EMOR: Energy-Efficient Mixture Opportunistic Routing Based on Reinforcement Learning for Lunar Surface Ad-Hoc Networks

Abstract

The lunar surface ad-hoc network is a critical component of the international lunar research station and an extension of the earth-moon communication networks. Its high reliability and low delay are essential for ensuring the safety of the lunar station and improving the efficiency of node collaboration. However, due to the lack of large-scale grid infrastructures, the network must operate autonomously for long periods under strong energy constraints. We propose EMOR, a cross-layer routing protocol, which aims to achieve sustainable high reliability and low latency while balancing energy recovery and consumption. EMOR improves reliability through the “parallel” forwarding feature of opportunistic routing and reduces delay through a mixture of table-based and timer-based routing mechanisms. Moreover, EMOR uses reinforcement learning to analyze the environment and calculate the weights of energy and progress to guide the emphasis on multi-metrics routing. To balance energy consumption and recovery, EMOR introduces a dynamic duty cycle in the MAC layer. Compared to table-based routing and the latest opportunistic routing, EMOR maintains the optimal end-to-end delay in the order of 1ms while improving the packet delivery ratio 6% to 21% higher than other protocols. Moreover, the network lifetime using EMOR is extended by 75.5% to 242%.