TTLA: two-way trust between clients and fog servers using Bayesian learning automata

摘要

Fog computing is a promising paradigm for use as an efficient architecture for Inter-net-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.