Volume-4, Issue-3, June-2014, ISSN No.: 2250-0758 International Journal of Engineering and Management Research Available at: www.ijemr.net Page Number: 80-86 Dynamic Source Routing Protocol for Ad Hoc Networks Using the Concept Intelligent Agent Fuzzy Logic Rajni Jain 1, Dr. Sohan Garg 2 1 Research Scholar, Venkteshwara University-Gajraula Amroha, Uttar Pradesh, INDIA 2 Director, SCRIET-CCS University Campus Meerut, Uttar Pradesh, INDIA ABSTRACT This paper presents a Dynamic Source Routing Protocol using Self Healing and Optimizing Routing Technique based on fuzzy logic concepts. The paths generated by conventional dynamical source routing protocol deviate far from the optimal paths because of the lack of knowledge about the global topology and the mobility of nodes. Routing optimality affects the network performance, especially when the load is high. Longer route consumes more bandwidth, power and is more prone to disconnections. Self Healing and Optimizing Routing Technique (SHORT) is a technique that monitors the route and tries to shorten it, if a short-cut is available. The proposed fuzzy logic method is evaluated and compared with conventional method using GloMoSim. Key words: Mobile Ad hoc Network, SHORT, Neighbor table, Short cut paths, Time- stamp I. INTRODUCTION A Mobile Ad hoc Networks (MANETs) are a collection of mobile nodes that are dynamically and arbitrarily located in such a manner that the interconnections between the nodes are capable of changing on a continual basis. On Demand Routing Protocol provides a scalable and cost-effective solution for packet routing in wireless ad hoc networks. In order to facilitate communication within the network, a routing protocol is used to discover routes between nodes. The primary goal of such an ad hoc network routing protocol is to provide correct and efficient route establishment between pair of nodes so that messages may be delivered in time. MANETs are dynamic multi-hop networks consisting of a set of mobile nodes that intercommunicate on shared wireless channels. Each node can operate as a host as well as a router. Due to the limited transmission range of the nodes, the distant nodes intercommunicate through multi-hop paths. The main features of MANETs are ease of deployment and absence of the need for any infrastructure and it makes ideal network for many applications. Examples of such applications are disaster relief, conferencing, interactive information sharing, file transfer and warfare situations, where setting up of infrastructure is very difficult. Challenges to MANETs include changing network topology, a limited transmission range, low availability of bandwidth due to wireless environment and consumption of higher control packets for establishing and maintaining. Overhead for establishing a route generated by the route search procedure decreases the overall network performance and increases power consumption of mobile devices [1]. Routing protocol designed for the MANETs should take into account the said above challenges and should make optimal attempt to find a route to the destination. MANET routing protocols are mainly clubbed into two techniques viz., table-driven and source - routing. Table-driven routing protocol requirements are periodic advertisement of the changes in the network and its global dissemination of connectivity because of which it is unsuitable for large - sized networks. Protocols based on table-driven technique are Destination Sequence Distance Vector (DSDV) routing [2], Clusterhead Gateway Switch Routing (CGSR) [3], and Wireless Routing Protocol (WRP) [4]. Source - initiated protocols are efficient for routing in large - sized ad hoc networks because they maintain the routes that are currently needed, initiating a path discovery process whenever a route is needed for the message transfer. Popular protocols based on on-demand scheme are ad-hoc On demand Distance Vector (AODV) [5], Dynamic Source Routing (DSR) [6], Temporally Ordered Routing Algorithm (TORA) [7], Signal Stability based Adaptive (SSA) [8] routing and Associativity Based Routing (ABR) [9]. The shape of the routing paths may change significantly because of the mobility of nodes in ad-hoc networks, because of which the path established is not an optimal one. The change in shape can be exploited in deriving better routing paths, if we can avoid any significant overheads (avoiding extra route discovery process).the problem is dealt with in the next section. The paths formed initially are longer than the shortest paths because of mobility. As the data packets pass along these paths, longer end-to-end delay, shorter life span and extra bandwidth is consumed. The 80
