Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 5, Issue 1, Pages 246-256Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2017.2779176
Keywords
Incentive-aware mechanism; mobile opportunistic crowdsensing; Nash bargaining; time-sensitive data collection
Categories
Funding
- Beijing Natural Science Foundation [4161001]
- National Natural Science Foundation Projects of International Cooperation and Exchanges [61720106010]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621063]
- National Natural Science Foundation of China [61572347, 61473024]
- U.S. National Science Foundation [CNS-1319915, CNS-134335]
- U.S. Department of Transportation Center for Advanced Multimodal Mobility Solutions and Education
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Mobile crowdsensing systems aim at providing various novel sensing applications by recruiting pervasive users with mobile devices, which are now equipped with enriched built-in sensors (e.g., GPS, microphone, camera, gyroscope, accelerometer, etc.). A key factor to enable such systems is substantial participation of large amount of mobile users. In this paper, we focus on the data collection in mobile opportunistic crowdsensing, where the data can be transferred between mobile users via opportunistic device-to-device communications. The goal is to deliver the sensed data from the collector to the corresponding requester, which can maximize the collector's rewards. Here, we assume that the data collection has time-sensitive characteristics, i.e., the reward is time-sensitive. We consider selfish mobile users with rational behaviors, and propose a credit-based incentive-aware mechanism to stimulate mobile users to participate in data collection for mobile opportunistic crowdsensing. Particularly, we propose an effective mechanism to define the expected rewards for the sensed data, and formulate the sensed data trading as a two-person cooperative game, whose solution is obtained through the Nash bargaining theory. Extensive simulations based on both synthetic and real-world mobility traces are conducted to validate the efficiency of our incentive-aware mechanisms.
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