Xiaoshan Sun, Jinyang Li, Wei Zheng, Hengchang Liu
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Towards a Sustainable Incentive Mechanism for Participatory Sensing
Participatory sensing plays an important role in Internet of Things (IoT) applications to collect large scale data via powerful sensors from ubiquitous devices. However, without a proper incentive mechanism to pay rewards to the users, the application provider (platform) cannot keep the motivation of users due to their costs during the data collection and uploading process. Moreover, conventional incentive mechanisms can hardly meet the requirements of data quality in various application scenarios in which the importance of data quality differs. In this paper, we present a novel incentive mechanism for sustainable participatory sensing considering the user selection and payment allocation in the long run. It takes data quality and historical participation information into account to prevent users from dropping out. It makes users report their costs truthfully and easily adjusts the adaptability to applications with different quality requirements. Extensive evaluation results demonstrate that our solution outperforms alternative state-of-the-art approaches and significantly improves system sustainability.