DEQLFER — A Deep Extreme Q-Learning Firefly Energy Efficient and high performance routing protocol for underwater communication

Abstract

With an advent of Underwater sensor networks, underwater communication has reached its new dimension of research. These networks are characterized by the elongated end to end delay, high energy utility and most importantly dynamic network topologies. By incorporating these characteristics, numerous automated routing algorithms has been proposed to achieve the energy efficient and low latency data transmission. But still, short-comings still exists due to the above mentioned characteristics and the most comprehensive routing algorithms are badly desired. In this article, a novel routing scheme based on Q-learning framework and Deep Extreme Learning Machines aided with Adaptive Firefly Routing algorithm to address the above mentioned research constraints including energy efficiency and network unsteadiness in underwater communication , that practices the hybrid combination of reward function and adaptive fireflies to determine the optimal routing mechanism. In this algorithm, traditional q-learning mechanism has been replaced by the powerful q-deep extreme learning mechanism which uses the adaptive reward function for the varying underwater environment and to boost the packet-delivery ratio (PDR) and throughputs. Also the paper uses the powerful firefly aided routing mechanism to achieve the energy efficient data transmission and to avoid the void dilemma problems. The extensive experimentations has been conducted on the proposed algorithm and compared with other state of art schemes such as Q deep q-Learning energy aware routing protocol (DQLER), DELR Protocols and VBF protocols in which the proposed algorithm has outperformed than the compared existing algorithms in terms of complexity, energy consumption , packet delivery ratio and end to end delay.