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Facilitating Mobile Crowdsensing from Both Organizers' and Participants DOCTORAT EN CO-ACCREDITATION TÉLÉCOM SUDPARIS - INSTITUT MINES-TÉLÉCOM ET L’UNIVERSITÉ PIERRE ET MARIE CURIE - PARIS 6 Spécialité: Informatique Ecole doctorale: Informatique, Télécommunications et Électronique de Paris Présentée par Leye WANG Facilitating Mobile Crowdsensing from both Organizers’ and Participants’ Perspectives Soutenue le 18 Mai 2016 Devant le jury composé de: Hervé RIVANO Rapporteur Chargé de recherche, HDR, INRIA - France Stéphane GALLAND Rapporteur Maître de conférences, HDR, UTBM - France Steven MARTIN Examinateur Professeur, HDR, Université Paris-Sud - France Djamal ZEGHLACHE Examinateur Professeur, HDR, Télécom SudParis - France Farid NAIT-ABDESSELAM Examinateur Professeur, HDR, Université Paris Descartes - France Abdallah MHAMED Directeur de Thèse Maître de conférences, HDR, Télécom SudParis - France Daqing ZHANG Co-Encadrant Directeur d’études, Télécom SudParis - France Thèse numéro : 2016TELE0008 Declaration I, Leye Wang, hereby declare that this dissertation presents the results of my original research. I have not copied from any others’ work or from any other sources except where due reference or acknowledgement is made explicitly in the text, nor has any part been written for me by another person. Leye Wang March 2016 Abstract With the prevalence of sensor-rich equipped smartphones in recent years, Mobile Crowd- Sensing (MCS) becomes a promising paradigm to facilitate urban sensing applications, such as environment monitoring and traffic congestion detection. MCS achieves the urban sensing goal by leveraging the mobility of mobile users, the sensors embedded in mobile phones and the existing wireless infrastructure. Compared to the traditional urban sens- ing paradigms relying on the expensive specialized infrastructures, MCS can cheaply and efficiently sense large urban regions. During the process of MCS, for both participants and organizers, there exist a variety of concerns affecting whether an MCS task can obtain enough satisfactory sensing results. For participants, these concerns include smartphone energy consumption, mobile data cost, pri- vacy, and incentive, which significantly influence the participants’ willingness to attend an MCS task. For organizers, quality and budget are two primary concerns, which, however, have some intrinsic conflicts, e.g., to achieve a better task quality, often more budget needs to be consumed; thus, balancing the trade-off between quality and budget is a vital issue for organizers to carry out satisfactory MCS tasks. How to appropriately address these partici- pants’ and organizers’ concerns during the process of MCS has attracted a huge amount of research interest nowadays. Following this research direction, in this dissertation, aiming to address both partici- pants’ and organizers’ concerns, we propose two categories of mechanisms for MCS tasks. The first category of mechanism is collaborative data uploading in crowdsensing, where participants help each other through opportunistic encounters in the data upload- ing process of crowdsensing, in order to save energy consumption, mobile data cost, etc. Specifically, two works in this dissertation belong to collaborative data uploading, • Save the participants’ smartphone energy consumption and mobile data cost via collaborative data uploading. Usually two classes of participants exist in MCS tasks: data-plan users, who are mostly concerned with energy consumption; non- data-plan users, who are more sensitive to data cost. Inspired by the observation that non-data-plan users can save mobile data cost by offloading data to data-plan users or uploading data via free Bluetooth/WiFi gateways, and data-plan users can save energy by piggybacking or using more energy-efficient networks than 3G, we propose a collaborative data uploading mechanism, called effSense, which can make intelligent decisions about the appropriate timing and network to upload data for each participant, in order to save both energy consumption and data cost. • Reduce the organizers’ mobile data incentive budget in collaborative data up- loading. Paying participants’ money to cover their mobile data cost is an effective incentive method for the organizers to eliminate the participants’ concern about the mobile data cost. Under the collaborative data uploading mechanism (i.e., certain participants can offload data to others for saving mobile data cost), we further study how to partition the users into two groups corresponding to two price plans Unlimited Data Plan and Pay As You Go, so as to minimize the overall mobile data cost for all participants, i.e., the mobile data incentive budget of the organizers. Based on pre- dicting users’ mobility patterns and sensed data size, we propose a genetic algorithm called