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20120140505009

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 75 A REVIEW ON FUZZY LOGIC BASED ROUTING IN AD HOC NETWORKS Pimal Khanpara CSE Department, Institute of Technology, Nirma University, Ahmedabad, India – 382481 ABSTRACT Ad hoc networks are increasing in popularity due to the spread of mobile devices. These networks are widely used in emergency situations like natural disasters and military conflicts. The dynamic and infra structureless nature of ad hoc networks makes them suitable for such applications. Self organization and self administration are the key features of any ad hoc network. Due to the dynamic and rapid movement of nodes, routing becomes very difficult in the ad hoc networks. Various routing schemes based on different approaches like Swarm based routing, Fuzzy logic based routing, Trust based routing, Energy level based routing, Secure routing etc. are proposed by the researchers. In the comparison of all the existing approaches, Fuzzy Logic Theory is proved to be a good approach for routing in the ad hoc networks. The main reason behind this is the capability of Fuzzy theory to handle uncertainties and randomness. This paper aims at reviewing the existing fuzzy logic theory based routing protocols that consider QoS parameters as routing metrics. Keywords: Ad hoc networks, Fuzzy Logic, Quality of Service parameters, Routing. I. INTRODUCTION An ad hoc network [1] is a group of nodes which are connected dynamically and moving rapidly. Such a network is decentralized in manner and wireless in nature. The topology of the network is not fixed due to the movement of the participating nodes. In an ad hoc network, the nodes can enter or leave the network at any time. In this dynamic environment, the implementation of routing functionality is challenging because a good route will probably be unavailable after a short while. The routing information is required to be updated with every change in the network topology. This results in consuming the precious network resources like bandwidth. Therefore, discovering and maintaining a route in the ad hoc environment is difficult. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 76 II. ROUTING PROTOCOLS Routing protocols proposed for ad hoc networks are categorized as: II.i. REACTIVE In the reactive routing protocol, the required routes are established only on demand. This may result into long waiting time for a node that wants to transmit because before the route discovery, transmission process can not be started. The advantage of reactive protocols is that routing information is not required be maintained and broadcasted with the changing network topology as a fresh route is always used for each transmission request. Examples of such protocols are AODV (Ad hoc On Demand distance Vector) [2] and DSR (Dynamic Source Routing) [3]. II.ii. PROACTIVE Proactive or table driven routing protocols maintain up-to-date and consistent routing information on all the nodes in the network. Whenever the topology changes, the routing information stored in routing tables is required to be updated. The clear advantage of the proactive protocols are that the transmitting node can immediately take the routing decision as the required path information is always available in the routing table. Some popular proactive protocols for ad hoc networks are TBRPF (Topology Broadcast based on Reverse Path Forwarding) [4] and OLSR (Optimized Link State Routing) [5]. II.iii. HYBRID Hybrid routing protocols incorporate the good features of both reactive and proactive routing protocols. These protocols are highly scalable and do not require to flood the network randomly. Some widely used hybrid routing protocols are HopNet [6] and ZRP (Zone Routing Protocol) [7]. III. FUZZY LOGIC THEORY Fuzzy Logic (FL) [8] is an approach for computing the results based on “degrees of truth”. It differs from the conventional binary logic. In the binary or boolean logic, we can use only two values, “true or false” or “0 or 1”. This logic is widely used in designing the modern computers. But for some problems, the results can not be represented into the absolute terms of 0 and 1. For such problems, Fuzzy Logic can be used to give the reasoning that is approximate rather than fixed or exact. In Fuzzy Logic Theory, the truth values range between completely true and completely false. This theory uses non-numeric variables called the linguistic variables to express the rules and facts for the given system. In Fuzzy Logic, the rules are defined in form of IF-THEN. We can also use some boolean operators like AND, OR and NOT in Fuzzy Logic. In some literature, other operators are also used which are linguistic in nature. Fuzzy Logic Theory has been used in numerous applications like artificial intelligence, pattern recognition, controlling unmanned military vehicles, knowledge based systems, weather forecasting systems, stock trading, subway control system and medical diagnosis. In fact, almost any control system can be replaced with a fuzzy logic based control system. A Fuzzy Logic System is mainly comprised of four components: Fuzzifier, Defuzzifier, Fuzzy Rule Base and Fuzzy Inference Engine. These components are arranged as follows in any Fuzzy Logic System.
