An efficient incentive mechanism for cloud-based mobile sensor network

Abstract

Mobile sensor networks (MSNs) enable extensive applications of data collection, such as accident report in transportation and health prediction in public health. Incentive mechanism (IM) is applied for sensing user (SU) recruitment. However, the IM used in traditional MSN is not efficient due to limited information of SU used for recruitment. With the development of cloud computing technology, cloud-based MSN is the trend to use more information of SUs for IM design to improve its efficiency. In this paper, a novel cloud-based MSN model is presented. Three parties are considered, including data request party, cloud-based platform and SUs. A data quality model is proposed to measure the credit level of SUs. In addition, with consideration of social connections of SUs, a SU recruitment strategy is presented. SUs are divided into first and second degrees based on how they join the sensing task. The utility functions of first degree SUs and cloud-based platform are presented, respectively. At last, an efficient IM is proposed by formulating a Stackelberg game. The performance of the proposed IM on data quality and SU recruitment time comparing with other method are presented and discussed. The simulation results illustrate that the proposed IM ensures data quality for data request party and recruits SUs more efficiently.