Efficacious implementation of deep Q-routing in opportunistic network

Opportunistic network (Oppnet) is consisting of wireless nodes, which follows the Bluetooth transmission range between nodes. These kinds of networks are capable to handle long delay during transmission of messages. Dynamic network topology and unstable connection between nodes makes routing difficult in Oppnet. Overall to provide efficient and reliable transmission of messages are such a crucial task in Oppnet. To resolve these issues; this article introduces reinforcement learning based on Deep Q-learning technique. In this approach, Q-value is used to find best current intermediate node for forwarding the packet from source Oppnet node to destination Oppnet node. This protocol is simulated on ONE tool and it is found that Q-Routing in Oppnet works more efficiently as compared to conventional protocols like Epidemic, Maximum Probability (Max-Prob), and Probability Routing Protocol using History of Encounters and Transitivity (PRoPHET). Proposed protocol utilized; 71% lesser energy consumption, 66% reduced overhead ratio, 61% lesser end-to-end delay, 51% enhanced throughput, and 10% higher delivery ratio as compared to PRoPHET protocol when variation in number of nodes.