TA-CROCS: Trustworthiness-Aware Coalitional Recruitment of Crowd-Sensors

The Internet of Things (IoT) data can be acquired in crowd-solicited manner, which is based on sensory data acquisition through users of data-enabled devices. Widely known as Mobile Crowd-Sensing (MCS), this method is a user-driven paradigm which facilitates multi-sensory data acquisition through built-in sensors of mobile devices. Despite its benefits, MCS-based services may suffer from low trustworthiness of acquired data. Another issue is the reliability of collected data as there is a risk of intentional tampering or falsified sensor readings. Furthermore, MCS-based services seek innovative methods to influence users' intention to participate in sensing campaigns. Since the effectiveness of MCS relies on the value of the crowd-sensed data, it is crucial to ensure that participants provide trustworthy sensory data. In this paper, we propose Trustworthiness-Aware Coalitional Recruitment of Crowd-Sensors (TA-CROCS), a coalitional game-based user recruitment and incentive solution to improve participation of truthful users by increasing their payoff as well as respecting the privacy of smartphone users during the sensing campaigns. Through simulations, we show that in the presence of malicious users, reputation-aware and coalitional game-based recruitment outperforms collaborative reputation-based crowd-sensor selection by up to 25% in terms of platform utility and up to 10% in terms of user utility.