Node Localization in 3D WSN Using Optimized Deep Learning Mechanism

ABSTRACT Many mobile and sensor nodes comprised wireless sensor networks (WSN). Yet, it is quite challenging to locate these sensor and mobile nodes. Because of the time‐varying movements, analysis of the current positions of sensor nodes in WSN is quite challenging. Because of locating all known sources in unknown nodes, the typical localization approaches are used to find the position of these nodes, producing a lot of inaccuracy when forecasting the distance between the source and unknown nodes. Also, it is very expensive to use Global Positioning System (GPS) technology for node detection. Although numerous localization procedures for WSNs in a three‐dimensional topology have been proposed, it is still important to create and refine new localization algorithms to further increase the accuracy of the node positioning method. In this research work, an advanced heuristic algorithm and a deep learning technique are developed for localizing the unknown nodes in a three‐dimensional wireless sensor network (3D‐WSN). Initially, the distance between the unknown node as well as the anchor node is evaluated using efficient hybrid deep learning techniques named bidirectional long short‐term memory (Bi‐LSTM) and gated recurrent unit (GRU). Hybrid position of mine blast and chameleon swarm (HP‐MBCS) is developed for tuning the parameters in deep learning techniques. An objective function of minimizing the average localization error (ALE) on node localization is obtained by optimally selecting the position of unknown nodes with the support of computed distance from the developed Bi‐LSTM‐GRU technique. The experimental simulation is carried out between the proposed and traditional models to show that the proposed model is efficient in minimizing localization error. The resultant outcome shows that the MEP value of the proposed HP‐MBCS‐Bi‐LSTM‐GRU model is 24.191, which is better than the other existing algorithms like EHO, EOO, MBO, and CSO, respectively. Thus, it was confirmed that the proposed Bi‐LSTM‐GRU not only improves the precision and effectiveness of node localization but also enhances the overall energy efficiency.