Self-improved butterfly optimization algorithm based cooperative routing model in Wireless Multimedia Sensor Networks

Abstract

Due to its potential influence, Wireless Multimedia Sensor Networks (WMSNs) receive a lot of attention from numerous appealing applications and the scientific studies that surround them. Over the past few years, the most active area of the research field has been routing in WMSNs. Additionally, the routing protocol is used to transfer the multimedia data that identifies the most effective resources and stable paths, as well as to guarantee various degrees of Quality-of-Experience (QoE) and Quality Of Service (QoS). It remains a difficult aspect, nevertheless, due to the complex responsibilities of multimedia, dynamic variations in network state, and a shortage of network resources. In order to choose the most appropriate cooperative nodes, this research seeks to implement a Deep Belief Network (DBN) based decision-making system. In addition, restrictions such as Tri-level energy consumption of nodes (Level 1: Low energy level for textual data, Level 2: Mid energy level for text, image, and video information, Level 3: Highly energetic level for high definition pictures), (ii) Reliability, and (iii) Delay are investigated while transferring the multimedia data in the network. Self-Improved Butterfly Optimization Algorithm (SIBOA) is proposed to tune the optimal weight of the DBN in order to make the correct choice. Finally, a comparison of the selected scheme's performance to various conventional schemes is made in terms of alive nodes, delay, residual energy, network lifetime, and reliability analysis.