighbor query, where the distance function is computed Estimated Components (ECs), i.e., a query we term CkNNEC. An EC defines a function that can have a fuzzy value on some estimates. Specific ECs used in this work are: the (available clean) power at the charger, which depends the estimated weather; (ii) the charger availability, which on the estimated busy timetables that show when the is crowded; and (iii) the derouting cost, which is the to reach the charger depending on estimated traffic. We the EcoCharge framework that combines these multiple objectives into an optimization task providing ranking means through an intuitive mobile GIS Particularly, our core algorithm uses lower and values derived from the ECs to recommend the top ranked chargers and present them through an intuitive map user to users. Our experimental evaluation with extensive and real traces from Germany, China, and USA along EV charger data from Plugshare shows that EcoCharge the objective functions in an efficient manner, allowing continuous recomputation on the edge devices (e.g.
Energy efficiency has become a primary optimization objective due to the global energy crisis and high levels of emissions. Climate and energy targets have been leading a growing utilization of solar photovoltaic power generation in residential buildings. As the number of IoT devices increases, their automation through an intelligent energy management system can provide energy and peak savings. The planning optimization of devices can be challenging due to the unsophisticated user-defined preference rules. Existing solutions face convergence difficulties due the management of multiple IoT devices tackling multiobjective problems. In this paper, we propose an innovative data system, coined GreenCap, which utilizes a Green evolutionary algorithm for load shifting of IoT-enabled considering the integration of renewable energy sources, constraints, peak-demand times, and dynamic pricing. have implemented a complete prototype system available Raspberry Pi and linked with openHAB framework. Our evaluation with extensive real traces shows that the prototype system efficiently generates a sustainable obtaining high levels of user comfort 92-99% along with of self-consumption while reducing ≈35% of the imported energy from the grid and ≈40% of CO2 emissions.
In this paper we present the design and utility of a Smart Alert (SMAS) for quick first response and effective fighting of in their initial stages on roll-on / roll-off (ro-ro) vessels. Given localization within the steel structures of a vessel is currently open problem, we develop a ground-breaking infrastructurefree (i.e., “zero” infrastructure) localization architecture to localize ordinary smartphones in vessel indoor spaces that lack any whatsoever (e.g., Wi-Fi, BLE, UWB, RFID, LED). Particularly, we developed a smartphone-based Computer Vision (CV) technology upon which an innovative nearest neighbor communication channel for spatio-textual alerting between first responders is constructed. We also develop information channels of multimedia content (e.g., heat scans) but also an integrated search and navigation tool for stationary mobile assets of a vessel and the fire control operation. We developed a complete functional system of SMAS using a edge architecture that deploys sharded sqlite microdatabases. We will present SMAS in two modes: (i) Online Mode, attendees will be able to carry out simulated emergency and (ii) Offline Mode where attendees will be able to observe emergency scenarios recorded on video traces.
In this paper we present the design and utility of a Smart Alert (SMAS) for quick first response and effective fighting of in their initial stages on roll-on / roll-off (ro-ro) vessels. Given localization within the steel structures of a vessel is currently open problem, we develop a ground-breaking infrastructurefree (i.e., “zero” infrastructure) localization architecture to localize ordinary smartphones in vessel indoor spaces that lack any whatsoever (e.g., Wi-Fi, BLE, UWB, RFID, LED). Particularly, we developed a smartphone-based Computer Vision (CV) technology upon which an innovative nearest neighbor communication channel for spatio-textual alerting between first responders is constructed. We also develop information channels of multimedia content (e.g., heat scans) but also an integrated search and navigation tool for stationary mobile assets of a vessel and the fire control operation. We developed a complete functional system of SMAS using a edge architecture that deploys sharded sqlite microdatabases. We will present SMAS in two modes: (i) Online Mode, attendees will be able to carry out simulated emergency and (ii) Offline Mode where attendees will be able to observe emergency scenarios recorded on video traces.
In this demonstration paper, we present an innovative indoor localization architecture, coined Anyplace Computer (AnyplaceCV), which provides an infrastructure-free (or infrastructure) method to localize in indoor spaces that any infrastructure whatsoever (e.g., Wi-Fi, BLE, UWB, Sonar, LED). We have developed a complete functional of AnyplaceCV around the Anyplace open-source architecture we developed over the years and will make our open-source. We will present AnyplaceCV in two (i) Online Mode, where attendees will be able to collect analyze real CV fingerprints at the conference venue; and Offline Mode where attendees will be able to interact with collected measurements through a smartphone and PC.
