Social mobility and geo-context aware macroscopic routing scheme for mobile opportunistic network

Mobile opportunistic networks (MONs) are deliberated as important aspect to proliferate the wireless communications. These pose several challenges such network lifetime, storage capacity, and forwarding capacity. End-to-End routing schemes considered promising technique solution deal with these issues. In this domain, has gained huge attention because it follows broadcasting nature of communication focus on selection relay node for packet transmission ensure better quality service (QoS) energy efficiency. This work focuses optimizing next hop process introduced reinforcement learning approach which considers distance, link connectivity assign reward different actions identify suitable node. Moreover, geo-context social behaviour based models used increase reliability selection. Similarly, model profiling, connectivity, interaction The outcome proposed is compared existing approaches prophet, spray wait, epidemic in terms delivery, overhead. relative study shows that achieves average delivery 47.22% minimizes