Abstract
To solve the problems of single evaluation attributes and highly overlapping trust paths in the current trust model, a multiattribute trust evaluation model based on the K shortest paths (KSP) algorithm is proposed. The model refines the evaluation attributes among nodes and uses the analytic hierarchy process (AHP) to allocate the weights based on users' preferences to meet the special needs of individual users. Also, the model introduces the penalty factor algorithm idea of KSP and proposes a trust path optimization algorithm RKSP based on the A* algorithm. It can filter highly overlapping trust paths during the formation of recommended trust paths so that the searched trust paths have certain differences. Through comparative experiments, it is proven that the model can reduce the resource overhead of edge devices, improve the accuracy of evaluation, ensure load balancing within the domain, and better align the results of the model recommendation with user needs.
Highlights
The rise of the Internet of Everything and 5G technology leads to new computing models such as fog computing [1], edge computing [2] and mobile edge computing [3]
To solve the above problems, this paper introduces the K shortest paths (KSP) algorithm and proposes a multiattribute evaluation model based on the KSP optimization algorithm
It was difficult for edge devices to undertake complex storage and trust aggregation tasks
Summary
The rise of the Internet of Everything and 5G technology leads to new computing models such as fog computing [1], edge computing [2] and mobile edge computing [3]. Huang et al [20] weighted different trust dimensions according to familiarity, similarity and timeliness and maintained and updated the trust information of local vehicles by using a vector machine and multiweight subjective logic He et al [21] combined the Bayesian reasoning method and D-S evidence theory and optimized the uncertainty in the trust evaluation of mobile social networks by using a deep learning algorithm. (2) The existing trust evaluation model based on graph theory ignores the deviation of evaluation results, which is caused by the high overlap of trust paths This model has difficulty in resisting collusion attacks between devices and it increases the calculation costs. This section constructs a trust evaluation model based on multiple attributes, and gives the description and calculation equation of the trust relationship between devices.
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