Hybrid Deep Neural Network and Particle Swarm Optimization for Energy-Efficient Node Localization in Wireless Sensor Networks

Accurate node localization in wireless sensor networks (WSNs) is challenging under variable signal propagation and strict energy constraints. This paper presents a hybrid localization framework that combines a deep neural network (DNN) with particle swarm optimization (PSO) to improve accuracy while reducing energy consumption. The DNN learns the non-linear mapping from received signal strength indicator (RSSI) measurements to node coordinates, mitigating propagation effects. PSO jointly optimizes key DNN hyperparameters and selects a minimal subset of anchor nodes that preserve localization performance, thereby lowering communication overhead. Simulation results on 200-node networks show that the proposed DNN–PSO achieves a mean localization error (MLE) of 0.87 m, outperforming a standard DNN (1.32 m) and classical multilateration (3.84 m). The optimized anchor selection reduces per-cycle energy consumption by 23% (239 mJ to 184 mJ) while maintaining sub-meter accuracy. Performance remains stable across diverse propagation conditions and scales well with increasing network size. These results indicate that the proposed approach provides an effective accuracy–energy trade-off for resource-constrained IoT/WSN deployments requiring reliable localization.