Hybrid Sparrow Search Optimization technique for quality of service cooperative routing in underwater acoustic sensor networks

Abstract

Underwater Acoustic networks comprise sensor nodes distributed randomly in a subsea environment. These nodes deployed at varying pressure levels forward the monitored information through multi-hop transmissions. Data transmission through acoustic links exhibits challenges like frequency-dependent attenuation, noise, multipath distortion, and low propagating speed of acoustic signal (about 1500 m/s) causing the Doppler effect. Various existing algorithms consider only the energy and pressure levels in the routing process. The consideration of channel quality in routing is necessary to enhance the overall network performance. Hence, it is envisaged that the drawbacks of meta-heuristic techniques in computing the optimal solution can be solved by constructing a hybrid optimization algorithm with the fitness function based on pressure levels, energy, and channel conditions. Hybrid-SSO integrates Sparrow Search Optimization with Grey Wolf Algorithm to detect optimal relay nodes for data transmission towards the destination node. Implementation of the proposed algorithm is done using Aquasim (NS2.30) and its performance is collated with the existing techniques. Simulation results show that the proposed Hybrid-SSO successfully reaches a maximum Packet Delivery Ratio of 99.2% for 180 nodes with a transmission range of 100 m while reducing the Average End to End Delay, Average Energy Consumption, and No. of Dead nodes.