Computers and Software

ISSN 1828-6003 Vol. 9 N. 2 February 2014

International Review on

Computers and Software Contents:

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Conviction Based Packet Promotion Scheme for Efficient Detection of Selfish Nodes in Mobile Ad Hoc Networks by Mani P., Kamalakkannan P.

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(IRECOS) 212

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Fuzzy Logic with Hybrid Optimization Approach for Optimal Route Selection in MANET by N. K. Kuppuchamy, R. Manimegalai

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Collaborator Homed Routing: a Mechanism for Fault Free Routing on Wireless Sensor Network by Sasikumar M., R. Anitha

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A Robust and Hybrid Rician Noise Estimation Scheme for Magnetic Resonance Images by N. Sasirekha, K. R. Kashwan

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Efficient Intrusion Detection Ensuring Connectivity in 2D and 3D WSN by Mohamed Mubarak T., Appa Rao, Syed Abdul Sattar, Sajitha M.

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Rule-Based Semantic Content Extraction in Image Using Fuzzy Ontology by G. Nagarajan, K. K. Thyagharajan

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Integration of Improved Region Growing (iRG) and Level Set Method for Automated Medical Image Segmentation by Maya Eapen, Reeba Korah

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Decision Support System Using Fuzzy Min-Max Neural Network with the Modified Genetic Algorithm by R. Sathya Bama Krishna, M. Aramudhan

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Neurofuzzy and Genetic Network Programming Based Intrusion Detection System by Deepa A. J., V. Kavitha

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Capturing the Dynamism of Situation in the Flow of Information by Jules Chenou, Albert C. Esterline, William Edmonson

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Mining of Cyclic Periodic Patterns for Prediction System by N. Sumathi, S. Sathiyabama

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Highly Robust Digital Image Watermarking Using Steerable Pyramid and Dual Encryption Technique by Azz El Arab El Hossaini, Mohamed El Aroussi, Khadija Jamali, Samir Mbarki, Mohamed Wahbi

(continued on inside back cover)

Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved

International Review on Computers and Software (IRECOS) Editor-in-Chief: Prof. Marios Angelides Brunel University School of Engineering and Design Electronic and Computer Engineering Department Uxbridge - UB8 3PH U.K.

Editorial Board: (Japan)

Pascal Lorenz

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Francoise Balmas

(France)

Marlin H. Mickle

(U.S.A.)

Vijay Bhatkar

(India)

Ali Movaghar

(Iran)

Arndt Bode

(Germany)

Dimitris Nikolos

Rajkumar Buyya

(Australia)

Mohamed Ould-Khaoua

(U.K.)

Wojciech Cellary

(Poland)

Witold Pedrycz

(Canada)

Bernard Courtois

(France)

Andre Ponce de Carvalho

(Brazil)

David Dagan Feng

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Mikio Aoyama

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(Greece)

(Romania)

Erich Schikuta

(Austria)

(Australia)

Arun K. Somani

(U.S.A.)

Peng Gong

(U.S.A.)

Miroslav Švéda

(Czech)

Defa Hu

(China)

Daniel Thalmann

(Switzerland)

Michael N. Huhns

(U.S.A.)

Luis Javier García Villalba

(Spain)

Ismail Khalil

(Austria)

Brijesh Verma

(Australia)

Catalina M. Lladó

(Spain)

Lipo Wang

(Singapore)

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Dana Petcu

The International Review on Computers and Software (IRECOS) is a publication of the Praise Worthy Prize S.r.l.. The Review is published monthly, appearing on the last day of every month. Published and Printed in Italy by Praise Worthy Prize S.r.l., Naples, February 28, 2014. Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved.

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International Review on Computers and Software (I.RE.CO.S.), Vol. 9, N. 2 ISSN 1828-6003 February 2014

Fuzzy Logic with Hybrid Optimization Approach for Optimal Route Selection in MANET N. K. Kuppuchamy1, R. Manimegalai2

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Abstract – Independent, self-governing, mobile wireless hosts communicating through wireless links and forming a temporary network dynamically without centralized infrastructure are called Mobile Ad-hoc Networks (MANETs). As MANET nodes are not stationary, the same routing path may not always be taken between sender and receiver(s). Hence, such routing is complicated. Literature proposes different routing protocols each having advantages and limitations. This study proposes fuzzy logic with hybrid optimization approach for optimal route selection in MANET applications. The proposed hybrid optimization is based on Genetic Algorithm (GA) and Hill Climbing algorithm. Fuzzy rule system is based on actual end to end delay, when a node leaves the network, number of packets dropped and number of RRER generated. Simulation results demonstrate the efficiency of the proposed hybrid fuzzy routing when compared to Ad hoc Ondemand Distance Vector routing (AODV). Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Mobile Ad-Hoc Network (MANET), Routing, Ad Hoc On-Demand Distance Vector Routing (AODV), Fuzzy Logic, Genetic Algorithm (GA), Hill Climbing

I.