mechanism called Self Healing and Optimizing Routing Technique (SHORT) [10], which can optimize the route length and result in significant performance gain over the underlying protocols. The SHORT monitors the path length and shortens it, if possible. The shorter paths reduce latency and enhance reliability. In this paper, we have incorporated the fuzzy concepts having SHORT as the underlying technique. The fuzzy concepts were simulated using MATLAB and the results of the fuzzy concepts were applied to the GloMoSim. The performance of the proposed method has been evaluated. It has been observed that it improves the protocol in the aspects of delivery rate, control packets, dropped packets and end-to-end delay. The sequence of the paper is as follows: The problem is stated in Section 2 and Section 3 gives a description of the proposed method. Section 4 evaluates the performance of the proposed method and conclusions are made in Section 5. The primary goal is to discover short-cut routing paths as and when feasible. The basic scenarios of the short-cut discovery process are shown in Fig. 2. In Fig. 2(a), the path A-B-C can be reduced to A - C as C is within the transmission range of A. This short-cut path formation is termed (2, 1) reduction. In general (n, 1) reduction implies the reduction of n hops into only one hop. Figure 2(b) shows that the routing path A-B-C-D can be shortened to A-E-D, since E is in the transmission range of A and D is in the range of E. This short-cut path formation is termed (3, 2) reduction. This (n, 2) reduction implies that n hops along the path can be reduced to only two hops. In general terms, (n, k) reduction implies that n hops along the path can be reduced to k hops, where k<n. The higher the difference between the values of n and k the better will be the performance of SHORT. II. PROBLEM DESCRIPTION Consider a routing path from the source node A to a destination node I as shown in Fig.1 (a). The initial path is determined using the path discovery process, in which the distance between the source and destination is the shortest or very close to it. A packet takes eight hops while following route from A to I. In the course of time, the mobility of the nodes may make the shape of the routing path similar to the one shown in Fig.1 (b) while retaining the connectivity. In this new shape, J is in the transmission range of A and E is in the transmission range of J. Similarly H is in the transmission range of F. However, because of the usage of route caches and the validity of the existing routing information, the routing table entries are not updated. Using the routing paths shown in Fig.1 (b), a packet still takes eight hops to reach from A to H and needs only five hops by dynamically modifying the entries of the routing tables as in Fig. 1(c). Fig. 2 Basic Short-cut discovery (Above) The mobility and pause-time of the MANETs impose a strong restriction on the data structures that are going to use SHORT. The data structures should have a dynamic nature so that they are able to change according to mobility of the nodes to get an optimal solution. III. FUZZY SHORT Fig. 1 An example of the changes in routing paths (Above) The SHORT is a general technique that should work with any underlying routing protocol. DSR will make an optimal decision at the route setup time due to its nature but that does not continue as the mobility of the node changes. The packets flow along the path established during route setup. The SHORT will monitor the path and try to identify the short-cut to the route and shorten it according to the up-to-date topology. It should be noted that the SHORT algorithm does not react to every little change in the topology or transient conditions. In source initiated routing techniques, such as DSR, the entire path (i.e., the address of all the nodes that the packet will hop through) is encoded in the packet header. SHORT can be employed on top of DSR to facilitate path formation with (n,2) and higher short-cuts in addition to the (n,1) shortcuts. Each node is in a mixed and indiscriminate listening mode. Each node maintains a current neighbor table from 81
the tapped packets. When node A taps a packet sent out by node B, which is the intended next hop receiver, node A checks the route given in the packet to see if it is in the upcoming part of the list of intermediate nodes on the route. If A is on the list and it is more than 2 hops away from B on the routing path, A finds a short-cut. A then sends a gratuitous RREP message to the source node of the route informing that A to B part of the route could be reduced to link A-B. RREP message travels back along the shortened path in the reverse direction. A. Implementation techniques to be corrected Without the curbing, short-cut messages interfere with the routing tables, causing much more route updates than original protocols. The implementation of the algorithm requires two data structures. They are neighbour table and time-stamp. Time-stamp is a timer associated with each entry after which the entry becomes invalid. In [10-11], they have assumed a temporal locality of the nodes but in any case this will not work. This temporal locality may work on the uniform node placement but when the node is placed in random the performance will degrade. We have tried to tackle this problem by varying the time-stamp according to the mobility and the pausetime of the nodes. This has been taken careof by the Fuzzy inference system. B. Fuzzy Logic Fuzzy systems are used to approximate