Minimizing Energy Expenditures using Genetic Algorithm for Scalability and Longlivety of Multi hop Sensor Networks

Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20–90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization.