Ant-Fuzzy Meta Heuristic Genetic Sensor Network System for Multi Sink Aggregated Data Transmission

Abstract

Wireless sensor network with the hierarchical organization of sensors aggregate the tasks into groups. The sensor nodes broadcast the aggregated data directly to the distant base station. Existing Mixed Integer Programming (MIP) formulation obtain the good solutions for multi-action processes but not effectual in developing the hybrid genetic algorithms with the Tabu search meta-heuristics ant colony optimization. Another existing work developed for security purpose named as Dynamic secure end-to-end Data Aggregation with Privacy function (DyDAP) decrease the network load but topological configurations with multiple sinks are not addressed. To develop the hybrid genetic algorithm on ant-fuzzy system, Hybrid (i.e.,) ant-fuzzy Meta-heuristic Genetic method (HMG) based on the Tabu search is proposed in this paper. Ant-fuzzy Meta heuristic Genetic method carries out the classification process on the aggregated data. The classification based on the genetic method uses the Tabu search based mathematical operation to attain the feasible solution on multiple sinks. Initially, Ant-fuzzy Meta-heuristic Genetic method classifies the data record based on the weighted meta-heuristic distance. The classified records perform the Tabu search operation to transmit the aggregated data to the multiple sink nodes. HMG method achieves approximately 19 % improved transmitted message rate. Experiment is conducted in the network simulator on the factor such as classification time and transmission rate.