.png){kind=icon}
.png){kind=icon}
Return
Multiattribute Evaluation Model Based on the KSP Algorithm for Edge Computing
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3015041.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Computational modeling
Edge computing
Solid modeling
Load modeling
Analytic hierarchy process
Optimization
Filtering algorithms
Trust model
multiattribute
trust path
KSP algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.