functions. The fuzzy can be used to model any continuous function or system. Fig. 3 shows the generalized block diagram of fuzzy system. The advantages of fuzzy logic conceptually easy to understand flexible tolerant of imprecise data can model nonlinear functions of arbitrary complexity can be built on top of the experience of experts can be blended with conventional control techniques based on natural language The quality of fuzzy approximation depends on the quality of the rules. The result always approximates some unknown non linear function that can change in time. Fuzzy systems theory or fuzzy logic is a linguistic theory that models how we reason with vague rules of thumb and commonsense. The basic unit of fuzzy function approximation is ifthen rules. A fuzzy system is a set of if- then rules that maps input to output. Fig. 3 Generalized Fuzzy System The steps involved in the Fuzzy inference system design are as follows. Step 1: Fuzzy Inputs: This step will obtain inputs and determine the degree to which they belong to each of the appropriate fuzzy sets via membership functions. Fuzzification of the input amounts to either a table lookup or a function evaluation. Step 2: Apply Fuzzy Operator: This step determines the degree to which each part of the antecedent has been satisfied for each rule. If the antecedent of a given rule has more than one part, the fuzzy operator is applied to obtain one number that represents the result of the antecedent for that rule. This number will then be applied to the output function. The input to the fuzzy operator is two or more membership values from fuzzified input variables. The output is a single truth value. The method used may be either AND or OR operation. Step 3: Apply Implication Method: Before applying implication proper weights are assigned to each rule. The input for the implication process is a single number given by the antecedent, and the output is a fuzzy set. Step 4: Aggregate all outputs: Aggregation is the process by which the fuzzy sets that represent the outputs of each rule are combined into a single fuzzy set. Aggregation only occurs once for each output variable, prior to the fifth and final step, defuzzification. The input of the aggregation process is the list of truncated output functions returned by the implication process for each rule. The output of the aggregation process is one fuzzy set for each output variable. Step 5: Defuzzify: The input for the defuzzification process is a fuzzy set and the output is a single number. The aggregate of a fuzzy set encompasses a range of output values, and so must be defuzzified in order to resolve a single output value from the set. C. Fuzzy inference system The Fuzzy system is a mamdani type [12] system with two inputs and two outputs. The system inputs are mobility and pause-time. The both inputs are characterized 82
by the fuzzy membership functions and the pause-time ranges from 0 to 900s. The mobility varies from 0 to 30 m/s. The rules of the fuzzy system are designed for an optimal performance. The rules are evaluated in parallel and the output time-stamp and the number of entries in the neighbor table is determined for a predetermined set of inputs. The outputs are applied to the GloMoSim for evaluations. The simulation of the Fuzzy inference system was done using MATLAB and the values are obtained. Design of FIS Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. The process of fuzzy inference involves all of the pieces: Membership functions, fuzzy logic operators, and if-then rules. In this project mamdani type FIS is used due to its simple nature. Mamdani-type inference expects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output variable that needs defuzzification. For this reason output membership function may be a spike and it is known as a singleton function. The fuzzy operator used for the AND method is min and the operator used for the OR method is max. The implication function modifies that fuzzy set to the degree specified by the antecedent. The implication function used in this work is min which simply truncates the output member function. The aggregation function used is probabilistic OR (prob OR) which is used to aggregate the output fuzzy sets to form a single fuzzy set. The Defuzzification is the process of conversion of fuzzy output set into a single number. The method used for the defuzzification is som, smallest of minimum. The input membership functions are pause-time and speed of the node. The shape of the membership function is triangular. The output membership functions are time-stamp and the number of entries, and they are also designed as triangular. The rules play an important role in the fuzzy logic design. The rules for fuzzy inference system are made on the basis of the inputs mobility and the pause-time. The membership functions for the input and output variables are shown below. Figure 4 (a) Pause-Time Figure 4 (b) Speed of the nodes Figure 4 (c) Number of entries in neighbor table 83