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 77 Figure 1: Fuzzy Logic System Fuzzification is the first process that takes place in the FLS. A numeric or crisp input value is given to the Fuzzifier. The crisp input value is required to be converted to the corresponding fuzzy value as the rules for determining the result, are defined for fuzzy inputs. This task is performed by the Fuzzifier and then the fuzzy input values are supplied to the Fuzzy Inference Engine, which is responsible for computing the set of outputs based on the IF-THEN rules defined in the Fuzzy Rule Base. Usually, when more than one inputs are required, AND operator is used to combine them. The last process in the Fuzzy Logic System is defuzzification. It coverts the fuzzy output values into their corresponding crisp values. There are different methods for fuzzification and defuzzification. Some widely used fuzzifiers are Singleton fuzzifier, Gaussian fuzzifier and Trapezoidal or Triangle fuzzifier. Singleton fuzzifier is the simplest fuzzifier which basically assigns a precise value to the given input and hence no fuzziness is introduced by fuzzification in this case. Gaussian and Triangular fuzzifiers are used to suppress the noise in the given inputs. Examples of defuzzifiers are Maximum defuzzifier, Mean of maxima defuzzifier, Centroid defuzzifier, Height defuzzifier, Modified height defuzzifier, center of sets and center of sums. IV. WHY FUZZY ROUTING? [9] The advantages of Fuzzy Logic are its simplicity, flexibility of combining conventional control techniques, ability to model nonlinear functions and imprecise information, use of empirical knowledge and dependency on heuristics. Due to the basic characteristics of ad hoc networks like uncertainty due to dynamic topology and mobility of nodes, limited resources and unstable links; a precise and accurate model is not possible to implement. In such an environment, Fuzzy Logic Theory has been proved a good approach for routing compared to other routing methods. Fuzzy Logic ccan be used to solve the problem of routing in ad hoc networks where the final outcome is based on the factors with uncertainty.
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 78 V. QUALITY OF SERVICE Quality of service (QoS) is a set of service requirements that needs to be met by the network while transmitting a stream of packets from a source to its destination. QoS is considered as an important aspect to utilize the network resources in a better and efficient manner. In ad hoc networks, we have to consider bandwidth, jitter, node energy level, queue length, delay, cost and reliability constraints as the main QoS parameters [10]. These QoS parameters can be given as inputs to the fuzzy controller for determining the most preferred path. VI. FUZZY LOGIC BASED ROUTING PROTOCOLS There are many algorithms proposed for routing in the ad hoc networks. Most of them are either purely reactive or proactive and thus fail to have the features of the hybrid routing protocols. Another important limitation of almost all the existing algorithms is that they do not take all the Quality of Service parameters into consideration while determining the “best” path. Most of the proposed routing algorithms consider path cost and delay as the routing metrics. But in an ad hoc environment, it is actually important to focus on all or maximum QoS parameters at the time of finding the optimal route. This section gives the overview of the existing Fuzzy Logic based routing algorithms for the ad hoc networks. VI.i. RRAF (Reliable Routing Algorithm based on Fuzzy logic) [11] In this algorithm, two parameters, trust value and energy value are defined for each node. Based on the values of these parameters, lifetime of the routes are determined. This scheme basically uses AODV for routing. At the time of route discovery, each node inserts its trust value and energy capacity in the Route Request (RREQ) packet. Fuzzy Logic is used at the destination. A parameter called “Reliability value” is generated by the destination using the input trust and energy values. This reliability value is then used for routing. A path which is having greater reliability value is preferred over the others. So, this algorithm improves the performance of AODV but fails to consider the important QoS parameters except reliability and cost. VI.ii. FSRS ( Fuzzy based Stable Routing Scheme) [12] The objective of this algorithm is to find the most stable route for routing. For this, it takes the number of intermediate nodes, packet queue occupancy and the distance between the nodes as the input parameters. A fuzzy controller is used by the algorithm to calculate the lifetime of the route. Route cost is the input given to the fuzzy controller. The proposed scheme considers the average end to end delay, packet delivery ratio and routing load as the metrics. VI.iii. FQRA ( Fuzzy QoS Routing Algorithm) [13] This algorithm is an extension of the shortest routing path algorithm and uses the bandwidth and delay as the routing metrics. Improved path success ratio, improved throughput and reduced end to end delay are good features of this scheme. VI.iv. FLWMR ( Fuzzy Logic Wireless Multi path Routing) [14] Fuzzy Logic is used by this algorithm for allocating the resources in the network considering the traffic in the network. Zero or more maximally disjoint routes are used to transport the messages. Some packets are transmitted redundantly over more than one disjoint paths to increase the reliability, while some packets are suppressed at the source. This decision is taken based on the importance of the packets. When a request for data transmission comes, the route request messages are broadcasted by the source node to every other node in the network. When the request packet reaches to the destination, the path traveled by that request packet is recorded and then that path is
