Deep Reinforcement Learning Enhanced Geographic and Cooperative Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
Keywords:
UWSN, Routing, Sensors, DBR, DRL, GCORPAbstract
In the rapidly evolving field of Underwater Wireless Sensor Networks (UWSNs), the development of efficient and robust routing protocols poses a significant challenge due to the unique characteristics of the underwater environment. This paper proposes an innovative adaptation of the Geographic and Cooperative Opportunistic Routing Protocol (GCORP), enhanced by Deep Reinforcement Learning (DRL) to improve routing efficiency, energy utilization, and reliability in UWSNs. This novel approach, named DRRP-UWSN, is a radical move from traditional routing protocols, utilizing a Deep Q-Network (DQN) to enable nodes to learn and adaptively select the optimal next-hop node for data transmission. The algorithm considers several key network parameters, such as distance to destination, energy level, and link quality, leveraging them to refine the routing decisions. Our proposed DRRP-UWSN is evaluated and compared with established protocols such as Depth-Based Routing (DBR), the original GCORP, and Balanced Routing Protocol Based on Machine Learning (BRP-ML). The results demonstrate a substantial improvement in network performance, indicating the considerable potential of integrating DRL into routing protocols for UWSNs.
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