Multi‐objective design of energy harvesting enabled wireless networks based on evolutionary genetic optimisation. Issue 6 (3rd November 2020)

• Record Type: Journal Article
• Authors: Mehta, Ridhima
• Is Part Of: IET networks. Volume 9:Issue 6(2020)
• Page Start: 360
• Page End: 366
• Publication Date: 2020-11-03
• Subjects: search problems -- Pareto optimisation -- genetic algorithms -- evolutionary computation -- energy conservation -- energy harvesting -- access protocols -- telecommunication network reliability -- radio spectrum management -- telecommunication power management; evolutionary genetic optimisation -- energy‐restrained wireless network services -- energy conservation -- spectrum outage -- genetic optimisation model -- energy efficiency -- Pareto‐optimal frontier -- wireless network design -- Pareto‐based evolutionary genetic algorithm technique -- heuristic design procedure -- multiobjective optimisation problem -- wireless communications -- medium access control -- energy harvesting operation -- minimal packet transmission failures; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2020.0093
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : Sustainable operation of energy‐restrained wireless network services requires multiple objectives to be satisfied synchronously. Among these objectives, reduced spectrum outage, energy conservation, and minimal packet transmission failures considerably affect the energy harvesting operation of these networks. These three objectives are associated with disparate protocol layers incorporating the transport, medium access control, and physical layers of traditional networking architecture. The authors investigate energy harvesting wireless communications by formulating the multi‐objective optimisation problem comprising these global networking criteria, which are simultaneously optimised with the heuristic design procedure. For this, they employ a Pareto‐based evolutionary genetic algorithm technique built in the wireless network design and operation to find the optimal set of all non‐dominated solutions traversing the entire design search space. Besides, iterative implementation of the presented genetic optimisation model with distinct feasible integrations of crossover and mutation operations is performed to evaluate the proficiency of the proposed scheme for evaluating the Pareto‐optimal frontier set. The influence of different combinations of these operations is examined and adaptively applied with appropriate genetic parameters tuning for efficient meta‐heuristic search through the candidate solution space. Simulation results demonstrate that the proposed hybrid genetic mechanism outperforms the existing methods in terms of throughput, energy efficiency, and loss rate.