Modeling and Performance Optimization of Wireless Sensor Network Based on Markov Chain

Wireless sensor networks are usually deployed in areas with relatively harsh natural environments, and the collection node transmits data to the destination node through a multi-hop route. Therefore, how to effectively plan the transmission path is an important issue. This paper combines the unbiased gray model with the Markov chain model to establish an unbiased gray Markov chain model, and points out that the unbiased gray Markov chain model also has shortcomings in parameter selection. The particle swarm algorithm is used to improve it, and the mathematical model, calculation principle and various parameters of the particle swarm optimization algorithm are introduced, and the implementation flow chart of the particle swarm algorithm is given. Aiming at the shortcomings of the unbiased gray Markov chain model, the particle swarm algorithm and the unbiased gray Markov chain model are combined to form the particle swarm unbiased gray Markov chain model. The simulation environment and the training environment of the particle swarm unbiased grey Markov chain model were designed in the experiment. The node scheduling optimization experiment proves that the scheduling method based on the particle swarm unbiased grey Markov chain model has achieved better results in coverage and energy consumption balance than the random and shortest distance method. In the routing experiment, the experimental analysis of the node’s Q value proved the convergence of the algorithm, and compared with other protocols, it proved that the routing algorithm can effectively extend the network life cycle and achieve load balancing.