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Fuzzy Based Node Disjoint QoS Ro...
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constrained quality of service ro...
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and defuzzification. µi(AB) is Me...
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DA’s are dispatched by AA to reac...
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3) QoS Route Maintenance: The pro...
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Fuzzy Based Node Disjoint QoS Routing in MANETs by Using Agents

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Support for real time multimedia applications such
as, video telephony, financial stock quote services, and
multiplayer interactive games etc., is very essential in Mobile
Ad hoc Networks (MANETs). Such applications require
multiple Quality of Service (QoS) parameters to be satisfied,
like bandwidth, end-to- end delay, packet loss rate, jitter, etc.
This paper considers the problem of finding node disjoint and
multi-constrained QoS multipaths from source to destination
by using agent based fuzzy inference system. The proposed
scheme, Fuzzy based Node Disjoint Multipath QoS Routing
(FNDMQR) operates in the following steps by integrating
static and mobile agents. (1) Determination of multiple paths
and picking up of resource information (available bandwidth,
link delay, and packet loss rate) of the intermediate nodes
from source to destination. (2) Recognition of node disjoint,
and multi-constrained QoS fit paths by using Takagi-Sugeno
Fuzzy Inference System (TSFIS). TSFIS extracts a fuzzy QoS
weight from available resource information of the
intermediate nodes. (3) Selection of the best path depending
on the fuzzy QoS weight. (4) Maintenance of QoS path when
path breaks due to mobility of node or link failure. To test the
performance effectiveness of the approach, we have analyzed
the performance parameters like packet delivery ratio, average
end-to-end delay and overall control overhead. The scheme
performs better as compared to a node-disjoint multipath
routing in MANETs.

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Fuzzy Based Node Disjoint QoS Routing in MANETs by Using Agents

  1. 1. Full Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 Fuzzy Based Node Disjoint QoS Routing in MANETs by Using Agents Vijayashree Budyal1, S. S. Manvi2, S. G. Hiremath3 1 Basaveshwar Engineering College, Bagalkot, India Reva Institute of Technology and Management, Bangalore, India 3 G. M. Institute of Technology Davangere, India E-mail: vrbudyal@yahoo.co.in, sunil.manvi@revainstitution.org, drsgh@yahoo.co.in 2 can have nodes and links in common. When a link or node is on several paths severe flow occurs when the incoming traffic load is high. As a result shared link or the node becomes the bottleneck. Node disjoint paths provide more reliability than the link disjoint paths [3].With the increasing demand in realtime multimedia, application in video telephony, video conferencing, and military arena requires multi-constrained Quality of Service (QoS) to be fulfilled. The QoS requirement of connection includes parameters like bandwidth, end-toend delay, jitter, packet loss rate etc. Multi-constraint QoS parameters are imprecise and uncertain due to dynamic topology of MANETs. However, selecting a route, which satisfies all multiple constraints, is an NP complete problem [4]. There is no accurate mathematical model to describe it. Fuzzy logic is used to provide a feasible tool to solve the multi-metric QoS problem. Fuzzy logic is a theory that not only supports several inputs, but also exploits the pervasive imprecision information [5]. So adopting fuzzy logic to solve multi metric problems in ad hoc networks is an appropriate choice. Multi-constraint based routing protocols use QoS satisfied paths other than the single shortest path to route the packets. If multiple node disjoint paths with multiconstraint QoS paths are set up between a source and a destination, then source node can use these routes as primary and backup routes, i.e., a new route discovery is invoked only when all of the routing paths fail or when there only remains a single path available, whenever node or link fails. This helps to reduce overhead in finding alternative routes and extra delay in packet delivery