text: Achieving optimal transmission throughput in data networks in a multi-hop wireless networks is fundamental but hard problem. The situation is aggravated when nodes are mobile. Further, multi-rate system make the analysis of throughput more complicated. In mobile scenario, link may break or be created as nodes are moving within communication range. Route Discovery' which is to find the optimal route and transmission schedule is an important issue. Route discovery entails some cost; so one would not like to initiate discovery too often. On the other hand, not discovering reasonably often entails the risk of being stuck with a suboptimal route and or schedule, which hurts end-to-end throughput. The implementation of the routing decision problem in one dimensional mobile ad hoc network as Markov decision process problem is already is discussed in the paper [1]. A heuristic based on threshold policy is discussed in the same paper without giving a way to find the threshold. In this paper, we suggested a rule for setting the threshold, given the parameters of the system. We also point out that our results remain valid in a slightly different mobility model; this model is a first step towards an open' network in which existing relay nodes can leave and or new relay nodes can join the network.
  @cite_3: The quality of service (QoS) routing has been receiving increasingly intensive attention in the mobile ad hoc networks (MANETs) fields, but it is difficult to solve the problem for the nature of MANETs such as performance constraints and dynamic network topology. In order to increase the probability of success in finding QoS feasible paths and reduce average cost in flooding path discovery scheme of the traditional MANETs routing protocols, we proposed a heuristic and distributed route discovery method named RLGAMAN that supports QoS requirement for MANETs in this paper. This method integrates the 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 avoid the problem of stagnation route. We investigate the performance of the RLGAMAN by simulation experiment bed in NS2. Compared with the traditional method, the experiment results showed the network performance is improved obviously, and RLGAMAN is efficient and effective
  @cite_12: 
  @cite_7: The Ad Hoc On-Demand Distance Vector (AODV) protocol is an on-demand protocol specialized for mobile ad hoc network. Because of node's mobility and limited transmission range, the routes created by original AODV become invalid frequently leading to larger control overhead. In this paper, we propose a new scheme to improve AODV protocol by the concept of reliable distance. The reliable distance, which is always smaller than transmission range, is depended on the node's velocity and direction information attained from Global Positioning System (GPS). By the new mechanism, the routes are more reliable. Performance comparison of optimized AODV with conventional AODV by NS-2 simulator in various conditions shows the performance improvement.
summary: Many authors have discussed route discovery process @cite_7 , @cite_3 , @cite_12 but neither suggested when to initiate route discovery as their case is related to static case nor they have suggested how frequently to initiate the discovery process, in case of mobile network. Most of them suggested initiation of discovery only when a link, in the existing route, is disrupted i.e., in case of route break. @cite_7 , the authors proposed a modified AODV which uses the concept of reliable distance that change dynamically. Peng @cite_3 suggested distributed route discovery method that uses reinforcement learning. @cite_12 , the authors have used fuzzy controller in every node. In their paper, the destination evaluates performance of all those routes and arranges it in order of preference, when route-request packet reaches its destination.