Publications

Abstract : In the wireless sensor networks(WSNs) with static and dynamic nodes, the movement of nodes or failure of sensor nodes may lead to the breakage of the existing routes. End-to-end delay, power consumption, and communication cost are some of the most important metrics in a wireless sensor networks when routing from a source to a destination. Recent approaches using the swarm intelligence (SI) technique proved that the local interaction of several simple agents to meet a global goal has a significant impact on WSNs routing. In this paper, a proposed routing algorithm that has an ant colony optimisation (ACO) algorithm with an endocrine cooperative particle swarm optimisation algorithm (ECPSOA) is used to improve the various metrics in WSNs routing. The ACO algorithm uses mobile agents as ants to identify the most feasible and best path in a network. Additionally, the ACO algorithm helps to locate paths between two nodes in a network. In the ECPSOA, finds the best solution for a particle's position and velocity, which can enhance the capacity of global search and improve the speed of convergence and accuracy of the algorithm. This routing algorithm has an improved performance when compared with the simple ACO algorithm in terms of delay, power consumption, and communication cost. Simulate with the help of network simulator OMNNET++, and analysis the result.