EA-DBSEP-IoUT: An Edge-Assisted Depth-Based Stable Election Protocol for Energy-Efficient Routing in IoUT

The Internet of Underwater Things (IoUT) is a rapidly evolving field with applications in environmental monitoring, disaster prevention, and military surveillance. However, underwater sensor networks face unique challenges, including high propagation delay, energy constraints, and dynamic topology changes. Traditional routing protocols such as the Stable Election Protocol for IoUT (SEP-IoUT) and Depth-Based Routing (DBR) suffer from inefficient cluster head (CH) selection, leading to rapid energy depletion. This paper proposes Edge-Assisted Depth-Based Stable Election Protocol for IoUT (EA-DBSEP-IoUT), an energy-efficient, edge-assisted adaptive clustering scheme. The proposed method integrates Reinforcement Learning (RL) for optimal CH selection, data aggregation at edge nodes, and multi-hop routing to reduce communication overhead. Simulation results demonstrate that EA-DBSEP-IoUT outperforms SEP-IoUT, Depth-Based Unequal Clustering (DBUC), and Energy-Efficient Depth-Based Routing (EEDBR) in terms of network lifetime, energy efficiency, and stability period.