Participatory sensing network: a paradigm to achieve applications of IoT

Abstract

Internet of Things (IoT) aims to provide ubiquitous connectivity and services for individual persons and machines. The related applications cover various areas in industry, healthcare, city management, etc. The efficient information collection is very critical for the success of these applications. Without enough number of data, it is difficult for a system to provide high-quality services. Participatory sensing network (PSN) is a promising paradigm to efficiently collect information/sensing data. Meanwhile, the incentive mechanism design plays a key role in achieving the collection of enough number of sensing data in PSN, where the sensor-equipped mobile devices are owned and controlled by individual users. Most of existing works on incentive mechanism design focus on the participation of smartphone users, rather than the quality of sensing data. However, data quality is also an important factor for a data collector since smartphone users may submit the erroneous or unreliable data. Low-quality data will impact the accuracy of data analysis result and degrade the provided service. Therefore, data quality should be considered in the incentive mechanism design. Reputation is one way to evaluate the quality of data provided by a mobile user. In this chapter, we introduce the significance of incentive mechanism design for the applications of IoT, then we design a reputation-aware incentive mechanism. Taking the quality of sensing data into account, the proposed mechanism can maximize the weighted social welfare of the whole system and guarantee the nice features of truthfulness and individual rationality. Extensive simulations have been conducted to demonstrate the better performance of the proposed incentive mechanism compared with other existing methods in terms of the weighted social welfare and the average reputation.