Internet of Things (IoT) devices have penetrated massively into smart environments (e.g., smart-homes, smartcars or more generally smart-anything). Besides data collection, many IoT devices also enable the execution of Rule Automation Workflows (RAW), which span from simple predicate statements to procedural workflows capturing a smart actuation pipeline. RAW aim to meet the convenience (comfort) level of users under specific conditions (e.g., raise room temperature to 22C if cold), but unfortunately cannot express long-term objectives of users (e.g., consume less than 400 kWh in December). In this paper, we present an innovative system, coined IoT Meta-Control Firewall (IMCF), which internally deploys an AI-inspired Energy-Planner (EP) algorithm that exploits domain-specific operators to balance the trade-off between convenience and energy consumption in satisfying the RAW pipelines of users. IMCF filters the RAW pipelines in a way that these do not conflict with the long-term objectives of users (like a network firewall). Our experimental evaluation with extensive real traces from an apartment, a house, and campus dorms shows that IMCF achieves very high levels of user convenience while remaining within the target energy consumption budgets expressed by users.
In this demonstration paper, we present an innovative IoT MetaControl Firewall (IMCF), which allows users to schedule their IoT devices in smart buildings (e.g., heating, cooling, lights) in order to reach some long-term energy consumption objective (e.g., consume less than 400 kWh in December) while, at the same time, retaining high levels of user convenience (comfort). IMCF internally deploys an AI-inspired Energy-Planner (EP) algorithm that exploits domain-specific operators to balance the trade-off between convenience and energy consumption. Our framework then filters the rules of users in a way that these do not conflict with the long-term objectives (i.e., like a network firewall). We demonstrate IMCF using a prototype system we have developed in the Laravel PHP web framework using the open Home Automation Bus (OpenHAB), the Linux crontab daemon and Anyplace for building modeling. In our demonstration scenario, attendees will be able to observe the execution and benefits of IMCF on a graphical dashboard using pre-configured or custom-made Meta-Rule-Table profiles
The Internet of Things (IoT) revolution has massively introduced sensor-rich tracking devices to an ever growing of smart spaces (e.g., factories, hospitals, and ships). problem that remains unsolved over the years is the problem for IoT, given that Satellite-based solutions inaccurate in indoor spaces where human activity takes place of the time. In this paper, we introduce a novel opensource architecture for IoT localization, coined Anyplace 4.0 IoT which exploits signal fingerprinting to organize under same roof a wide range of different localization technologies Wi-Fi, BLE, Cellular, UWB, Computer Vision). We present technical requirements of A4IoT inspired by the Alstom SA factory, operating worldwide in rail transport markets. comprises a crowdsourcing architecture where deployers collect and organize fingerprint signals inside smart spaces a designated localization service running on the Edge (from to Datacenter). The service incorporates timeseries for tracking targets and deployers can provide accurate localization accuracy (≈ 2 m) on a variety of platforms Android, Linux, Mac, Windows Robot OS) but also A4IoT through Web 2.0 endpoints to their software
Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates that state- of-the-art methods.
Large document collections can be hard to explore if the user presents her information need in a limited set of keywords. Ambiguous intents arising out of these short queries often result in long-winded query sessions and many query reformulations. To alleviate this problem, in this work, we propose the novel concept of semantic aspects (e.g., ⟨michael- phelps athen
There is a wealth of schema-free tables on the Web, valuable information about quantities on sales and costs, footprint of cars, health data and more. Table can only be properly interpreted in conjunction with the context that surrounds the tables. This paper introduces quantity alignment problem: bidirectional linking between mentions of quantities and the corresponding table cells, order to support advanced content summarization and faster between explanations in text and details in tables. We the BriQ system for computing such alignments. BriQ designed to cope with the specific challenges of approximate aggregated quantities and calculated quantities in text are common but cannot be directly matched in table cells. judiciously combine feature-based classification with joint by random walks over candidate alignment graphs. with a large collection of tables from the Common Crawl project demonstrate the viability of our methods.
