Abstract
It is very difficult to find feasible QoS (Quality of service) routes in the mobile ad hoc networks (MANETs), because of the nature constrains of it, such as dynamic network topology, wireless communication link and limited process capability of nodes. In order to reduce average cost in flooding path discovery scheme of the traditional MANETs routing protocols and increase the probability of success in finding QoS feasible paths and we proposed a heuristic and distributed route discovery method named RLGAMAN that supports QoS requirement for MANETs in this study. This method integrates a distributed route discovery scheme with a reinforcement learning (RL) method that only utilizes the local information for the dynamic network environment; and the route expand scheme based on genetic algorithms (GA) method to find more new feasible paths and avoid the problem of local optimize. We investigate the performance of the RLGAMAN by simulation experiment bed in NS2. Compared with traditional method, the experiment results showed the network performance is improved obviously and RLGAMAN is efficient and effective.
Highlights
Mobile ad hoc networks (MANETs) is a kind of new technique of wireless communication for mobile hosts
We propose a novel adaptive Quality of Service (QoS) route discovery method for MANETs, based on Reinforcement learning (RL) and Genetic Algorithm (GA), named RLGAMAN, which integrate two key parts
We introduce a Qos route explore and discovery scheme based on RL to reduce the flooding in MANET
Summary
Mobile ad hoc networks (MANETs) is a kind of new technique of wireless communication for mobile hosts. Existing studies show that table-driven QoS protocols if e ∈ E , define the metric function: Bandwidth function: B(e) : E → R + , Delay function: D(e) : E → R+ The QoS parameters of path l can be represented as: request globe network state information; and ondemand QoS protocols need initiates a route discovery based on flooding, which are not fit the dynamic and capability constrain in MANETs. distributed routing or source routing. Route table keep the average round-trip delay, available bandwidth and other QoS metrics to every known destination through every neighbor nodes This information is required to operate the learning algorithm to decide the output of the packets. In the approach we discuss in this study, a GA will run at each source node, to generate and select paths for the packets that carry payload based on the QoS goal. The simulations compared a network with ordinary QoS route method based on on-demand route discovery scheme such as AODV with our RLGAMAN route discovery scheme
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