Hybrid routing algorithm for improving path selection in sustainable network

Abstract

Sustainable network are a type of network with sustainable energy. They consist of components and sensors that work in a cooperative manner. Multi-path routing optimization is a promising platform in wireless sensor network (WSN) with performance parameters that are application specific. In this network the sensor nodes generates vast amount of data in the applications like event monitoring, object tracking etc. These sensor data are forwarded to the node designated as sink that consumes lot of energy. It depends on factors like communication path, number of hops, network bandwidth support. Previous studies on optimal multipath routing in WSN are restricted to generate the optimal path using more number of random parametric values. There is limited work focusing on categorizing the paths that are used to route the critical data like traffics related to the real time and non-real time. We devised a hybrid routing algorithm that is a modified version of bio-inspired dynamic programming model of DNA sequence algorithm that results in selection of optimal path using node specific deterministic values from the numerous paths between source and the sink node. Our approach is tested and evaluated through simulation set up with mobility support and compared with evolutionary algorithms ACO, PSO and AOMDV routing protocols. Simulation results are analysed by varying the number of traffics and node density that confirms the critical change in throughput execution and packet delivery ratio, substantial reduction in energy consumption against standard multi-path routing protocols.