In this demonstration paper, we present an integrated signal management studio, coined Fingerprint Studio (FMS), which provides a spatio-temporal to: (i) manage the collection of location-dependent readings (i.e., fingerprints) in indoor environments; (ii) the localization accuracy based on the collected fingerprints; (iii) assess Wi-Fi coverage and data rates. The will present the components comprising FMS, CSM (Crowd Signal Map), ACCES (Accuracy Estimation) WS (Wi-Fi Surveying), through a compelling map-based analytic interface implemented on top of our open-source navigation service, coined Anyplace. We will present FMS two modes: (i) Online Mode, where attendees will be able to and analyze real fingerprints at the conference venue; and Offline Mode, where attendees will be able to interact with of University campus in Cyprus a Hotel in the and an Expo in S. Korea.
In this demonstration paper, we present the TBDDP which relies on existing Machine Learning (ML) to abstract Telco Big Data (TBD) into compact models can be stored and queried when necessary. Our proposed operator has the following two conceptual phases: (i) an offline phase, it utilizes a LSTM-based hierarchical ML to learn a tree of models (coined TBD-DP tree) over and space; (ii) in an online phase, it uses the TBD-DP tree recover data within a certain accuracy. Our framework also visual and declarative interfaces for a variety of telcospecific exploration tasks. We demonstrate the efficiency of proposed operator using SPATE, which is a novel TBD visual architecture we have developed. Our demo will enable to interactively explore synthetic antenna signal traces, will provide, in both visual and SQL mode. In both cases the of the propositions will be quantitatively conveyed the attendees through dedicated dashboards
In this paper, we present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.
Recent studies in urban navigation have revealed new demands diversity, safety, happiness, serendipity) for the navigation that are critical to providing useful recommendations to This exposes the need to design next-generation navigation that accommodate these newly emerging aspects. In paper, we present a prototype system, namely, EPUI (an Experimental of Urban Informatics), which provides a testbed exploring and evaluating venues and route recommendation that balance between different objectives (i.e., demands) the newly discovered ones. In addition, EPUI incorporates modularized design enabling researchers to upload their algorithms and compare them to well-known algorithms using performance metrics. Its user interface makes it easily by both end-user and experienced researchers.
A telecommunication company (telco) is traditionally only perceived the entity that provides telecommunication services, such as and data communication access to users. However, the backbone infrastructure of such entities spanning densely urban and widely rural areas, provides nowadays a unique to collect immense amounts of mobility data that can valuable insights for road tra c management and avoidance. this paper we outline the components of the Tra c-TBD Telco Big Data) architecture, which aims to become an innovative tra c analytic and prediction system with the following i) provide micro-level tra c modeling and prediction goes beyond the current state provided by Internet-based enterprises utilizing crowdsourcing; ii) retain the location boundaries of users inside their mobile network operator, avoid the risks of exposing location data to third-party mobile and iii) be available with minimal costs and using infrastructure (i.e., cell towers and TBD data streams are available inside a telco). Road tra c understanding, management analytics can minimize the number of road accidents, fuel and energy consumption, avoid unexpected delays, to a macroscopic spatio-temporal understanding of traf- in cities but also to “smart” societies through applications in city public transportation logistics and eet management for startups and governmental bodies.
With the proliferation of mobile computing and the ability to collect detailed data for the urban environment a number of systems that aim at providing Points of Interest (POIs) and tour recommendations have appeared. The overwhelming majority of these systems aims at providing an optimal recom- mendation, where optimality refers to objectives of minimizing the distance to be covered or maximizing the quality of the POIs recommended. A major problem is that by focusing on the optimization of these objectives, there remains little room to the user for serendipity. Urban and social scientists have identified serendipity, i.e., the ability to come across unexpected places, as a feature that makes a city livable. In this work, we introduce a prototype of an experimental platform for evaluating venue recommendation algorithms by providing informative tour recommendations based on the suggested venues. Our prototype system integrates the notion of serendipity in urban navigation at both the venue as well as the route recommendation level without compromising the quality and diversity of the recommended POIs. In addition our system allows the user to upload their own algorithms and explore their performance as compared to many well-known algorithms.
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel frame- work for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds demonstrates that the navigability score closely matches the accuracy variations users experience.
In this demonstration we present ACCES, a novel framework that enables quality assessment of arbitrary fin- gerprint maps and offline accuracy estimation for the task of fingerprint-based indoor localization. Our framework considers collected fingerprints disregarding the physical origin of the data. First, it applies a widely used statistical instrument, namely Gaussian Process Regression (GPR), for interpolation of the fingerprints. Then, to estimate the best possibly achievable localization accuracy at any location, it utilizes the Cramer-Rao Lower Bound (CRLB) with interpolated data as an input. Our demonstration entails a standalone version of the popular and open-source Anyplace Internet-based indoor navigation service in which the software modules of ACCES are integrated. At the conference, we will present the utility of our method in two modes: (i) Collection Mode, where attendees will be able to use our service directly to collect signal measurements over the venue using an Android smartphone; and (ii) Reflection Mode where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap.