ecoSense to do the participant partitioning. The second category of mechanisms is called sparse mobile crowdsensing. To reduce the sensing costs, such as energy and incentive, while still achieving satisfactory data qual- ity, we propose sparse mobile crowdsensing to intelligently select only a small part of the target area for sensing, while inferring the data of the remaining unsensed area with high accuracy. Specifically, we also conduct two works for sparse mobile crowdsensing. • Reduce the organizers’ incentive budget while guaranteeing a required level of quality via sparse mobile crowdsensing. Inspired by the spatial and temporal cor- relations among the data sensed in different sub-areas, we propose sparse mobile crowdsensing, which leverages such correlations to significantly reduce the required number of sensing tasks allocated, thus lowering the organizers’ incentive budget, yet ensuring the data quality. We implement a sparse mobile crowdsensing frame- work, called CCS-TA (Compressive CrowdSensing Task Allocation), combining the state-of-the-art compressive sensing, Bayesian inference, and active learning tech- niques, to dynamically select a minimum number of sub-areas for sensing in each cycle, while inferring the missing data of unsensed sub-areas under the data quality guarantee. • Protect the participants’ location privacy through quality-optimized differential location obfuscation in sparse mobile crowdsensing. To protect participants’ loca- tion privacy in sparse mobile crowdsensing, we adopt a differential location privacy notion called -region-ambiguity to provide guaranteed level of privacy regardless of an adversary’s prior knowledge. As differential location privacy protection can cause data quality loss due to the discrepancy between the original and obfuscated loca- tions, we propose a linear program, called DUM-e (Data Uncertainty Minimization under the constraints of -region-ambiguity and evenly-distributed obfuscation), to select the optimal location obfuscation function that attempts to minimize the data quality loss incurred by the obfuscation. Finally, we summarize the insights learned from both collaborative uploading and sparse mobile crowdsensing mechanisms, and discuss future research directions, such as how to enhance our mechanisms to cope with malicious behaviors of participants, how to adapt our mechanisms to more innovative MCS applications in smart city scenarios, and how to integrate all the techniques proposed in this dissertation into a unified MCS platform. Keywords Mobile Crowdsensing, Energy consumption, Mobile data cost, Data quality, Location privacy, Delay-tolerant data uploading, Data relay, Piggybacking, Task allocation, Com- pressive sensing, Bayesian inference, Active learning, Location obfuscation, Differential privacy, Linear program. Acknowledgments Four years, like a flush, finally lead me here. Not long, not short. But they must be the most important four years for me so far. First of all, I would like to thank my advisors, Prof. Daqing Zhang and Prof. Abdallah Mhamed, for offering me the opportunity to get started on the way to the research. They encouraged and inspired my research, and shared me with their valuable experience in academia. Without their guidance, I would not be able to overcome the difficulties during my PhD study and to complete this dissertation. The talk and discussion with them always bring me new and deep understanding of what is a meaningful research work, how to do it, as well as how to express your ideas to others in an easy way. I also want to express my gratitude to Prof. Bing Xie, my master thesis advisor at Peking University, China. Without his encouragement, I would not choose to pursue the PhD degree; besides, I really appreciated his kindly offering that makes me work as a short-time visiting student at Peking University during my PhD studies, where I learned many research and life advices from him. Second, I appreciate all my colleagues and friends during my studies at Institut Mines Télécom/Télécom SudParis, particularly Dr. Xiao Han, Dr. Mingyue Qi, Dr. Chao Chen, Dr. Dingqi Yang, Dr. Haoyi Xiong, Dr. Jian Zhang, Longbiao Chen, Zaiyang Tang, Tianben Wang. The life with them in France would be always a precious memory for me. Third, I would like to thank my co-authors who assist me tremendously in conducting research works and writing paper manuscripts: Dr. Zhixian Yan, Prof. J. Paul Gibson, Dr. Animesh Pathak, Dr. Brian Y. Lim. Their brilliant suggestions and comments helped to enhance my research works substantially, and are indispensable factors giving rise to the birth of this dissertation. Finally, I express my deepest gratitude to my parents Weimin Sheng and Jian Wang for their unconditional support and love since I came to this world. I would not be able to accomplish
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