Research Of Multi-constrained Routing Protocol With QoS Guarantee Based On Fuzzy Logic

Nowadays most of routing protocols use a single constraint parameter to select route, however, the route selected based on a single constraint is not necessarily the best, because the changes of the network topology are related to a number of factors, so only considering any of these parameters can not meet the traffic's QoS requirements when making routing decisions. In addition, some characters of Ad Hoc networks make the gathering and mainernance of network state information very difficult, resulting in that QoS guarantee mechanisms usually works under inaccurate state information in Ad Hoc networks. At the same time multi-constrained routing selection is an NP-complete problem, which is difficult to use precise mathematical model to describe, and therefore fuzzy logic system is more suitable for routing decisions.This paper proposes a multi-constrained QoS routing protocol based on fuzzy logic, which considers a number of restrictive conditions, makes routing decision through fuzzy logic system and ensures that the selected route could meet the different QoS requirements of different traffic. The protocol has the functions of service-aware, route inform mechanism and speed adjustment of sending packet at source node.Multi-constrained outing protocol with QoS guarantee based on fuzzy logic mainly includes two areas of design.1. Fuzzy logic system designA typical fuzzy logic system includes three steps: fuzzification, fuzzy inference and defuzzification. (1)fuzzification, finding the related membership degree for input parameters according to the membership functions; (2)fuzzy inference, fuzzy reasoning in accordance with the fuzzy rules, the result of fuzzy inference is still fuzzy; (3)defuzzification, turning the fuzzy sets from fuzzy inference into crisp values. In this paper, the fuzzy logic system designed is consisted of three fuzzy logic subsystems. Subsystems 1 (FLS1) is responsible for the route stability, the input parameters are the mobile speed, number of hops, and the output parameter is a fuzzy set S about route stability. Type of traffic is examined before feeding the output result of FLS1 to the next fuzzy logic system. Real-time flow is sent to FLS2 which is responsible for the calculation of the end-to-end delay of each route, the input parameters are route stability and bandwidth, the output parameters is the end-to-end delay. According to the end-to-end delay, the speed of sending packets is adjusted and the route with the lowest delay is chosen. While if the flow is non-real-time flow, it will be sent to FLS3. The function of FLS3 is calculating the packet loss rate of each route. The input parameters are packet buffer occupancy rate and route stability, the output is packet loss rate. According to the packet loss rate, the speed of sending packets is adjusted and the route with the lowest packet loss rate is selected. The difficulty of fuzzy logic system design is the choice of membership function, the design of fuzzy inference and the selection of defuzzification method. In this paper, the triangle membership function is selected; the choice of the defuzzification method is weighted mean method; the inference rules are based on massive simulation tests.2. Routing mechanism designFuzzy logic system needs four input parameters (mobile speed, number of hops, bandwidth and packet buffer occupancy rate) which are gathered in the process of route discovery. Hence the process of route discovery of DSR protocol needs to be improved, including the design of route packets, data packets and route cache. It means thatis the corresponding bandwidth list, the list of the length of packet queue and buffer capacity list should be carried by related route packets and data packets and the routing cache is updated if possible. At the same time, number of hops, route bandwidth and the packet occupancy rate of the whole path are caculated (the mobile speed of nodes in the whole network is the same). Before sending data packets, each node first feeds the four parameters into fuzzy logic system, and then makes route decision based on fuzzy logic system. Fuzzy logic system is installed in the source node, and each time fuzzy logic calculation should be excuted when there is a data packet need to be sent.In the on-demand source routing protocol, when the state of network changes the information of route table can not be updated in time. Therefore, for real-time traffic, the delay of some route which is used to send data may changes. When the delay is greater than the delay of sub-optimal route, if the data of this session is still be sent through this route, the QoS requirements may not be met. For solving this problem, this paper proposes a new route inform mechanism, which is responsible for sending a RINF (Route Inform Packet) to inform source node that the route delay has changed. So the sub-optimal route is used to send packets in order to ensure real-time traffic is always be sent through the route with the lowest delay.Because of the mobility of nodes, the dynamic network topology and some other relevant factors, end-to-end delay and the packet loss rate changes. When network congestion occurs, delay and packet loss rate will be increased. In order to relieve network congestion, this paper adaptively ajusts the speed of sending packets at source node by comparing the current speed and the initial speed, making network always be in a good condition.This paper uses OPNET network simulation software to build simulation models of Ad Hoc networks and study the performance of Fuzzy DSR protocol under different network configurations scenes. The purpose of an experiment 1 is to study the influence of Fuzzy DSR protocol on the Ad Hoc network performance, which includes the real-time traffic performance indicators end-to-end delay and delay variation, the non-real-time traffic performance indicators packet loss rate, as well as the related performance indicators of the network layer routing protocol. The purpose of experiment 2, experiment 3 and experiment 4 is to the impact o f Fuzzy DSR protocol on the performance indicators of real-time traffic and non-real-time traffic under the conditions with different mobile speeds, different network scales and different network payload compared with DSR protocol. The simulation results show that the performance of network using Fuzzy DSR protocol in the condition of high-speed, large-scale and high network payload is better than the performance of network using DSR protocol, while the advantages of Fuzzy DSR protocol in the condition of low-speed, small-scale and low network payload is not obvious.In conclusion, this paper mainly studies the QoS guarantee in Ad Hoc networks security issues. Aiming at the disadvantages of single-constrained routing protocol with QoS guarantee, this paper proposes a multi-constrained routing protocol with QoS guarantee based on fuzzy logic considering the imprecise character of Ad Hoc network parameters. This paper discusses a fuzzy logic system design process and routing mechanism design process in detail, and does lots of simulation experiments through OPNET. Simulation results show that the improved protocol has strong capability of QoS guarantee. Currently there are still many imperfections in the system, the author will be further her study in future work.