In this demonstration paper, we present SPATE, innovative telco big data exploration framework whose are two-fold: (i) minimizing the storage space needed incrementally retain data over time; and (ii) minimizing the time for spatiotemporal data exploration queries over data. Our framework deploys lossless data compression ingest streams of telco big data in the most compact manner full resolution for data exploration tasks. We augment storage structures with decaying principles that lead to progressive loss of detail as information gets older. Our also includes visual and declarative interfaces for a of telco-specific data exploration tasks. We demonstrate in two modes: (i) Visual Mode, where attendees will able to interactively explore synthetic telco traces we will and (ii) SQL Mode where attendees can submit custom queries based on a provided schema.
In the realm of smart cities, telecommunication companies (telcos) are expected to play a protagonistic role as these can capture a variety of natural phenomena on an ongoing basis, e.g., traffic in a city, mobility patterns for emergency response or city planning. The key challenges for telcos in this era is to ingest in the most compact manner huge amounts of network logs, perform big data exploration and analytics on the generated data within a tolerable elapsed time. This paper introduces SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time; and (ii) minimizing the response time for spatiotemporal data exploration queries over recent data. The storage layer of our framework uses lossless data compression to ingest recent streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. The indexing layer of our system then takes care of the progressive loss of detail in information, coined decaying, as data ages with time. The exploration layer provides visual means to explore the generated spatio-temporal information space. We measure the efficiency of the proposed framework using a 5GB anonymized real telco network trace and a variety of telco-specific tasks, such as OLAP and OLTP querying, privacy-aware data sharing, multivariate statistics clustering and regression. We show that out framework can achieve comparable response times to the state-of-the-art using an order of magnitude less storage space.
Wi-Fi (or WLAN) based indoor navigation applications for mobiles rely on cloud-based services (s) that take care of a user’s (u) localization task using structures called RadioMaps (RMs). It is imperative for u to have a stable WiFi connection in order to either continuously receive location updates from s or to download RMs a priori for offline navigation. Wi-Fi networks however, suffer from intermittent connectivity dueto poor network planning that results in sparse deployment of access points and effectively areas where Wi-Fi coverage cannot be guaranteed. This inherently affects the localization accuracy and therefore the navigation experience of users. In this paper, we propose an innovative framework for accurate and fast indoor localization over an intermittently connected WiFi network, coined Prefetching Localization (PreLoc). In Preloc, we prioritize the download of RM records based on knowledge acquired from historic traces of other users inside the same building. Instead of downloading the complete RM from s to u, we propose a Probabilistic Group Selection (PGS) strategy, which identifies RM records that have a higher probability of being necessary to a user moving inside a target area. We have evaluated our framework using a real prototype developed in Android, as well as realistic Wi-Fi traces we collected at the University of Cyprus. Our experimental study reveals that PreLoc using PGS and conventional fingerprint-based indoor positioning algorithms can yield accuracy that is as good as using the same algorithms with a complete RM even under scenarios of weak Wi-Fi coverage.
In this paper we present a comprehensive architecture to carry out experimental repeatability studies on clusters of smartphones. Our architecture is founded on SmartLab (https://smartlab.cs.ucy.ac.cy/), our in-house architecture for managing real and virtual smartphones via an intuitive Web user interface. Our presented architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner as well as executing mockup experiments by “feeding” the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our testbed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.
People do most of their activities, business, commerce, entertainment and socializing indoors. As all of these are increasingly aided by online services and indoor spaces are becoming bigger and more complex, there is a growing need for cost-effective indoor localization, mapping, navigation and information services. In this paper, we present a complete Indoor Information Service, coined Anyplace (https://anyplace.cs.ucy.ac.cy/), which has an open, modular, extensible and scalable architecture, making it ideal for a wide range of applications. Our service features three highly desirable properties, namely crowdsourcing, scalability and accuracy. Anyplace implements a set of crowdsourcing-supportive mechanisms to handle the enormous amount of crowd-sensed data, filter incorrect user contributions and exploit Wi-Fi data from heterogeneous mobile devices. Moreover, it uses a big-data architecture for efficient storage and retrieval of localization and mapping data. Finally, our service relies on the abundance of sensory data on smartphones (e.g. Wi-Fi signal strength and inertial measurements) to deliver reliable indoor geolocation information that received several international awards.
