000206762 001__ 206762
000206762 005__ 20190717172526.0
000206762 0247_ $$2doi$$a10.5075/epfl-thesis-6530
000206762 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6530-5
000206762 02471 $$2nebis$$a10403230
000206762 037__ $$aTHESIS
000206762 041__ $$aeng
000206762 088__ $$a6530
000206762 245__ $$aData Management in Participatory Sensing
000206762 269__ $$a2015
000206762 260__ $$bEPFL$$c2015$$aLausanne
000206762 336__ $$aTheses
000206762 502__ $$aProf. W. Zwaenepoel (président) ;  Prof. K. Aberer (directeur) ;  Prof. B. Faltings,   Prof. I. Podnar,   Prof. A. Zaslavsky (rapporteurs)
000206762 520__ $$aIn recent years there has been a proliferation of privately owned sensing devices such as GPS devices, cameras, home weather stations and, more importantly, smart-phones. Most of these devices are either intrinsically mobile, e.g., smart-phones and GPS devices, or can be easily carried by people during their daily activities. Nowadays, it is possible to embed various sensors in small devices as the result of sensor technology advancement. For example, we can consider smart-phones as sensing devices because they are equipped with several sensors such as GPS, accelerometer, gyroscope, microphone, and proximity sensors. This provides an unprecedented opportunity for a new application paradigm called participatory sensing, in which people collect and share sensing data about some phenomenon of interest in their environment. This unique opportunity is mainly due to (I) the ubiquity of smart-phones with various built-in sensors, (II) the availability of small, low-cost and pluggable sensors, and (III) the easy access to various connectivity media such as 3G, 4G, and WiFi. However, for using the full potential of participatory sensing, several challenges exist that must be addressed. These challenges include, but are not limited to, privacy protection of participants, quality assessment of collected data, efficient energy consumption of sensing devices, data unavailability due to uncontrolled mobility of the participants, and efficiently incentivizing people to participate. In this thesis we propose methods for addressing some of these issues. In particular, this thesis addresses the following topics: ** Efficient Data Acquisition in Participatory Sensing. In participatory sensing systems participant often require to make some level of effort for data collection and sharing, which includes the consumption of the limited resources on their devices. Some people might altruistically participate in such systems. However, it is not realistic to assume that all participants offer this effort altruistically. Therefore, adequate incentives must be given to people to participate. One common approach is to provide the participants with monetary incentives. Additionally, data need not be constantly collected at all places. In many applications, data collection is necessary only when there is some utility for the data. The difference between the value of the collected data to the application and the data collection cost is defined as the utility of the data. We propose a data acquisition framework in Chapter 3 for participatory sensing systems. This framework takes into account the major factors pertinent to this context and efficiently shares sensor data among queries of different types with the objective of maximizing the total utility. Queries for sensor data can come from multiple different applications with arbitrary utility considerations. ** Truthful Data Elicitation in Participatory Sensing. In participatory sensing systems, some participants might have incentives to report wrong data. For example, a participant might report higher costs for her data or wrong location tags for the data with the objective of receiving higher payments. Therefore, it is critical to prevent dishonest behavior of participants by appropriately designing the participatory system. [...]
000206762 6531_ $$aparticipatory sensing
000206762 6531_ $$aprivacy
000206762 6531_ $$aone-shot query
000206762 6531_ $$acontinuous query
000206762 6531_ $$aquality assessment
000206762 6531_ $$autility
000206762 6531_ $$amechanism design
000206762 6531_ $$aoptimization
000206762 6531_ $$afrequent itemset mining
000206762 6531_ $$aincentive compatible
000206762 700__ $$0244042$$g192388$$aRiahi, Mehdi
000206762 720_2 $$aAberer, Karl$$edir.$$g134136$$0240941
000206762 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/206762/files/EPFL_TH6530.pdf$$s1380982
000206762 909C0 $$xU10405$$pLSIR$$0252004
000206762 909CO $$pthesis-bn2018$$pthesis-public$$pDOI$$pIC$$ooai:infoscience.tind.io:206762$$qDOI2$$qGLOBAL_SET$$pthesis
000206762 917Z8 $$x108898
000206762 917Z8 $$x108898
000206762 917Z8 $$x108898
000206762 918__ $$dEDIC2005-2015$$cIIF$$aIC
000206762 919__ $$aLSIR
000206762 920__ $$b2015$$a2015-3-27
000206762 970__ $$a6530/THESES
000206762 973__ $$sPUBLISHED$$aEPFL
000206762 980__ $$aTHESIS