With the advent of mobile cloud computing paradigm, mobile social networks (MSNs) have become attractive tools to share, publish and analyze data regarding everyday behavior of mobile users. Besides revealing information about social interactions between individuals, MSNs can assist smart city applications through crowdsensing services. In presence of malicious users who aim at misinformation through manipulation of their sensing data, trustworthiness arises as a crucial issue for the users who receive service from smart city applications. In this paper, the authors propose a new crowdsensing framework, namely Social Network Assisted Trustworthiness Assurance (SONATA) which aims at maximizing crowdsensing platform utility and minimizing the manipulation probability through vote-based trustworthiness analysis in dynamic social network architecture. SONATA adopts existing Sybil detection techniques to identify malicious users who aim at misinformation/disinformation at the crowdsensing platform. The authors present performance evaluation of SONATA under various crowdsensing scenarios in a smart city setting. Performance results show that SONATA improves crowdsensing utility under light and moderate arrival rates of sensing task requests when less than 7% of the users are malicious whereas crowdsensing utility is significantly improved under all task arrival rates if the ratio of malicious users to the entire population is at least 7%. Furthermore, under each scenario, manipulation ratio is close to zero under SONATA while trustworthiness unaware recruitment of social network users leads to a manipulation probability of 2.5% which cannot be tolerated in critical smart city applications such as disaster management or public safety.

With the advent of mobile cloud computing paradigm, mobile social networks (MSNs) have become attractive tools to share, publish and analyze data regarding everyday behavior of mobile users. Besides revealing information about social interactions between individuals, MSNs can assist smart city applications through crowdsensing services. In presence of malicious users who aim at misinformation through manipulation of their sensing data, trustworthiness arises as a crucial issue for the users who receive service from smart city applications. In this paper, the authors propose a new crowdsensing framework, namely Social Network Assisted Trustworthiness Assurance (SONATA) which aims at maximizing crowdsensing platform utility and minimizing the manipulation probability through vote-based trustworthiness analysis in dynamic social network architecture. SONATA adopts existing Sybil detection techniques to identify malicious users who aim at misinformation/disinformation at the crowdsensing platform. The authors present performance evaluation of SONATA under various crowdsensing scenarios in a smart city setting. Performance results show that SONATA improves crowdsensing utility under light and moderate arrival rates of sensing task requests when less than 7% of the users are malicious whereas crowdsensing utility is significantly improved under all task arrival rates if the ratio of malicious users to the entire population is at least 7%. Furthermore, under each scenario, manipulation ratio is close to zero under SONATA while trustworthiness unaware recruitment of social network users leads to a manipulation probability of 2.5% which cannot be tolerated in critical smart city applications such as disaster management or public safety.