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 79 used for transmitting the data. VI.v. FLWLAMR ( Fuzzy Logic Wireless Load Aware Multi path Routing) [14] FLWLAMR is an extension of FLWMR algorithm. It considers the network status while selecting the route. To determine the network status, Fuzzy Logic is used. Fuzzy rules are defined to get the value for the network state, ranging from excellent to poor. Packets are forwarded immediately if the network is in excellent state. VI.vi. Multiclass FQRA [15] This algorithm considers the traffic state and bandwidth as the input parameters to take the routing decision based on the Fuzzy Logic. It used weighted round robin scheduler concept for forwarding the packets. The weights are assigned to the queues containing the data and based on these weight values, fuzzy controller determines the routing process. VI.vii. SSR (Source Select Route) [16] This routing algorithm aims at finding the optimized path between the source and the destination. Maximum distance between the nodes, maximum relative speed between the neighbour nodes and the total number of data transmission links in the intermediate nodes are the important factors for selecting the route. The authors claimed that the proposed algorithm performs better over the conventional routing algorithms for the ad hoc networks. VI.viii.Fuzzy Scheduler [17] The routing decision made by this scheme is dependent on the priority values of the packets. To calculate the priority of a packet, the algorithm considers various input parameters. The algorithm uses the fuzzy scheduler to determine the priority index values of the packets. Packet lifetime and the transmission rate are the important factors to be given to the fuzzy scheduler as the inputs. VI.ix. FAQM (Fuzzy Algorithm for QoS Multicast routing) [18] This algorithm used the concept of Swarm Intelligence with the Fuzzy Logic Theory. The authors of this algorithm have considered three Quality of Service parameters,: bandwidth, jitter and delay. This multicast routing protocol outperforms the conventional routing protocols for ad hoc networks. VI.x. ImRMR (Improved Rank based Multicast Routing) [19] In this multipath routing protocol, the routes are selected based upon the values of five parameters: bandwidth, number of hops, efficiency of the selected path, power consumption and traffic state. The algorithm tries to evaluate the rank for each existing path using the five resource constraints. According to the authors, this protocol performs better than the existing protocol ODMRP (On Demand Multicast Routing Protocol). VI.xi. FCMQR (Fuzzy Cost based Multi constrained QoS Routing) [20] This algorithm is based on multi criterion objective fuzzy measure. To select an optimal path, this protocol takes different parameters like bandwidth, number of intermediate hops and end to end delay into account. All the available resources for a path are used to compute the fuzzy cost for that path. The path which is having minimum fuzzy cost and maximum lifetime is chosen as the optimal route for transmission. Table 1 shows the comparison of the above existing Fuzzy Logic based routing algorithms with respect to a variety of QoS parameters. None of these protocols consider all the required QoS parameters.
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 80 Table 1 Comparison of fuzzy logic based routing protocols Routing Protocol QoS Parameters Remarks RRAF (Reliable Routing Algorithm based on Fuzzy logic) [14] Trust value, energy level Fails to consider time, frequency, space and cost metrics FSRS (Fuzzy based Stable Routing Scheme) [20] Hop count, queue status, distance between nodes Uses two fuzzy controllers into each node that take three QoS parameters as inputs FQRA (Fuzzy QoS Routing Algorithm) [17] Delay, bandwidth Based on Dijkstra’s shortest path algorithm; bandwidth and delay are taken into consideration FLWMR (Fuzzy Logic Wireless Multipath Routing) [2] Number of intermediate nodes Considers only hop count to find the optimal route FLWLAMR (Fuzzy Logic Wireless Load Aware Multipath Routing) [2] Network status Extension of FLWMR; deals with load balancing by considering network traffic Multiclass FQRA [18] Scheduler; bandwidth, delay Based on the packet weight, the scheduler takes the routing decision considering time and frequency constraints SSR (Source Select Route) [23] Maximum distance between intermediate nodes, maximum relative speed, total number of links Does not consider all the QoS parameters Fuzzy scheduler [16] Priority value Routing takes place based on the priority index of the packet FAQM (Fuzzy Algorithm for QoS Multicast Routing) [19] Bandwidth, delay, jitter Multicast routing algorithm based on Artificial Fish Swarm algorithm. Bandwidth, delay and jitter are taken as routing metrics ImRMR (Improved Rank based Multicast Routing) [21] Bandwidth, computing power, hop count, network load, energy consumption Does not consider all the QoS parameters FCMQR (Fuzzy Cost based Multi constrained QoS Routing) [22] Delay, link expiration time, bandwidth Fails to consider space, cost, energy level and reliability constraints VII. CONCLUSION This paper discusses how the Fuzzy Logic Theory can be used for implementing routing in the ad hoc networks. To select an optimal route in the ad hoc networks, it is necessary to consider all or maximum Quality of Service parameters. But to the best of my knowledge, none of the existing algorithms has this feature. Most of the authors have used two or three parameters as routing metrics. So, the objective of this survey is to find out the possibility of research in this area. The future scope of this work lies into the development of a new, Fuzzy Logic based routing protocol for ad hoc networks that take all the important QoS parameters into consideration. REFERENCES [1] Stefano Basagni, Marco Conti, Silvia Giordao and Ivan Stojmenovic, Mobile ad hoc networking, IEEE press, Wiley interscience publication. [2] Perkins, C.; Belding-Royer, E. and Das, S., Ad hoc On-Demand Distance Vector (AODV) Routing, IETF, RFC 3561, July 2003. [3] David B. Johnson and David A. Maltz, Dynamic Source Routing in Ad hoc wireless networks, Mobile Computing, Kluwer Academic Publishers, 1996. [4] Bhargav Bellur, Richard G. Ogier and Fred L. Templin, Topology Dissemination Based on Reverse-Path Forwarding (TBRPF), RFC 3684, February 2004. [5] Philippe Jacquet, Paul Muhlethaler, Amir Qayum, AnisLaouiti, Laurent Viennot et al., Optimized Link State Routing Protocol (OLSR), RFC 3626.
