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

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.