introduced. Therefore, in this paper we adopt both node disjoint and multi-constraint QoS routing in MANETs. Software agents based applications are an emerging discipline, which can be applied to provide flexible, adaptable, and intelligent services in MANETs. Software agents are autonomous and intelligent programs that execute tasks on behalf of a process or a user. They have two special properties: mandatory and orthogonal, which make them different from the standard programs. Mandatory properties are: autonomy, reactive, proactive and temporally continuous. The orthogonal properties are: communicative, mobile, learning and believable [6]. Abstract— Support for real time multimedia applications such as, video telephony, financial stock quote services, and multiplayer interactive games etc., is very essential in Mobile Ad hoc Networks (MANETs). Such applications require multiple Quality of Service (QoS) parameters to be satisfied, like bandwidth, end-to- end delay, packet loss rate, jitter, etc. This paper considers the problem of finding node disjoint and multi-constrained QoS multipaths from source to destination by using agent based fuzzy inference system. The proposed scheme, Fuzzy based Node Disjoint Multipath QoS Routing (FNDMQR) operates in the following steps by integrating static and mobile agents. (1) Determination of multiple paths and picking up of resource information (available bandwidth, link delay, and packet loss rate) of the intermediate nodes from source to destination. (2) Recognition of node disjoint, and multi-constrained QoS fit paths by using Takagi-Sugeno Fuzzy Inference System (TSFIS). TSFIS extracts a fuzzy QoS weight from available resource information of the intermediate nodes. (3) Selection of the best path depending on the fuzzy QoS weight. (4) Maintenance of QoS path when path breaks due to mobility of node or link failure. To test the performance effectiveness of the approach, we have analyzed the performance parameters like packet delivery ratio, average end-to-end delay and overall control overhead. The scheme performs better as compared to a node-disjoint multipath routing in MANETs. Index Terms—MANETs, QoS, Takagi-Sugeno fuzzy Inference, software agents. I. INTRODUCTION Ad hoc wireless network consists of collection of mobile devices like, personal digital assistant (PDA), laptops, cell phones etc. These nodes are interconnected by multi-hop communication path, due to limited transmission range. The route found between source and destination becomes invalid often because of the temporary topology of the network. Therefore routing in Mobile Ad hoc Networks (MANETs) is a challenging task [1].Multi-path provides more than one route to the destination node. Multi-path routing protocols are deemed superior over conventional single path protocols for enhanced throughput, reliability, robustness, load balancing, fault-tolerance, offering QoS, and to avoid frequent route discovery attempts [2]. Multi-path routing protocols can attempt to find node- disjoint, link-disjoint, or non-disjoint routes. Node- disjoint routes have no nodes or links in common on the routes. Link-disjoint routes have no links in common, but may have nodes in common. Non-disjoint routes © 2013 ACEEE DOI: 03.LSCS.2013.1. 544 A. Related Work Some of the related works to build multi-constrained QoS routing in MANETs are as follows: Fuzzy cost based multi7
  2. 2. Full Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 constrained quality of service routing is discussed in [7] to select an optimal path by considering multiple independent QoS metrics such as bandwidth, end-to-end delay, and number of intermediate hops.The work given in [8] explores the node disjoint path routing subject to different degrees of path coupling, with and without packet redundancy. Multipath routing problem of MANETs with multiple QoS constraints, which may deal with the delay, bandwidth and reliability metrics, and researching the routing problem is explained in [9]. Architecture for guaranteeing QoS based on nodedisjoint multi-path routing protocol in MANETs is explained in [10]. The work given in [11] uses fuzzy set and roughs set theory to select an effective routing path in MANETs. In the first stage, the data set consisting of resources and paths are fuzzified. In the second stage, information gain