Energy Adaptive and Max-Min based BFS Model for Route Optimization in Sensor Network

Background: The restricted energy and network life is a critical issue in the real-time sensor network. The occurrence of low-energy and faulty intermediate nodes can increase communication failure. The number of intermediate nodes affects the number of re-transmission, communication-failures, and increases the energy consumption on the routing path. The existing protocols take the greedy decision on all possible intermediate nodes collectively by considering one or more parameters. Objective: This work divides the distance between the source and destination into coveragespecific zones for restricting the hop-count. Now each zone is processed individually and collectively for generating the energy effective and failure preventive route. Methods: In this paper, the energy and coverage weighted BFS (Best First Model) algorithm is presented for route optimization in the sensor network. The max-min BFS is implied on sensor nodes of each zone and identified as the most reliable and effective intermediate node. The individual and composite weighted rules are applied to energy and distance parameters. This new routing protocol discovered the energy-adaptive route. Results: The proposed model is simulated on a randomly distributed network, and the analysis is done in terms of network life, energy consumption, hop count, and the number of route switching parameters. The comparative analysis is done against the MCP, MT-MR, Greedy, and other stateof- art routing protocols. Conclusion: The comparative results validate the significance of the proposed routing protocol in terms of energy effectiveness, lesser route switching, and improved the network life.