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The smart city concept denotes an intelligent platform consisting of interconnected sensors, embedded devices, decision making systems that process real time data (Chourabi, et al., 2012) (Meier & Lee, 2011) (Batty, 2014). Innovations in smart city design and applications can be accelerated by the integration of cloud computing into several smart city services such as transport, education, energy and water monitoring, healthcare, public safety and other ICT-based applications (Clohessy, Acton, & Morgan, 2014).A promising solution that would improve the benefits of smart city applications can be the integration of built-in sensors in mobile devices and providing the sensing resources as a service to particular smart city applications when needed (Kantarci & Mouftah, Trustworthy Sensing for Public Safety in Cloud-Centric Internet of Things, 2014). This concept is defined as Sensing as a Service (S2aaS) in the literature (Sheng, Xiao, Tang, & Xue, 2013). In S2aaS, distributed smart mobile devices participate in crowdsensing tasks that are requested by end users that are using either fixed computers or smart mobile devices.A comprehensive survey of S2aaS in the context of cloud-centric Internet of Things has been presented in (Kantarci & Mouftah, Sensing as a Service in Cloud-Centric Internet of Things Architecture, 2015).
Trustworthiness of crowdsensed data is a crucial challenge in S2aaS applications (French, Bessis, Maple, & Asimakopoulou, 2012). Kantarci and Mouftah have raised the trustworthiness issue in the recruitment process trough user centric incentives (Kantarci & Mouftah, Trustworthy Sensing for Public Safety in Cloud-Centric Internet of Things, 2014). Trustworthiness of crowdsensed data has been improved by applying a user reputation system that uses statistical accuracy of the crowdsensed data per mobile device. This approach has later been improved by incorporating mobility-awareness (Kantarci & Mouftah, Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things, 2014) and social interaction-awareness (Kantarci & Mouftah, Trustworthy crowdsourcing via mobile social networks, 2014). In order to address trustworthiness issue, there have been studies which take social ties into consideration, and some mobile devices are defined as social sensors denoting the trusted crowdsensing nodes (Hao, Mingjie, Geyong, & Yang, 2014). Furthermore, social trust and reciprocity have been combined to maximize the crowdsensing utility by modeling the problem as maximizing utility of a circulation flow (Gong, Chen, Zhang, & Poor, 2014).