Abstract

AbstractExtraordinary class of mobile ad-hoc network and delay tolerant network (DTN) is known as opportunistic network, which includes diverse range of applications in today’s scenario. This network works in many applications like animal evacuee tracking, intelligent transportation, connectivity in rural area, and mobile social networking (MSN), etc. Routing in opportunistic network is a crucial task due to its onerous characteristics. As many routing protocols has been already proposed in earlier, each protocol have their own advantages and disadvantages. In this paper, novel routing protocol named QRP is proposed to enhance the best selection of relay node in the opportunistic network. Reinforcement-based deep-q-learning approach is used to find the Q-value of the node. This Q-value of the node decides that either the packet is disseminated in the network.KeywordsMachine learningNature-inspired algorithmOptimizationOpportunistic networkRouting protocolONE toolQ-learning

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