is calculated by using ID3 algorithm for evaluating the importance among attributes. In the third stage, a decision table is reduced by removing redundant attributes without any information loss. Finally, if-then decision rules are extracted from the equivalence class to select the best routing path. Fuzzy based priority scheduler for mobile ad-hoc networks, to determine the priority of the packets using Destination Sequenced Distance as the routing protocols is presented in [12]. The proposed fuzzy agent based Node Disjoint Multi-path QoS Routing in MANETs is motivated by observing inherent drawbacks of existing QoS routing schemes like: lack of support of multi-constraint QoS routing and maintenance of the QoS path when link/node fails. B. Our Contributions In this work, we investigate on the use of Takagi-Sugeno fuzzy inference system (TSFIS) for multi-constrained QoS route selection in MANETs, integrating static and mobile agents. Source knows the multiple nodes disjoint paths to the destination, and collects the resource information (available bandwidth, delay, and packet loss rate) of intermediate nodes. The source uses gathered intermediate node information to select the QoS path by using TSFIS model. This model accepts uncertain and imprecise crisp parameters like, available bandwidth, link delay, and packet loss rate as input and is being processed in stages, i.e., fuzzification, inference, and defuzzification. After experiencing all the stages, a single value score fuzzy QoS weight is generated from the combination metrics for each node on the path. This is used to measure QoS satisfaction on the path. The performance of our scheme Fuzzy based Node disjoint Multipath QoS Routing (FNDMQR) is compared to nodedisjoin multi-path routing in MANETs (NDMRP) [9].The rest of paper is organized as follows. Section II explains proposed work on fuzzy agent based multi-constrained QoS routing. Section III describes an evaluation of our approach using simulation. Finally, section IV concludes our paper. II. PROPOSED WORK This section describes network model, Takagi-Sugeno Fuzzy Inference System (TSFIS), QoS routing agency, and 8 © 2013 ACEEE DOI: 03.LSCS.2013.1.544 fuzzy and agent based multi-constraint QoS routing scheme. A. Network Model An ad hoc network consists of set of mobile nodes and set of links between the mobile nodes as shown in figure 1. Due to mobility of the nodes in the ad hoc network, link connection varies with respect to time. Each mobile node has certain transmission range. Each node is equipped with an agent platform and an agency in which agents reside. We assumed that agents have protection from hosts on which they execute. Similarly, hosts have protection from agents that can communicate on available platform. The secured platform consists of protection from denial of execution, masquerading, eavesdropping, etc. Recently developed techniques for mobile agent security have techniques for protecting the agent platform. Fig. 1. A Mobile Ad hoc Network B. Takagi-Sugeno Fuzzy Inference System Fuzzy system is classified as Mamdani and Takagi-Sugeno models. In this paper we propose Takagi Sugeno (first-order) fuzzy inference system for reasoning, because as it has high interpretability and computational efficiency, and built-in optimal and adaptive technique. And also, it is not necessary to define a prior linguistic terms for conclusions, since the mapping is direct. And also, the effort of performing defuzzification is saved, because the crisp output is directly determined by the fuzzy mean formula. Our Takagi-Sugeno fuzzy system consists of three crisp inputs and one output. The system inputs are available bandwidth ‘AB’ and link delay ‘TD’, and packet loss rate ‘PR’ of the intermediate nodes and output is QoS weight ‘γ’. Three inputs are characterized by bell shaped membership functions. Bell function for ‘AB’ is defined by equation 1. i ( AB )  1 AB  ci 2bi 1 ( ) ………. ai (1) Where a, b and c are the parameters of membership function governing the centre, width and slope of the bell-shaped membership function. ‘TD’ and ‘PR’ take similar kind of bell function. The steps involved in FIS are fuzzification,inference