The smartphone revolution has introduced a new era of social networks where users communicate over anonymous messaging platforms to exchange opinions, ideas and even carry out commerce. These platforms enable individuals to establish social interactions between strangers based on a common interest or attribute. In this paper we present Rayzit, a novel anonymous crowd messaging architecture, which utilizes the location of each user to connect them instantly to their k Nearest Neighbors (kNN) as they move in space. Contrary to the very large body of location-based social networks that suffer from bootstrapping issues our architecture enables a user to always interact with the geographically closest possible users around. We establish this communication using a fast computation of an All kNN query that generates a dynamic global social graph every few seconds. We present motivating application scenarios and the detailed backend architecture that allows Rayzit to scale. We have collected and analyzed data from the interactions of thousands of active users and confirm our claims.
In this demonstration paper, we present the Anyplace system that relies on the abundance of sensory data on smartphones (e.g., WiFi signal strength and inertial mea- surements) to deliver reliable indoor geolocation information. Our system features two highly desirable properties, namely crowdsourcing and scalability. Anyplace implements a set of crowdsourcing-supportive mechanisms to handle the enormous amount of crowdsensed data, filter incorrect user contributions and exploit WiFi data from heterogeneous mobile devices. More- over Anyplace follows a big-data architecture for efficient and scalable storage and retrieval of localization and mapping data.
In this demonstration paper we present SmartLab, an architecture for managing a cluster of both Android Real De- vices (ARDs) and Android Virtual Devices (AVDs) via an intuitive web-based interface. Our architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner as well as executing mockup experiments by “feeding” the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our testbed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.
The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. In this paper, we make three major contributions: First, we propose a comprehensive architecture, coined SmartLab, for managing a cluster of both real and virtual smartphones that are either wired to a private cloud or connected over a wireless link. Second, we propose and describe a number of Android management optimizations (e.g., command pipelining, screen-capturing, file management), which can be useful to the community for building similar functionality into their systems. Third we conduct extensive experiments and microbenchmarks to support our design choices providing qualitative evidence on the expected performance of each module comprising our architecture. This paper also overviews experiences of using SmartLab in a research-oriented setting and also ongoing and future development efforts.
In this paper we present our Crowdsourced Linked Open Data Architecture (CLODA), a first attempt to combine crowdsourcing, localization and location-based services to gener- ate, collect, validate and relate real-world, geo-spatial and multi- dimensional information using smartphones and other mobile devices. CLODA focuses on the construction of URI addressable, interlinked and semi-structured data following the Linked-Open Data (LOD) paradigm. The validity of the constructed data is then contributed by a participating crowd. We present our prototype implementation on top of Google Maps and a blend of in-house technologies, particularly our indoor positioning framework, coined Airplace, our trajectory similarity framework, coined SmartTrace, our neighborhood detection framework, coined Proximity and our smartphone testing platform coined SmartLab.
Big data refers to data sets whose size and structure strains the ability of commonly used relational DBMSs to capture, manage, and process the data within a tolerable elapsed time. Big data sizes commonly range from a few dozen terabytes to many petabytes in a single database and their underlying data model might be anything from structured (relational or tabular) to semi-structured (XML or JSON) or even unstructured (Web text and log files). Big data architectures are highly parallel and distributed in order to cope with the inherent I/O and CPU limitations. Such systems typically perform on mid-scale private clouds, offering higher privacy, to large-scale public clouds, both exposing operational and analytic functionality stand-alone or as-a-Service. This talk aims to overview the current big-data management landscape, the underlying technologies and their provenance, the latest NoSQL and NewSQL trends, possible applications of big-data management systems for online and offline processing of sensor data, text data, social data and medical data in enterprise environments. The talk will also overview ongoing big-data research and teaching activities at the University of Cyprus.
