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A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks

A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks

Dongfeng Fang, Feng Ye, Yi Qian, Hamid Sharif
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 14
ISSN: 1947-9158|EISSN: 1947-9166|EISBN13: 9781522513438|DOI: 10.4018/IJHCR.2017070101
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MLA

Fang, Dongfeng, et al. "A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks." IJHCR vol.8, no.3 2017: pp.1-14. http://doi.org/10.4018/IJHCR.2017070101

APA

Fang, D., Ye, F., Qian, Y., & Sharif, H. (2017). A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks. International Journal of Handheld Computing Research (IJHCR), 8(3), 1-14. http://doi.org/10.4018/IJHCR.2017070101

Chicago

Fang, Dongfeng, et al. "A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks," International Journal of Handheld Computing Research (IJHCR) 8, no.3: 1-14. http://doi.org/10.4018/IJHCR.2017070101

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Abstract

Due to proliferation of smart cities and other smart services, extensive data collection needs to be accomplished by mobile sensor networks (MSNs). However, sensing and data collection are voluntary tasks for many MSN users. For example, drivers are not required to report traffic condition although their vehicles with advanced sensors have easy access to critical information. Therefore, incentive mechanisms are needed to recruit sensing users (SUs). Incentive mechanisms proposed for traditional MSNs cannot be applied directly due to limited information of SU used for recruitment. In this article, the authors propose a novel cloud-based MSN model that consists of three parties, including data request party, cloud-based platform and SUs. To better utilize information of SUs, a data quality model is proposed to measure the credit level of SUs. The proposed SU recruitment strategy takes into consideration social connections of users. According to the strategy, SUs are divided into two separate levels. Moreover, the authors propose an incentive mechanism using a Stackelberg game theoretical approach to achieve the maximum utility of each recruited SU. The simulation results demonstrate that the proposed incentive mechanism can recruit SUs more efficiently while providing data quality guarantee.

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