Multi-Perspective Trust Management Framework for Crowdsourced IoT Services

Abstract : We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective’s influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.
 ? Existing trust models suffer from this attack since they rely on a unique trust value that globally characterizes a node including all assisted services. ? It is more complex to quantify the former term, namely context similarity in terms of type of service, since multiple collaborative services exist that share little in common. ? There exists a local trust management system within this infrastructure that manages collaboration between nodes for multiple networking services. ? Of course, truly malicious nodes do exist too and have to be dealt with, even though these would likely try to fail the trust metric by camouflaging their misbehaviours.
 ? We address the issue of establishing trust between IoT service providers and consumers. Service providers and consumers are assumed to own IoT devices. ? Therefore, the problem is defined as assessing the context-dependent trust of highly dynamic IoT services. ? However, if such data is missing, the problem becomes a bootstrapping problem. ? Some of the main issues in service provisioning are highlighted as follows: trustworthiness, reliability, and availability. ? The framework tries to overcome these issues by giving users the control over who can use their services and which services they can use (i.e., setting their preferences).
 • The proposed system claims to be safe from bad mouthing and ballot-stuffing attacks, assuming that the deployed agents are trusted-third parties and would not engender these types of attacks. • Eventually, only the best partners with respect to a sought cooperative service are proposed to a requesting node. • The proposed model takes into account both first-hand information (direct observations and own experiences) and second-hand information (indirect experiences and observations reported by neighbouring nodes) to update trust values. • The proposed TMS defines a weighting factor to evaluate the confidence put in recommendations received from other nodes.
 ? Attributes that have no influence on the trust should be filtered out and not be used for trust evaluation as it could degrade the performance of the network and the framework as a result. ? Device perspective attributes can have more impact on the trust in services where the device and its performance is crucial (e.g., sensing and energy sharing services). ? The node’s reputation is computed based on its performance characteristics: packet delivery, forwarding ratio, and energy consumption. ? While IoT crowdsourcing platforms provide distinct opportunities in terms of convenience and efficiency, they also present fundamental challenges.
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