The main idea behind social participatory sensing is to leverage social friends to participate in mobile sensing tasks. A main challenge, however, is the identification and recruitment of sufficient number of well-suited participants. This becomes especially more challenging for large-scale online social networks with unknown network topology and complex friendship relations. Moreover, the potential sparseness of the friendship network may result in insufficient participation, thus reducing the validity of the obtained information. In this paper, we propose a participant selection framework to address the aforementioned limitations. The nomination module of the framework makes use of a customised random surfer to crawl the requester's social graph and identify suitable nominees. The nominee selection is determined as a function of the members' suitability scores and pairwise trust perception among members. The selection module is responsible for selecting the required participants from the set of nominees based on the nominee's timeliness, the number of participants selected so far and the task's remaining time. Moreover, the selection is done in a way that prevents from the formation of a colluding group among the selected participants. Simulation results demonstrate the efficacy of our proposed framework in terms of selecting a large number of reputable participants with high suitability scores, in comparison with state-of-the-art methods.

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