  3. 3. Full Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 and defuzzification. µi(AB) is Membership function value for the available bandwidth. Fuzzification: The first step is to consider the crisp inputs and determine the degree to which they belong to each of the appropriate linguistic sets via bell membership functions which is termed as fuzzification. Fuzzification converts input data into suitable fuzzy values (linguistic terms). The linguistic terms, which divide the membership functions for available bandwidth, are {ABless, ABmore} and is as shown in figure 2. Link delay linguistic terms are {TDless, TDmore}, and for packet loss rate the linguistic terms are {PRless, PRmore}. Vertical coordinates represent the degree of membership, which distributes in the interval of [0- 1]. Defuzzification: The final output fuzzy QoS weight ‘γ’ of the system is the weighted average of all rule outputs, computed as given in equation 3. N w z i i  i 1 N w ………. (3) i i 1 C. QoS Routing Agency Each node comprises of Fuzzy based Node Disjoint Multipath QoS Routing agency (FNDMQR). Components of agency and their interactions are depicted in figure 3. Agency consists of Knowledge Base (KB), static agents and mobile agents. Static agent are Administrator Agent (AA), and QoS Decision Agent (QDA). Mobile agents are Disjoint Agent (DA) and Recovery Agent (RA). Fig. 2. Membership functions for available bandwidth Inference: Fuzzified data trigger one or several rules in the fuzzy model to calculate the result. The fuzzy rules are realized in the form of IF-THEN. The input parameters are combined using T-norm operator ‘AND’. The total number of rules formed is as follows: Rule 1: If AB is ‘ABless’ and TD is ‘TDless’ and PR is‘PRless’ Then z1 = Ψ1AB + ζ 1T D + φ1PR + σ1 Rule 2: If AB is ‘ABless’ and TD is ‘TDless’ and PR is‘PRmore’ Then z2 = Ψ2AB + ζ 2TD + φ2PR + σ2 Rule 3: If AB is ‘ABless’ and TD is ‘TDmore’ and PR is‘PRless’ Then z3 = Ψ3AB + ζ 3TD + φ3PR + σ3 Rule 5: If AB is ‘ABmore’ and TD is ‘TDless’ and PR is‘PRless’ Then z5 = Ψ5AB + ζ5TD + φ5PR + σ5 Rule 6: If AB is ‘ABmore’ and TD is ‘TDless’ and PR is‘PRmore’ Then z6 = Ψ6AB + ζ6TD + φ6PR + σ6 Rule 7: If AB is ‘ABmore’ and TD is ‘TDmore’ and PR is‘PRless’ Then z7 = Ψ7 AB + ζ7TD + φ7 PR + σ7 Rule 8: If AB is ‘ABmore’ and TD is ‘TDmore’ and PR is‘PRmore’ Then z8 = Ψ8AB + ζ8TD + φ8 PR + σ8 The output level zi of each rule is weighted by the firing strength wi of the rule given by 2. Ψi , ζi , φi , and σi are constants chosen between 0-1. Where i = 1 to N. N is the number of rules. For example, for and ‘AND’ rule with inputs AB and TD, and PR have a firing strength as given by equation 2. wi = µ(AB) . µ (TD). µ (PR) …….. (2) Where µ (AB), µ (TD), and µ (PR) are the membership values for inputs available bandwidth and link delay and packet loss rate. © 2013 ACEEE DOI: 03.LSCS.2013.1. 544 Fig. 3. Fuzzy based node disjoint multipath QoS routing agency KB: KB of source comprises of information of node ID, destination, resource information {AB, TD, PR} of the intermediate nodes on the paths, multiple path IDs from source to destination and their fuzzy QoS weight γ obtained by using TSFIS and running application(s) details.. Intermediate node KB consists of node status (connected/disconnected to network), Node disjoint Forward QoS Routing Table (NDFQRT), {AB, TD, PR} of its own. KB is read, updated and is used by agencies (AA, QDA, DA and RA) to establish QoS route and to maintain the path between source and destination. Administrator Agent: It is a static agent and performs the following functions at source, (1) creates and dispatches DA to find multiple paths to destination, (2) collects multiple node disjoint paths and resource information of intermediate nodes from DA, (3) computes γ for each node by using TSFIS and ‘Γ’ for each node disjoint path (4) selects a QoS node disjoint path from multiple node disjoint paths, and (5) initiates reconstruction of QoS path upon request from RA during link/node failure. Disjoint Agent: It is a mobile agent triggered by AA of source whenever it wishes to send data to the destination. 9
