EEMC

Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy—among other things—may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost “for free”: with only an insignificant additional amount of energy required to piggy-back the data—usually incoming task assignments and outgoing sensor results—on top of the call. Here, we present an <i>Energy-Efficient Mobile Crowdsensing</i> (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed <i>EEMC</i> framework outperforms the baseline approaches, and it can reduce overall energy consumption in data transfer by 54--66% when compared to the 3G-based solution.

Authors

• Haoyi Xiong Baidu Inc.
• Daqing Zhang
• Leye Wang
• J. Paul Gibson
• Jie Zhu School of Artificial Intelligence and Computer Science, Jiangnan University

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