An Efficient Epidemic Routing Protocol with Reinforcement Learning Algorithm in Opportunistic Networks

In many African countries, mobile payments are vital for financial transactions. However, enabling peer-to-peer payments is challenging due to limited network infrastructure and resource-constrained devices. Companies like Ejara are working to bring accessible financial services to underserved communities. However, existing routing protocols for Opportunistic Networks (OppNets), like traditional epidemic routing, create high routing overhead and latency from indiscriminate packet flooding, putting significant pressure on network and device resources. This study addresses these issues by introducing an optimized epidemic routing protocol with Reinforcement Learning algorithms integration to enhance packet forwarding in OppNets. The protocol dynamically adapts forwarding decisions based on delivery probability, latency, and resource constraints. Emulation results demonstrate that this approach significantly reduces routing overhead and latency while improving packet delivery reliability. This solution has meaningful implications for enabling efficient mobile payments and peer-to-peer interactions, especially in resource-constrained environments with intermittent connectivity, supporting broader accessibility to financial services.