  4. 4. Full Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 DA’s are dispatched by AA to reach all its neighbors. Every DA performs the following functions. (1) Traces all the feasible paths to the destination by cloning. Gathers intermediate node resource information {AB, TD, PR}. (2) Handover the multiple path information to AA of destination. (3) AA of destination separates out the node disjoint path from a number of multiple paths identified by DA, and (4) DA traverses back through the node disjoint paths to reach source, gathering resource information of the intermediate nodes. QoS Decision Agent: This agent is a static agent triggered by AA only at the source node. It is responsible for computing the γ for each of the node on the disjoint paths by using TSFIS. Updates computed γ of each node on the node disjoint paths in AA of source. Later it is disposed off. Recovery Agent: It is a mobile agent and performs the operation of route maintenance whenever link/node fails. route to reach the source. When destination AA receives duplicate DA, it compares the whole node IDs of the entire route with existing node disjoint paths in its reverse routing table. If there is no common node (except source and destination) between the node IDs from the the duplicate DA and node IDs of existing node disjoint path in the destination reverse routing table then, the path in current DA is node disjoint path and is recorded in the reverse routing table of the destination and DA traces the reverse route to reach the source. Otherwise, current DA is disposed. DA collects the intermediate node’s resource information {AB, TD, PR} while tracing back the reverse path from destination to source. The multiple node isjoint paths and resource information of the intermediate node is made available to the AA of source for further QoS verification. 2) Fuzzy Agent based QoS Path Selection: Multiconstrained QoS path is selected from numerous known node disjoint multi-paths placed in AA of source by using TSFIS (refer section II B). TSFIS computes the γ for every node on each of the path by considering { AB, TD, PR } as input metrics to TSFIS. AA of source computes Γ by considering γ of all the nodes on the path and is given by equation 4. D. Fuzzy and Agent based Multi-constraint QoS Routing Scheme This section describes the functioning of the proposed multi-constraint QoS routing scheme. The scheme operates in the following steps. 1) Recognition of node disjoint multiple paths to the destination: When a source node needs multi-constraint QoS path to the destination. Source AA dispatches DA to reach its neighbors. DA carries source ID, sequence number, maximum number of hops, and traveled node list. Upon reaching the intermediate node, AA of intermediate node checks for the duplication of the DA by looking at the sequence number. When receiving a duplicate DA, the possibility of finding node disjoint multiple paths is zero if it is dropped, for it may come from another path. But if all of the duplicate DA are broadcast, this will generate broadcast storm and decrease performance. In order to avoid this problem DA records the shortest routing hops to keep loop-free paths and decrease routing broadcast overhead. When intermediate node receives DA for the first time, it checks the node list of path traversed and calculates the number of hops from the source node to itself and records the number as the shortest number of hops in its reverse routing table. If the node receives the duplicate DA, it computes the number of hops and compares with shortest number of hops in its reverse routing table. If the number of hops is more than shortest number of hops in the reverse routing table, then the DA is dropped. Only when it is less than or equal to the shortest number of hops, the node appends its own address to the node list of path in DA and is cloned to reach the neighbors or the destination. Agent cloning is a technique of creating an agent similar to that of parent, where cloned agent contains the information of parent agent that it has traversed. A child agent can communicate either to any one of its parents who are within the range or to any of its parents at a given level.When first DA is received by the destination, it records the list of node IDs of entire route in its reverse route table and DA traces the reverse © 2013 ACEEE DOI: 03.LSCS.2013.1.544 P  j  i 1 ……………. (4) P Where, P is the number of nodes on the path ‘j’. If à is greater than QoS required by the user, it implies the path satisfies the requirement and QoS packets are transmitted through that path.As an example consider figure 4, which is consisting of number of mobile nodes. There exist multiple paths between source and destination shown with dotted lines. Destination decides a node disjoint paths from numerous multiple paths and these multiple node disjoint paths are shown with solid lines. Upon receiving node disjoint paths, source AA uses TSFIS to identify a paths which satisfies multi-constraint QoS shown with solid bidirectional arrow. One among them with least number of hops is selected as QoS path to route the packets. The other QoS satisfied node disjoint paths act as back up paths. Fig. 4. Fuzzy based node disjoint multi-path QoS routing agency 10
