Crowdsensing with Social Network-Aided Collaborative Trust Scores

Crowdsensing has appeared as a viable solution for data gathering in many applications with the advent of three emerging paradigms, namely Internet of Things, cloud computing, and mobile social networks. Built-in sensors in mobile devices can leverage the performance of the IoT applications in terms of energy and communication overhead savings by sending their data to the cloud servers. When crowdsensing is used for critical applications such as disaster/crisis management and/or public safety in the context of a smart city, trustworthiness of the collected data occurs as a crucial concern. In this paper, we propose using social network theory to evaluate trustworthiness of crowdsensed data, as well as the mobile devices that provide sensing services. To this end, we combine centralized reputation- based evaluation with collaborative reputation values based on votes and vote capacities. We model each participant as a node in a social network where nodes are inter-connected through their interaction values. Interaction stands for being assigned common sensing tasks. We evaluate the performance of our proposal through simulations, and show that use of social network theory-based crowdsensing with combined reputation formulation significantly improves the utility of the crowdsensing platform while dramatically reducing the manipulation probability of malicious nodes.