Title: Disaster management using D2D communication with ANFIS genetic algorithm-based CH selection and efficient routing by seagull optimisation

Authors: Lithungo K. Murry; R. Kumar; Themrichon Tuithung

Addresses: Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukeidma, Dimapur, India ' Department of Electronics and Instrumentation Engineering, National Institute of Technology Nagaland, Chumukeidma, Dimapur, India ' Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukeidma, Dimapur, India

Abstract: The next generation networks and public safety strategies in communications are at a crossroads in order to render best applications and solutions. There are three major challenges and problems considered here, they are: 1) disproportionate disaster management scheduling among bottom-up and top-down strategies; 2) greater attention on the disaster emergency reaction phase and the absence of management in the complete disaster management series; 3) arrangement deficiency of a long-term reclamation procedure, which results in stakeholder resilience and low level community. In this paper, a new strategy is proposed for disaster management. A hybrid adaptive neuro-fuzzy inference network-based genetic algorithm (D2D ANFIS-GA) is used for selecting cluster head and for the efficient routing seagull optimisation algorithm (SOA). Implementation is done in the MATLAB platform. The performance metrics such as energy utilisation, average battery lifetime, battery lifetime probability, average residual energy, delivery probability, overhead ratio are monitored. Experimental results are compared with the existing approaches, Epidemic and Finder. According to the experimental results our proposed approach gives better results.

Keywords: disaster management; adaptive neuro fuzzy inference network; residual energy; device-to-device; D2D; communication; seagull optimisation algorithm; SOA.

DOI: 10.1504/IJCSE.2021.117017

International Journal of Computational Science and Engineering, 2021 Vol.24 No.4, pp.373 - 384

Received: 01 Jan 2020
Accepted: 11 May 2020
Published online: 12 Aug 2021 *

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