Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning

Sensor nodes, typically small and low-power devices, are components of wireless sensor networks (WSNs). Each node monitors its surroundings for relevant environmental changes and sends all detected events to the data collector for analysis. If the sensor nodes are not placed correctly, there may be areas that are not within the detection zone of any sensor node. Coverage holes in WSNs are usually caused by random deployment and node failure. Energy holes and dead nodes are the main problems caused by detection and recovery of coverage holes in WSNs. The size of coverage holes increases the time complexity and power of recent protocols. However, there is a high computational complexity associated with distributed methods proposed in recent years to solve the coverage hole detection problem. In this paper, we propose optimal cluster-based node position estimation and coverage hole detection in WSNs using a hybrid deep learning approach. First, a modified Lyapunov optimization (MLO) algorithm to compute the node position is presented, which ensures edge nodes in the network. Next, we design optimal clustering technique by using improved sand cat swarm optimization (ISCSO) algorithm to formulate efficient balanced clusters which computes coverage hole area in the network. Afterward, we develop a hybrid deep reinforcement learning (Hyb-DRL) technique for hole shape detection and hole size judgment within clusters, among clusters and along edges. The results show that the proposed approach achieves significant improvements compared to existing benchmark approaches. Specifically, the average energy consumption of CG-DCHD approach is 43.835%, 32.674% and 26.164% lower for node density, hole density and simulation rounds, respectively. The hole detection time is 18.4%, 16.802% and 15.462% lower, while the coverage is 16.885%, 14.977% and 12.219% higher for node density, hole density and simulation rounds, respectively. Additionally, the network lifetime of CG-DCHD approach is 15.58%, 17.702% and 20.492% higher, while the control packet overhead is 0.83%, 1.907% and 1.466% lower for node density, hole density and simulation rounds, respectively.