Reliability Evaluation for WSNs Based on Deep Reinforcement Learning and Graph Neural Networks

Wireless Sensor Network (WSN) reliability evaluation is essential for ensuring the stable operation of network. Traditional methods usually focus on the network topology structure, and calculate the normal operation probability of WSNs. However, these methods usually ignore the energy consumption and network lifetime. In this paper, a novel reliability evaluation algorithm TLR is proposed, which calculates the network lifetime under dynamic network environment according to the pre-set network topology structure reliability threshold, and realizes the comprehensive reliability analysis of network topology and lifetime. In addition, as the basis for reliability evaluation, this paper proposes a new deep reinforcement learning network framework GNN-AC combining graph neural network and actor-critic network, which solves the challenge of constructing Virtual Backbone Network (VBN) in dynamically operating networks. Based on the self-defined fitness matrix and fitness value, the objective function is set to optimize the VBN construction scheme to accurately calculate the network lifetime, and the relationship between the reliability of network topology and network lifetime is discussed. Simulations are carried out for various sizes of WSNs to show the advantages and effectiveness of the proposed approach in estimating network lifetime and reliability evaluation.