Adaptive transmission power level algorithm according to random deployment of nodes in WSN

Abstract

Wireless sensor nodes are generally deployed randomly in hostile, harsh and inaccessible environments. For this reason, the sensor nodes are supposed to operate over long periods of time without human intervention in order to extend the life of the network as much as possible, and also, it is not possible to restore the nodes or change their positions after their deployment, but by changing the transmitting power level and redeploying a new nodes above the deployment Previously, the network performance improves and we guarantee that the deployed nodes are not lost, and we also guarantee the operation of the network as a whole. The researcher has developed an algorithm "Adaptive transmission power level according to random deployment (ATPLRD)", where the presented algorithm includes determining the power levels relative to random deployment and identifying possible paths in the network in order to reach high interconnection between nodes to achieve the least number of published nodes at the lowest energy levels for the nodes, and also determines the most important nodes in the network whose exit or failure leads to the collapse of the network, and determining The boundary nodes of the network, as well as the weakest coverage areas, which represent gaps in the network, and from it determines the number of nodes needed to deploy within these gaps as few as possible. The results of the study showed that the imposed algorithm is effective in all of the above, and we focus in this research on adaptively determining the transmission energy levels of the nodes and reducing the number of deployed nodes that make the network work effectively and improving the quality of deployment by deploying additional nodes within the Reigon of Interest. The results showed achieving the least number of deployed nodes at the lowest transmission power level and achieving high interconnection between nodes. An overall energy consumption improvement of 31.25% was achieved.