Consider a centralized query operator that identifies to every smartphone user its k geographically nearest neighbors at all times, a query we coin Continuous All k-Nearest Neighbor (CAkNN). Such an operator could be utilized to enhance public emergency services, allowing users to send SOS beacons out to the closest rescuers, allowing gamers and social networking users to establish ad-hoc overlay communication infrastructures, in order to carry out complex interactions. In this paper, we study the problem of efficiently processing a CAkNN query in a cellular or WiFi network, both of which are ubiquitous. We introduce an algorithm, coined Proximity, which answers CAkNN queries in O(n(k+λ)) time, where n denotes the number of users and λ a network-specific parameter (λ << n). Proximity does not require any additional infrastructure or specialized hardware and its efficiency is mainly attributed to a smart search space sharing technique we introduce. Its implementation is based on a novel data structure, coined k+heap, which achieves constant O(1) look-up time and logarithmic O(log(k*λ)) insertion/update time. Proximity, being parameter-free, performs efficiently in the face of high mobility and skewed distribution of users (e.g., the service works equally well in downtown, suburban, or rural areas). We have evaluated Proximity using mobility traces from two sources and concluded that our approach performs at least one order of magnitude faster than adapted existing work.
The majority of smartphone localization systems useAssistedGPS for fine-grained localization in outdoor spaces or WiFibased RSS (Received Signal Strength) technologies for coarsegrain positioning in indoor and outdoor spaces. The former consumes precious energy from mobile devices, is strictly affected by the environment (e.g., cloudy day, forests, etc.) and does not work in indoor spaces. The latter collects RSS from WiFi beams within a user’s vicinity and transfers an RSS vector to the server for localization, in which the position of the user is disclosed possibly violating users’ privacy. In this paper, we present BloomMap, an innovative and efficient algorithm that conducts a localization process without unveiling the user’s location to the localization service, minimizing the energy consumption of the mobile unit and also minimizing the network traffic by not transferring large positioning structures to the client (i.e., known as radiomap). Our framework is designed for planet-scale RSS localization scenarios, which are expected to emerge in the near-future. In particular, a user may localize itself using a subset of a vast data repository of RSS signals that is updated in real time by smartphone wardrivers. Our preliminary evaluation shows that our propositions can localize a device without unveiling its location in approximately 80% less time, energy and network resources than competitive approaches. We also describe our WiFi-based prototype system developed on the Android OS.
In this paper we present a powerful distributed framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, coined SmartTrace, exploits opportunistic and participatory sensing in order to quickly answer queries of the form: 'Report the users that move more similar to Q, where Q is some query trace '. SmartTrace, relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and data-disclosure reasons. SmartTrace then deploys an efficient top-K query processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. We assess our ideas with realistic and real workloads from Microsoft Research Asia and other sources. Our study reveals that SmartTrace computes the desired results with 74% less energy consumption and 13% faster than its centralized and decentralized counterparts. Our experimental results also confirm our analytical study.
The bulk of social network applications for smartphones (e.g., Twitter, Facebook, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smartphone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined SmartOpt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owner 's smartphones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsoft 's Geolife project and social patterns derived by DBLP. Our study reveals that SmartOpt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, sensors are typically organized in structures coined Query Routing Trees (QRTs). Our study reveals that predominant data acquisition systems construct QRTs in ad-hoc manners leading to a significant waste of energy. In this talk I will present MicroPulse+, a framework for minimizing the consumption of energy during data collection in Sensor Networks. MicroPulse+ eliminates a variety of data transmission and data reception inefficiencies using a collection of in-network algorithms. In particular, MicroPulse+ introduces: i) the Workload-Aware Routing Tree (WART) algorithm, which is established on profiling recent data collection activity and on identifying the bottlenecks using an in-network execution of the critical path method; and ii) the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions. The talk will conclude with an outlook into current and future research work.
We present a novel distributed algorithm (MHS) that constructs a query routing tree that minimizes collisions during query execution. It was shown in previous work that minimizing collisions during query execution saves significant amount of energy[1]. In the same paper it is shown that balancing the node degrees of a query routing tree significantly reduces collisions during query execution. We address the inefficiencies of the previously proposed algorithm and propose a simpler, purely distributed, parameter-free, cheaper and more efficient algorithm. Our resulting query trees are optimally balanced, guarantee minimum collisions and minimum latency for query execution and allow for opportunistic in-network processing. MHS poses the minimum possible communication overhead to the network and is parameter-free as opposed to previously proposed algorithms. Our proposed algorithm can be used for acquiring data from the nodes of any distributed systems where the main objective is to minimize the communication cost.
