A Survey on Trust Management Mechanism for Internet of Things

The Internet is populated with billions of electronic contraptions that have turned into a piece of our texture. Trust administration assumes an essential part in IoT for dependable information combination and reliable information, qualified administrations with setting � mindfulness, and improved client protection and data security.In network arrangement reliable information handling in remote sensor systems is a quickly rising examination theme. In remote sensor arrange calculation is regularly considerably less vitality devouring than correspondence. Reliability of sensor information is most critical part when detecting undertaking done in remote sensor arrange. In this paper we discuss about the trust management mechanism, wireless sensor network, Internet of Things architecture, and also give the literature survey of some papers

Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing

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 properl

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