EQ-STAR: Energy-Efficient High-Quality Routing Based on Spatio-Temporal Attention and Reinforcement Learning
Qi Sun, Winston K.G. Seah, Gang Xu · 2025
Opportunistic networks can support message delivery flexibly without infrastructure nor end-to-end connectivity, making them suitable for various scenarios, such as, crowd sensing, Internet of Things (IoT), vehicular ad hoc networks, etc. However, the performance of opportunistic networks, especially the delivery rate and network's operational time, is adversely affected by the uncertainty of encounter probabilities and the limited energy of IoT nodes. In this paper, we propose EQ-STAR, an optimal reachable path approach that combines encounter probabilities and energy to enhance network performance. First, the DySAT-pro model provides more accurate encounter probabilities based on the network's periodic patterns. Second, the Energy-Efficient high-Quality Markov Decision Process (EQMDP) model balances energy consumption and encounter probabilities to optimize the high-quality reachable path between the source and destination nodes. Finally, the next-hop relay node is selected based on the reachable path determined by EQMDP. Extensive experimental results show that our approach outperforms classicial and state-of-the-art baselines in both realworld and synthetic datasets.