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- W4366990045 abstract "Fog computing is a promising paradigm for use as an efficient architecture for Internet-of-Things applications. This architecture’s advantages include proximity, low latency, adaptable resource capacity, and a distributed structure. A considerable amount of generated data and their requisites to real-time processes cause fog nodes to offload the number of tasks to the others, causing trust issues. In this paper, each client prefers to offload a task to a trusted server, and each server prefers to serve trusted clients. This may take some time, especially if we wish to reduce energy consumption. This paper proposes a Bayesian learning automaton-based two-way trust management strategy to address this issue. The proposed method outperforms current state-of-the-art methods regarding energy consumption, network usage, latency, response time, and trust value." @default.
- W4366990045 created "2023-04-27" @default.
- W4366990045 creator A5036480012 @default.
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- W4366990045 date "2023-04-25" @default.
- W4366990045 modified "2023-09-27" @default.
- W4366990045 title "TTLA: two-way trust between clients and fog servers using Bayesian learning automata" @default.
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- W4366990045 doi "https://doi.org/10.1007/s11227-023-05264-6" @default.
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