Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 17, Issue 1, Pages 16-28Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2017.2702613
Keywords
Mobile crowdsensing; user recruitment; semi-markov; uploading cost
Funding
- National Natural Science Foundation of China [61272412]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120061110044]
- NSF US National Science Foundation [CNS 1449860, CNS 1461932, CNS 1460971, CNS 1439672, CNS 1301774, CCS 1231461]
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Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users' main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. We conduct extensive simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency.
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