Journal of Trends in Computer Science and Smart Technology · 2019 · 74 citations · 12 references
ComputingComputer ScienceAlgorithmsComputer NetworksMachine LearningMobile Ad Hoc NetworksMobile NetworksReinforcement LearningRoutingRouting AlgorithmsWireless NetworksAd Hoc NetworkingAd Hoc NetworksEnergy ConsumptionNetwork RoutingPacket FramingRoute DiscoveryTransmission DelayWireless Communication Systems
Wireless communication advances have propelled mobile ad hoc networks, which face significant routing challenges due to their dynamic, infrastructure‑less nature. The study proposes a reinforcement‑learning based routing method aimed at reducing energy consumption, transmission delay, and improving packet delivery ratio to enhance throughput. The method’s performance was evaluated in NS‑II by measuring energy usage, transmission delay, and packet delivery ratio.
The rapid advances in wireless communication technology has led to an extraordinary progress in the adhoc type of networking. The mobile adhoc networks being a subtype of the adhoc network almost poses the same characteristics of the adhoc network, presenting multiple challenges in framing a route for the transmission of the information from the source to the destination. So the paper proposes a routing method developed based on the reinforcement learning, exploiting the node information’s to establish a route that is short and stable. The proposed method scopes to minimize the energy consumption, transmission delay, and improve the delivery ratio of the packets, enhancing the throughput. The efficiency of the proposed method is determined by validating its performance in the network simulator-II, in terms of the energy consumption, delay in the transmission and the packet delivery ratio.
12