An Enhanced Localization Approach for Energy Conservation in Wireless Sensor Network with Q Deep Learning Algorithm

Wireless Sensor Networks (WSN) have distributed a collection of tiny sensor nodes deployed randomly in the given symmetry environment to sense natural phenomena. The sensed data are disseminated symmetrically to the control station using multi-hop communication. In WSN, the energy conservation during node coverage plays a major role in detecting node failure and providing efficient and symmetrical data transmission to the nodes of WSN. Using the cluster method and efficient localization techniques, the nodes are grouped and the precise location of the nodes is identified to establish the connection with the nearby nodes in the case of node failure. The location accuracy is achieved using the localization estimation of the anchor nodes and the nearest hop node distance estimation using the received signal strength measurement. The node optimization can be performed efficiently by the accurate estimation of the localization of the node. To optimize the node coverage and provide energy efficient and symmetrical localization among the nodes, in this paper, a cluster-based routing protocol and a novel bio-inspired algorithm, namely, Modified Bat for Node Optimization (MB-NO), to localize and optimize the unknown nodes along with the reinforcement-based Q learning algorithm is proposed with the motive of increasing the accuracy estimation between anchor nodes and the other neighbor nodes, with the objective function to optimize and improve the nodes' coverage among the network's nodes in order to increase the nodes' localization accuracy. The distance metrics between the anchor nodes and other neighbor nodes have an estimated symmetry with three node positions, namely C-shape, S-shape and H-shape, using the Q learning algorithm. The proposed algorithm is implemented using the NS3 simulator. The simulation results show that the accuracy and precision of the proposed algorithm are achieved at 98% in the node coverage optimization with reduced Mean Localization Error (MLE) and computational process time compared with other bio-inspired algorithms, such as Artificial Bee Colony optimization and Genetic Algorithms.