  5. 5. Full Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 3) QoS Route Maintenance: The proposed scheme uses RA to maintain QoS path. Whenever node moves or fails, then RA sends error to the source. AA of the source checks to find a path from the existing QoS satisfied node disjoint multipaths to reach destination. If not found it initiates new route discovery. III. SIMULATION The proposed FNDMQR scheme is simulated along with NDMRP in the network scenario using C programming language to verify the performance and operation effectiveness. Membership functions and rule bases of the fuzzy are carefully designed and the output is verified using Matlab 7.0 fuzzy logic toolbox with FIS editor. Then the inputs are identified in the library of C code programming. In this section, we describe the simulation model. Fig. 5. Packet delivery Ratio vs. Node Speed the multi-constrained QoS on the path by using TSFIS and stable path is identified by considering minimum number of hops. Average end-to-end delay generated for varying number of nodes and speed is reported in the figure 6. As the node speed increases average end to end delay also increases. The decrease of end-to-end delay in FNDMQR is mainly presented by selecting a suitable QoS route that results in reduction of path breakage. Where as NDMRP suffers frequent link breaks and needs route reconstruction frequently which results in increase in end-to-end delay. A. Simulation Model A mobile ad hoc simulation model consists of N = 80 number of mobile nodes placed randomly within the area of A X B = 1000 X 1000 m2. A random way point mobility model is used. Each node randomly selected a position with a speed ranging from Smin to Smax = 0-10 m/s. A pause time Pautime = 0-10 sec, is assigned for each node. If a node tries to go out of the boundary, its direction is reversed (Bouncing ball model). The radio propagation range for each node is selected as R ran = 250 m and channel capacity is Ch cap = 10 Mbps. Link delays may vary between Ldmin to Ldmax = 20-50 ms. The sources and destinations are randomly selected with uniform probabilities. Residual power of each node varied between pwrmin to pwrmax = 20- 200 mW. Traffic sources are with constant bit rate (CBR) with data payload size as Dtpld = 512 bytes. Each simulation is executed for Simtime = 600 seconds. Simulation was carried out with different QoS requirements. The following performance metrics are used for evaluating the proposed scheme. Packet delivery ratio (PDR): It is the ratio of the number of data packets delivered to the destination node to the number of data packets transmitted by the source node. It is expressed in percentage. Overall control Overhead: It is defined as the ratio of the total number of control messages or agents to the total number of packets generated to perform communication. Average end-to-end delay: It is defined as the average time taken to transmit predefined number of packets from source to destination. It is expressed in seconds. Fig. 6. Average end-to-end delay vs. Node Speed Figure 7 shows that the average end to end delay raisesgradually as the number of source increases. The reason is that with increasing number of sources, the total traffic load increases and the network becomes congested. So, more packets are kept waiting in the queues for long time which causes the delay to increase. However FNDMQR outperforms NDMRP in reducing the end-to-end delay. B. Results In this section, we discuss various results obtained through simulation. The results include packet delivery ratio, overall control overhead, average end-to-end delay. Our scheme FNDMQR is compared with existing NDMRP.Figure 5 depicts PDR with variation in node speed and number of nodes. PDR decreases, as node speed, increases in both FNDMQR and NDMRP because when node speed increases packets are lost while reconstructing the QoS path. PDR of FNDMQR is more compared to NDMRP since it accounts © 2013 ACEEE DOI: 03.LSCS.2013.1. 544 Fig. 7. Average end-to-end delay vs. No. of Sources 11
