Journal
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS)
Volume -, Issue -, Pages 433-441Publisher
IEEE
DOI: 10.1109/MASS.2018.00067
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Funding
- US National Science Foundation [CNS-1343355]
- National Natural Science Foundation of China [61572347, 61772077]
- Beijing Institute of Technology Research Fund Program for Young Scholars
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With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the tasks. However, the capabilities of individual participants are usually unknown by the selection mechanism, which leads to the most challenging issue of participant selection. While online learning techniques can be used to learn the participant's capability, the diverse expertise of each individual makes a single capability metric is not sufficient. To address the multi-expertise of participants, in this paper we introduce a new self-learning architecture which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Formulating the participant selection problem as a combinational multi-armed bandit problem, we present an online participant selection algorithm with both performance guarantee and bounded regret. Extensive simulations with a real-world mobile dataset demonstrate the efficiency of the proposed solution.
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