In long-term deployments of Wireless Sensor Networks, it is often more efficient to store sensor readings locally at each device and transmit those readings to the user only when requested (i.e., in response to a user query). Many of the techniques that collect information from a sensor network require that the data is sorted on some attribute (e.g., range queries, top-k queries, join queries, etc.) Yet, the underlying storage medium of these devices (i.e., Flash media) presents some unique characteristics which renders traditional disk-based sorting algorithms inefficient in this context. In this paper we devise the FSort algorithm, an efficient external sorting algorithm for flash-based sensor devices with a small memory footprint. FSort minimizes the expensive write/delete operations of flash memory minimizing in that way the consumption of energy. In particular, FSort uses a top-down replacement selection algorithm in order to produce sorted runs on flash media in a log-based manner. Sorted runs are then recursively merged in order to yield the sorted result. Our experimentation with real traces from Intel Research Berkeley show that FSort greatly outperforms the traditional External Mergesort Algorithm both in regards to time and energy consumption. We found similar advantages in regards to the wearability constraints of flash media.
This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of events from the network in cases of failures. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures are less than 60\% failure then we can recover over 80\% of generated events exactly.
Continuous queries in Wireless Sensor Networks (WSNs) are founded on the premise of Query Routing Tree structures (denoted as T ), which provide sensors with a path to the querying node. Predominant data acquisition systems for WSNs construct such structures in an ad-hoc manner and therefore there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions, thus reducing energy consumption, during data acquisition in WSNs. We show through real micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that ETC can provide significant energy reductions under a variety of conditions prolonging the longevity of a wireless sensor network.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. Research in this area has to this day primarily focused on the trade-off between local computation and communication in order to minimize the transfer of data over the fundamentally expensive wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy in Wireless Sensor Networks as the communication channel is only accessed for answering on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each sensor has to be complemented by efficient access methods that will speed up the execution of queries when required. In this talk I will provide an overview of recent developments in Wireless Sensor Network Technology and highlight some important data indexing and searching challenges that arise in this context. In particular, I will present MicroHash which is an external memory index structure that is tailored to the distinct characteristics of flash memory, the most prevalent type of non-volatile memory used in sensor systems.
ICGrid (Intensive Care Grid) is a distributed platform that enables the seamless integration, correlation and retrieval of clinically interesting episodes across Intensive Care Units, which is currently under development by our group. Such a task requires huge processing and data storage capabilities, which are common attributes of Grid infrastructures. ICGrid is based on a hybrid architecture that combines i) a heterogeneous set of monitors that sense the inpatients and ii) Grid technology that enables the storage, processing and information sharing task between Intensive Care Units.
The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is to make these systems \em dependable. In this presentation we present \em FailRank a nove
This paper assumes a set of $n$ mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter in order to minimize energy consumption while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a message complexity of O(p + n), where p denotes the number of nodes on the perimeter and $n$ the overall number of nodes. For storage and replication we devise a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles and minimum bounding cuboids. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates.
In this talk I will introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wildlife monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions. In this talk, I will also present techniques to efficiently answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric, such as the utilization of the communication medium, which is associated with the retrieval of the desired answer set. I will provide an overview of state-of-the-art algorithms that solve the Top-K problem in a centralized setting and show why these are not applicable to the distributed case. I will then focus on the Threshold Join Algorithm (TJA), which is a novel solution for executing Top-K queries in a distributed environment. I will also present results from our performance study with a real middleware testbed deployed over a network of 75 workstations.
The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is to make these systems \em dependable. In this paper we present \em FailRank a nove
In this paper we present MicroPulse, a novel framework for adapting the waking window of a sensing device S based on the data workload incurred by a query Q. Assuming a typical tree-based aggregation scenario, the waking window is defined as the time interval t during which S enables its transceiver in order to collect the results from its children. Minimizing the length of t enables S to conserve energy that can be used to prolong the longevity of the network and hence the quality of results. Our method is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the Critical Path Method. We show through trace-driven experimentation with a real dataset that MicroPulse can reduce the energy cost of the waking window by three orders of magnitude.
In this paper we introduce MINT (Materialized In-Network Top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V ' (\subseteq V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively-defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.