  6. 6. Full Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 Overall control overhead with respect to node speed and number of nodes are shown in figure 8. As the speed of the nodes increases control overhead increases. Because of network connectivity, as the node mobility increases mobile agents are generated for repairing the path for the QoS communication. Ad hoc Networks: A Quantitative Comparison”, Proc. Springer Next Generation Tele traffic and Wired/Wireless Advanced Networking Lecture Notes in Computer Science, vol. 4003, pp. 313-316, 2006. [3] Luo Liu, Laurie Cuthbert, “ Multi- rate QoS enabled NDMR for Mobile Ad Hoc Networks”, Proc. IEEE International Conference on Computer Science and Software Engineering, pp. 143-146. Wuhan, China, 2008. [4] Sanguankotchakorn T, Maharajan P., “A New Approach for QoS Provision based on Multi-constrained feasible Path Selection in MANETs”, Proc. 8 th IEEE International Conference on Electrical Engineering/ Electronics,Computer, Telecommunication and InformationTechnology, pp. 352-356, Khon Kaen University, Thailand,2011. [5] V. R. Budyal, S. S. Manvi, S. G. Hiremath, “Fuzzy Agent Based Quality of Service Multicast Routing in Mobile Ad Hoc Networks”, Proc. IEEE International Conference on Advances in Mobile Network, Communication and its Applications, pp. 95-98, Bangalore, India, 2012. [6] S.S. Manvi, P. Venkataram, “Applications of Agent Technology in Communications: A Review”, International Journal of Computer Communications, vol. 27, pp. 1493- 1508, 2004. [7] G. Santhi, Alamelu Nachiappan,” Fuzzy-cost based Multiconstrained QoS Routing with Mobility Prediction in MANETs”, Elsevier Egyptian Informatics Journal, vol. 13, pp. 19-25, 2012. [8] Xiaoxia Huang, Yuguang Fang, “Performance Study ofNodeDisjoint Multi-path Routing in Vehicular Ad Hoc Networks”, IEEE Transactions on Vehicular Technology, vol. 58, no. 4, 2009. [9] Xu Yi, Cui Mei, Yang Wei, Xan Yin, “A Node-disjoin Multipath Routing in Mobile Ad hoc Networks”, Proc.IEEE International Conference on Electric Information and Control Engineering, pp. 1067-1070, Wuhan, China, 2011. [10] Luo Liu, Laurie Cuthbert, “A Novel QoS in Node-Disjoint Routing for Ad Hoc Networks”, Proc. IEEE International Conference on Communications Workshops, pp. 202-206, Beijing, China, 2008. [11] P. Seethalakshmi, M.Gomathi, G.Rajendran, “Path Selection in Wireless Mobile Ad Hoc Network Using Fuzzy and Rough Set Theory”, Proc. IEEE International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic System Technology, pp. 1-5, Denmark, 2011. [12] K. Manoj, S. C. Sharma, Leena Arya, “Fuzzy Based QoS Analysis in Wireless Ad hoc Network for DSR Protocol”, Proc. IEEE International Conference on Advance Computing Conference (IACC 2009), pp. 1357- 1361, Patiala, India, 2009. Fig. 8. Overall Control Overhead vs. Node Speed CONCLUSIONS This paper presented fuzzy based multi-constrained QoS node disjoint multi-path routing in MANETs by using agents. Fuzzy rule base is developed to unite the various uncertain QoS metrics such as available bandwidth, link delay, and packet loss rate to generate single QoS weight for the node disjoint paths, which is used for path selection. The results for our proposed FNDMQR show good packet delivery ratio and reduction in end-to-end delay and control overhead. The agent-based architectures provide flexible, adaptable and asynchronous mechanisms for distributed network management, and facilitate software reuse and maintenance. Future work includes optimization of membership function of fuzzy system according to the user requirement, to support QoS routing in MANETs REFERENCES [1] Yuh Shyan Chen, Yu-Chee Tseng, and Jang - Ping Sheu,Po Hsuen Kuo, “ An On-demand, Link State, Multi - path QoS Routing in Wireless Mobile Ad hoc network”, Elsevier International Journal of Computer Communications, vol. 27, no. 1, pp. 27-40, 2004. [2] Georgios Parissidis, Vincent Lenders, Martin May, Bernhard Platter,” Multi-path Routing Protocols in Wireless Mobile © 2013 ACEEE DOI: 03.LSCS.2013.1.544 12

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