Each packet is 512 bytes long, thus resulting 2K byte per second data transfer rate for each session. B. Results and Evaluations 1) Delivery rate: Packet delivery ratio of a session is the ratio of number of packets tha t are received by the destination over the number of packets submitted to the network by the CBR source. The simulation is averaged for 20 sessions of the runs. Fig. 5 shows the comparison of the delivery rate and pause time. At the lower mobility rate, there is not much difference when SHORT is added. As the mobility rate increases, the delivery rate decreases due to link breaks. The Fuzzy-DSR performs well when compared to that of the conventional DSR. The delivery rate is lower than that of [10]. Figure 4 (d) Time-stamp of neighbor table Fig.4 Membership functions of FIS (Above) The Pause-time and the Speed of the nodes forms the membership functions of the input part of FIS. The number of nodes and the Time-stamp of the entries forms the output of the FIS. The outputs, time-stamp and the number of entries are obtained using a Stand-alone C code for FIS. The different combinations of the inputs, speed of the nodes and pause-time are fed in a file which is given as input to the FIS. The output from the FIS, the number of entries and the time-stamp are noted down for its use in GloMoSim. Fig.5 Comparison of delivery rate IV. PERFORMANCE EVALUATION A. Simulation Setup Simulation is done on the GloMoSim developed at the UCLA labs.it is being used to test the protocols of the wireless networks. The simulator provides a proper model for the signal propagation and its radio model supports a 2Mbps of transmission rate and 250 meters of transmission range. The IEEE 802.11 was simulated at the MAC layer, with the implementation of the distributed coordination function (DCF). In this simulation, 50 mobile nodes move within a rectangular field of 1500m x 300m in size. We choose this rectangular field so that the average hop distance between any two nodes will be larger than that of a square field with the same area. The duration of each run is 900 simulated seconds. The mobility model uses the random waypoint model [13]. The radio model used is the two ray model [14]. We change the mobility rate by setting different values to pause time as 0, 45, 90, 180, 270, 540, 720 and 900 simulated seconds. Here, a pause time of zero means continuous mobility and 900 seconds reflects stable nodes. The maximum moving speed can be 10m/s or 20m/s. We run simulations covering each combination of pause time and moving speed. For the traffic model, we use 20 simultaneous sessions with source-destination pairs spreading randomly on the whole network. Traffic sources are constant- bit-rate, sending 4 UDP packets a second. Fig. 6 Comparison of Control overhead for DSR and Fuzzy DSR (above) Control overhead is the number of control packets used to establish a path to the destination, maintain and repair the routes. It is the RREQ, RREP, RERR and salvaging packets. Figs. 5 and 6 show the comparison of control overhead for the DSR and the Fuzzy- DSR for the pause time 10 and 20 m/s. The Fuzzy DSR has a better performance over the conventional technique. The fuzzy DSR has a reduction of the control overhead due to less link breaks. The reduction in control packets is due to the SHORT technique and the intelligent decision of the Fuzzy inference system. 84
3) Dropped packets: The dropped packets are the data packets that are dropped during the link breaks and collision. Fig. 7 shows that the dropped packets are more for 20 m/s than 10 m/s due to mobility. The results show a low dropped packet for Fuzzy-DSR than the conventional one due to the fact that the packets follow a shortest path and hence the probability of a link to get break is reduced. This can be easily inferred from the Fig. 7. Form the table I and II, it is inferred that Fuzzy DSR out performs the conventional and DSR protocol. V. CONCLUSION In this paper, we have presented a self- healing technique based on Fuzzy concepts for mobile Ad-hoc networks. This proposed technique shows an improvement over the conventional technique DSR. The basic idea is to modify the entries of the neighbor table and the time-stamp of the entry each based on the fuzzy system. The present system has only two inputs. The performance may be improved, if we consider more than two metrics and have more rules to make a perfect decision. The comparison of the end-to-end delay is shown in Fig. 8. The end-to-end delay is averaged over all the packets. The Fig. 8 shows that the packets follow the shortest path, thus a smaller end-to-end delay results. Thus an improvement is obtained using the Fuzzy-SHORT algorithm. REFERENCES [1] D.B. Johnson and D.A. Maltz, Protocols for Adaptive wireless and Mobile Networking, IEEE Personal Communications, Vol.3, No.1, pp.32-42, Feb 1996. [2] C.E. Perkins and P. Bhagwat, Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers, Proceedings of ACM SIGCOMM 94, pp. 234-244, 1994. [3] C.-C. Chiang, H.K. Wu, W. Liu and M.Gerla, Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channels, Proceedings of IEEE SICOM 97, pp.197-211, 1997. 85
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