Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermittent connectivity. However, routing performance suffers due to dynamic topology changes, unpredictable contact patterns, and resource constraints including limited energy and buffer capacity. These challenges compromise delivery reliability, increase latency, and reduce node longevity in highly dynamic environments. This paper proposes Cluster-based Routing using Deep Reinforcement Learning (CR-DRL), an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function. CR-DRL enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency. Simulation results demonstrate significant improvements over state-of-the-art baselines. CR-DRL extends node lifetimes by up to 21%, overall energy use is reduced by 17%, and nodes remain active for 15% longer. Communication performance also improves, with up to 10% higher delivery ratio, 28.5% lower delay, 7% higher throughput, and data requiring 30% fewer transmission steps across the network.

翻译：机会网络（OppNets）采用存储-携带-转发（SCF）范式在间歇性连接中维持通信。然而，由于动态拓扑变化、不可预测的接触模式以及有限的能量和缓冲区容量等资源约束，路由性能受到影响。这些挑战在高动态环境中会降低投递可靠性、增加延迟并缩短节点寿命。本文提出基于深度强化学习的分簇路由（CR-DRL），这是一种将Actor-Critic学习框架与启发式函数相结合的自适应路由方法。CR-DRL能够实现实时最优中继选择和动态簇重叠调整，以在维持连接的同时最小化冗余传输并提升路由效率。仿真结果表明，与现有先进基线方法相比，CR-DRL取得了显著改进：节点寿命最高延长21%，整体能耗降低17%，节点活跃时间增加15%。通信性能亦得到提升，投递率最高提高10%，延迟降低28.5%，吞吐量提升7%，且数据在全网传输所需步骤减少30%。