The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.
@cite_10 also proposed a distributed recruitment and data collection framework for opportunistic sensing. The recruitment component exploits the suitability of user behaviours and based on the mobility history information, it recruits only the nodes that are likely to be in the sensing area when the sensing activity is taking place. As a distributed recruitment framework, a set of recruiting nodes visit the sensor area before the campaign is launched and then disseminate recruitment messages. In order to transfer collected sensor data to the requester, a collection of nodes called data sinks are used and participating nodes opportunistically exploit ad hoc encounters to reach data sinks that are temporarily deployed in the sensed area.
@cite_10
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People-centric sensing is a novel apporach that exploits the sensing capabilities offered by smartphones and the mobility of users to sense large scale areas without requiring the deployment of sensors in-situ. Given the ubiquitous nature of smartphones, people-centric sensing is a viable and efficient solution for crowdsourcing data. In this work, we propose a fully distributed, opportunistic sensing framework that involves two main components which both work in an ad hoc fashion: Recruitment and Data Collection. We analyzed the feasibility of our distributed approach for both components through preliminary simulations. The results show that our recruitment method is able to select 66 of the nodes that are appropriate for the sensing activity and 88 of the messages sent by these selected nodes reach the sink by using our data collection method.