Deep Neural Network Algorithm to Improve Link Reliability in Wireless Sensor Networks

Abstract

Wireless Sensor Network (WSN) is distinguished by size, dynamism, and decentralization. These complicated properties give rise to various problems, one of which is the impact of wireless communications on the efficiency of networks and the protocols used for routing. The prediction methods of link reliability can boost the efficiency of the routing algorithms used in WSNs while preventing weak connections. This approach introduces a Deep neural network algorithm to improve link reliability (DILR) in WSN. A Deep neural network (DNN) algorithm is used to evaluate the input parameters like node Received Signal Strength, available bandwidth, delay, and packet received rate parameters for calculating the link reliability output. The available bandwidth parameter recognizes the efficient data transmitting path. The experimental outcomes illustrate that the DILR mechanism improves the link reliability among nodes and reduces routing overhead compared to the conventional mechanism.