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME 81 [6] Jianping Wanga, Eseosa Osagiea, Parimala Thulasiraman and Ruppa K. Thulasiram, HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network, Elsevier 2008, June 2008. [7] Zygmunt J. Haas and Marc R. Pearlman, The performance of query control schemes for the Zone Routing Protocol, IEEE/ACM Transactions on Networking, Vol. 9, No. 4, August 2001. [8] Joseph BIH, Paradigm shift – an introduction to fuzzy logic, IEEE Potentials, January – February 2006. [9] Yaozhou Ma, M. RubaiyatKibria and Abbas Jamalipour, A fuzzy logic based delivery framework for optimized routing in mobile ad hoc networks, Wireless Communications and Mobile Computing Conference, IWCMC 2008, p. 801-806. [10] Prasant Mohapatra, Jian Li and Chao Gui, QoS in mobile ad hoc networks, IEEE Wireless Communications, June 2003, p. 44-52. [11] Golnoosh G., Azadeh G., Arash D. and Mahmaz R., Reliable routing algorithm based on fuzzy logic for mobile ad hoc network, Proceedings of ICACTE 2010. [12] Taqwa Odey and A. Ali, Fuzzy controller based stable routes with lifetime prediction in MANETs, IJCN 2011, vol. 3, issue 1, p. 37-42. [13] M. Yaghmaei, M. Baradaran and H. Talebian, A Fuzzy QoS Routing Algorithm, IEEE conference on Communication networks and systems 2006, p. 1-5. [14] Alandjani Gasim and Johnson Eic E., Fuzzy routing in ad hoc networks, Performance, computing and communications conference 2003, IEEE international volume, April 2003, p. 525-30. [15] B. Sun, C. Gui, Q. Zhang and H. Chen, Fuzzy controller based QoS routing algorithm with a multiclass scheme for MANET, International journal of Computers, Communications and Control, 2009, vol 4, p. 427-438. [16] Mohamad Hadi Babaci Rochi, Arash Dana and Mahshid Ziyaee, A new source routing mechanism in MANET, IEEE ICACT 2011. [17] Shangchao P. and Baolin S., Fuzzy controllers based multipath routing algorithm in MANET, Proceedings of Applied physics and industrial engineering conference 2012, p. 1178-1185. [18] Junweiwang and Zhaoxia Wu, A fuzzy decision based intelligent QoS multicast routing algorithm, Proceedings of IEEE Automation and Logistics Conference 2011, p. 169-172. [19] A. Naga Raju and S. Ramachandram, Fuzzy cost based multipath routing for MANETs, Journal of theoretical and Applied information technology, p. 319-326. [20] G. Santhi and A. Nachiappan, Fuzzy cost based multi constrained QoS routing with mobility prediction in MANETs, Egyptian informatics journal 2012, p. 19-25. [21] V. Bapuji, R. Naveen Kumar, Dr. A. Govardhan and Prof. S.S.V.N. Sarma, “Maximizing Lifespan of Mobile Ad Hoc Networks with QoS Provision Routing Protocol”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 150 - 156, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [22] Thaker Minesh, S B Sharma and Yogesh Kosta, “A Survey: Variants of Energy Constrained Reactive Routing Protocols of Mobile Ad Hoc Networks”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2, 2012, pp. 248 - 257, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [23] Taniya Jain and Neeti Kashyap, “Factors for Designing Routing Protocol in Manet”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, 2013, pp. 189 - 193, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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