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Statler College of Engineering and Mining Resources


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Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models for crowdsensing; however, there is no standard way of identifying adversaries in smart city crowdsensing. This paper adopts previously proposed vote-based approaches, and presents a thorough performance study of vote-based trustworthiness with trusted entities that are basically a subset of the participating smartphone users. Those entities are called trustworthy anchors of the crowdsensing system. Thus, an anchor user is fully trustworthy and is fully capable of voting for the trustworthiness of other users, who participate in sensing of the same set of phenomena. Besides the anchors, the reputations of regular users are determined based on vote-based (distributed) reputation. We present a detailed performance study of the anchor-based trustworthiness assurance in smart city crowdsensing through simulations, and compare it with the purely vote-based trustworthiness approach without anchors, and a reputation-unaware crowdsensing approach, where user reputations are discarded. Through simulation findings, we aim at providing specifications regarding the impact of anchor and adversary populations on crowdsensing and user utilities under various environmental settings. We show that significant improvement can be achieved in terms of usefulness and trustworthiness of the crowdsensed data if the size of the anchor population is set properly

Source Citation

Pouryazdan, M., Kantarci, B., Soyata, T., & Song, H. (2016). Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing. IEEE Access, 4, 529–541.


2169-3536 2016 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.

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