Providing Trustworthy Contributions via a Reputation Framework in Social Participatory Sensing Systems
This addresses trust issues for users in social participatory sensing, but it is incremental as it builds on existing reputation and social network concepts.
The paper tackles the problem of ensuring trustworthy contributions in social participatory sensing systems by proposing a reputation framework that combines contribution quality and participant trustworthiness using a fuzzy inference system and PageRank algorithm, achieving high overall trust and accurate reputation scores in simulations.
Social participatory sensing is a newly proposed paradigm that tries to address the limitations of participatory sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic reputation framework for social participatory sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A reputation score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate reputation scores.