Energy Efficient Neuro-Fuzzy Cluster based Topology Construction with Metaheuristic Route Planning Algorithm for Unmanned Aerial Vehicles

At present times, unmanned aerial vehicles (UAVs) received significant attention among several application areas and services in both defense and civilian domains. The existence of many UAVs performs difficult process effectually when they are arranged in an adhoc way. The restricted battery capacity of the UAVs, rapid mobility, and high dynamic nature of the UAVs necessities the design of energy efficient clustering and routing protocols. With its motivation, this paper develops an Energy Efficient Neuro-Fuzzy Cluster based Topology Construction with Metaheuristic Route Planning (EENFC-MRP) algorithm for UAVs. The presented model involves EENFC based clustering and MRP based routing processes. The EENFC model make use of three input parameters namely Residual Energy in UAV, Average Distance to Nearby UAVs, and UAV Degree for the cluster construction. In addition, Quantum Ant Lion Optimization (QALO) based MRP is applied to choose an optimal set of routes for intercluster UAV communication. In order to investigate the energy efficient outcome of the EENFC-MRP algorithm, a series of simulation processes were carried out and the results are examined under several aspects. The resultant experimental values ensured the betterment of the EENFC-MRP algorithm over the existing models interms of energy efficiency, throughput, network lifetime, and average delay.

Automated summary

This paper develops an Energy Efficient Neuro-Fuzzy Cluster based Topology Construction with Metaheuristic Route Planning (EENFC-MRP) algorithm for UAVs, which is proven to be superior in terms of energy efficiency compared to existing models through a series of simulation processes.