Cooperative routing in wireless multimedia sensor network with deep learning based decision making system

Abstract

Wireless Multimedia Sensor Networks (WMSNs) provide great attention on various attractive applications and their scientific researches due to their potential impact. For the past few years, the most active area related to the research field is routing in WMSNs. Moreover, the multimedia data is transmitted based on the routing protocol for determining the efficient resources and stable path, and also provides different levels of Quality-of-Experience (QoE)/Quality-of-Service (QoS) assurance. Still, it is the difficult aspect based on the complicated operations of multimedia applications, dynamic changes of network condition and limited network resources. This paper intends to introduce a Deep Belief Network (DBN) oriented decision making system to select the suitable cooperative nodes. Moreover, constraints like (i) Tri-level energy consumption of nodes-Level 1: Low energy level for text information, Level 2: Medium energy level for text, image and video information, Level 3: High energy level for high definition pictures (ii) Reliability, and (iii) Delay are examined while transmitting the multimedia information in the network. To make the precise decision, it is planned to tune the optimal weight of DBN by a new hybrid Dragon Integrated Firefly (DIFF) algorithm that combines the concept of Firefly Optimization (FF) and Dragonfly Optimization (DA) algorithm. At last the performance of the adopted scheme is computed over other traditional schemes with respect to alive nodes, delay, residual energy, network lifetime and reliability analysis.