In this talk, I will present techniques to efficiently answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric, such as the utilization of the communication medium, which is associated with the retrieval of the desired answer set. I will provide an overview of state-of-the-art algorithms that solve the Top-K problem in a centralized setting and show why these are not applicable to the distributed case. I will then focus on the Threshold Join Algorithm (TJA), which is a novel solution for executing Top-K queries in a distributed environment. I will also present results from our performance study with a real middleware testbed deployed over a network of 75 workstations.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor and understand the physical world at an extremely high fidelity. Research to this day has primarily focused on the trade-off between local computation and communication, in order to offset the expensive transfer of data over the fundamentally unreliable wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy, as we access the communication channel to answer on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each node has to be complemented by efficient index structures that will enable access to data when required. In this talk we present MicroHash, an external memory index structure which is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. Our index structure exploits the asymmetric read/write and wear characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of a low energy budget.
Intensive Care Units (ICUs) at hospitals utilize cutting edge technology in order to acquire the physiological state of inpatients, which are in a critical (life-threatening) physiological state, at an extremely high fidelity. In particular, ICUs utilize a very large number of monitoring and sensing devices that are continuously attached on inpatients in order to uncover the physiological state of the inpatients. Such measurements can then be utilized for i) education, ii) early diagnosis and iii) for defining early warning systems that identify when a human life is jeopardy. A problem with the current setting is that individual ICUs are limited to the locally acquired measurements. As a result, the number of clinically 'interesting ' episodes available to doctors is also very limited. ICGrid (Intensive Care Grid) is a distributed platform that enables the seamless integration, correlation and retrieval of clinically interesting episodes across Intensive Care Units, which is currently under development by our group. Such a task requires huge processing and data storage capabilities, which are common attributes of Grid infrastructures. ICGrid is based on a hybrid architecture that combines i) a heterogeneous set of monitors that sense the inpatients and ii) Grid technology that enables the storage, processing and information sharing task between Intensive Care Units. Our demonstration aims at presenting the first part of the hybrid architecture of ICGrid (i.e. the acquisition of real signals from inpatients and their storage on the Grid). Our demonstration platform operates on a standalone laptop. In a real setting, this software is able to extract the physiological parameters from monitoring devices installed at ICUs.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor and understand the physical world at an extremely high fidelity. Research to this day has primarily focused on the trade-off between local computation and communication, in order to offset the expensive transfer of data over the fundamentally unreliable wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy, as we access the communication channel to answer on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each node has to be complemented by efficient index structures that will enable access to data when required. In this talk we present MicroHash, an external memory index structure which is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. Our index structure exploits the asymmetric read/write and wear characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of a low energy budget.
Modern Sensor and Peer-to-Peer data management systems have to cope with data that is generated automatically and continuously across distributed and potentially geographically diverse locations. Organizing data in centralized repositories is becoming increasingly expensive and in many occasions impractical. Additionally, users are usually only interested in finding the highest ranked answers to their queries rather that the complete range of answers. In this talk, I will present efficient techniques to answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric which is associated with the retrieval of the desired answer set. My talk focuses on the Threshold Join Algorithm (TJA), which is a novel distributed Top-K query processing algorithm that combines local similarity scores available at each computing site. I will also present the LB-K and UBLB-K algorithms which utilize lower and upper bounds, when exact scores are not available. An extensive experimental evaluation with our distributed middleware testbed reveals that the proposed methods are orders of magnitudes more efficient than their competitors.
We initiate a study on the effect of the network topology on the performance of Peer-to-Peer (P2P) information retrieval systems. The emerging P2P model has become a very powerful and attractive paradigm for developing Internet-scale systems for sharing resources, including files, or documents. We show that the performance of Information Retrieval algorithms can be significantly improved through the use of fully distributed topologically aware overlay network construction techniques. Our empirical results, using the Peerware middleware infrastructure, show that the approach we propose is both efficient and practical.
One important problem in peer-to-peer (P2P) networks is searching and retrieving the correct information. However, existing searching mechanisms in pure peer-to-peer networks are inefficient due to the decentralized nature of such networks. We propose two mechanisms for information retrieval in pure peer-to-peer networks. The fir, the modified Breadth-First-Search (BFS) mechanism, is an extension of the current Gnuttela protocol, allows searching with keywords, and is designed to minimize the number of messages that are needed to search the network. The second, the Intelligent Search mechanism, uses the past behavior of the P2P network to further improve the scalability of the search procedure. In this algorithm, each peer autonomously decides which of its peers are most likely to answer a given query. The algorithm is entirely distributed, and therefore scales well with the size of the network. We implemented our mechanisms as middleware platforms. To show the advantages of our mechanisms we present experimental results using the middleware implementation.