Trust-based Routing Optimization using Learning Automata in Wireless Sensor Network

The use of wireless sensor networks is becoming more and more important due to the COVID-19 pandemic and the living conditions of human beings today. The three main goals in designing this type of network are to reduce energy consumption, choose the shortest route and choose a reliable route for data transmission. In this paper, these three goals are considered in routing. Due to the fact that this type of network is exposed to many attacks, identifying malicious nodes and removing them creates security in this type of network. This paper presents an energy-aware and trusted-based routing method using learning automata and an evaluation function. Learning automata identifies trusted nodes (to send data) and malicious nodes using the corresponding evaluation function. The evaluation function considers the residual energy, the node's trust and the number of hops to the sink parameters. Thus, the data reaches its destination in a safe and reliable way. The evaluation results of the proposed method show an improvement in the performance of this method compared to other relevant methods. .