  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.
