Adjacent Cell Score for Mobile Sink Placement Using Deep Q Net-Based Energy Prediction in WSN

In recent years, Wireless Sensor Networks (WSNs) have been widely adopted in healthcare, manufacturing, military, environmental monitoring, and other applications. Because sensor nodes have limited energy, multi-hop communication can create energy-hole regions around the sink. This paper proposes a mobile-sink placement framework that combines Voronoi partitioning, Deep Embedded Clustering (DEC), an adjacency-based cell score, and a Deep Q-Network (DQN) for energy prediction. The network is first partitioned into Voronoi cells, and DEC is used consistently for Cluster Head (CH) selection. For each candidate sink cell, the adjacency-based cell score considers distance, fairness, and predicted residual energy. DQN is used to predict residual energy from a defined WSN state representation, while the DQN agent is trained at the Base Station (BS); sensor nodes are not required to execute the neural network. The reported simulations consider 100, 200, 300, and 400 nodes over 2000 rounds. In the comparative table, the proposed method reports a minimum distance of 21 m for the 400-node case, residual energy of 0.746 J, normalized fairness of 95, network lifetime of 89 s, and normalized throughput of 96 for the corresponding reported case. The study focuses on placement optimization and does not model mobile-sink travel energy, speed, or collection latency; these are identified explicitly as limitations and future extensions.