Introduction

The nature of swarms resembles MANETs and hence ideas from swarm animals like ants and bees are used to create suitable MANET routing protocols and Wireless Sensor Networks (WSN). They are more efficient, robust and discover multiple paths. AODV is an on demand approach using periodic “hello” broadcasts to track neighboring nodes resulting in network overheads [39]-[42]. A route should be discovered before actual data packet transmission in AODV. Such initial search latency degrades interactive applications [6] performance. Similarly, path quality is unknown before call set-up and is discovered only when setting it up. Also, path quality must be monitored by intermediate nodes in an active session leading to additional latency and overhead. AODV requires nodes to maintain list of active neighbors through forwarding of periodic HELLO packets or listening to neighboring nodes data transmissions in promiscuous mode. When a source node forwards a data packet to a destination node without a destination route, it initiates path discovery to locate a destination [7] route. For this, nodes maintain two monotonically increasing counters: a sequence number and broadcast id. Source broadcasts a route request (RREQ) packet to neighbors with source address (its own address), sequence number, broadcast id, destination address, last known destination sequence number, and a hop count with zero value. The pair consisting of address of the source and broadcast id identifies a RREQ. For routing table routes, a host maintains neighboring nodes list using that route informing them of potential link breakages with RERR messages.

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MANET is a wireless communication network in which the nodes not within transmission range establish communication with other nodes help to forward data [1]-[38]. It operates without fixed infrastructure, support user mobility coming under the general scope of multihop wireless networking. In such networks, nodes move arbitrarily without prediction. Each node acts as a transreceiver and locates network routes to aid the network form a complete communication route. Thus MANETs are dynamically establishing wireless networks, maintaining routes through network, forwarding packets to others to ensure multi-hop intra node communication not in transmission range. Present MANET routing protocols are classified into 4 basic categories, i.e. proactive routing, reactive routing, flooding and dynamic cluster based routing [2]. Flooding based routing needs no network topology knowledge. An ad-hoc routing protocol is distributed as nodes must be involved in route discovery ensuring reliable routing information and link costs [3]. In reactive protocols like Dynamic Source Routing (DSR) and Ad hoc On-demand Distance Vector routing (AODV), routing request is demand based: when a node plans to communicate with another, it broadcasts a route request and anticipates a response from destination [4]. Conversely, proactive protocols update routing information to ensure a permanent overview of network topology. An ad hoc network routing protocol comprises of a routing algorithm with rules monitoring network operation [5].

Manuscript received and revised January 2014, accepted February 2014

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N. K. Kuppuchamy, R. Manimegalai

Each node records individual routing table entries and deleting those unused recently [8]. AODV routing supports multicasting through constructing trees connecting multicast members with required nodes; smaller control and message packets lead to less network bandwidth overhead. Fuzzy logic is a useful tool to solve hard optimization problems with conflicting objectives where values of different criteria are mapped to linguistic values characterizing satisfaction level with objectives numerical values. The latter operate in interval [0, 1] based on each objective’s [9] membership function. According to three types of trust value; friend, acquaintance and stranger, they define three fuzzy sets: high, medium and low, respectively. MANET routing must consider important characteristics like node mobility. This study uses Fuzzy logic approach with GA along with a hill-climbing algorithm for optimal location of best route in routing issues. Results show that hybrid GAs performs better than original AODV. The rest of the paper is organized as follows. Section 2 discusses works related to MANET routing. Section 3 discusses GA and Hill climbing with the algorithm. Section 4 reports simulation and experimental results. Finally, Section 6 concludes the work.

Related Work

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II.

Misbehaving nodes were classified into three categories: malicious, misleading and selfish nodes all of whom are detected by HEAD, and isolated from the network. An algorithm different from previous works to solve Broadcast Storm Problem was proposed by Leuand Chang [13]. The algorithm suited dynamic MANET environment. All nodes randomly move around, leave network or switch off. Hence, broadcasting was used to diffuse data, routing or topology information in MANETs. Two algorithms, exhibiting trade-offs between simplicity, failure locality and response time were presented by Kogan [14]. The first algorithm has 2 variations, one being response time that depends weakly on system nodes and was polynomial in maximum neighboring nodes; the failure locality, though not optimal, was small and grew slowly with system size. The second algorithm has optimal failure locality/response time that was quadratic in nodes. An aspect of latter algorithm was that when nodes did not move, it had linear response time, improved on earlier results for static algorithms with optimal failure locality. Effects of flooding attacks in network simulation 2 were investigated and performance parameters, including average delay, packet loss ratio, throughput and average number of hops under different numbers of attack nodes, network bandwidth, flooding frequency and network size were measured by Yi et al., [15]. Simulation results showed that increase of flooding frequency and attack nodes led to network performance dipping. However, when both were greater than a certain value, performance degradation tends to be stable. An on-demand location-based anonymous MANET routing protocol (PRISM) that ensured privacy and security against external and internal adversaries was constructed by Defrawy and Tsudik [16]. PRISM’s security, privacy and performance were analyzed/compared to alternative techniques. Results showed PRISM to be more efficient with better privacy. A mathematical framework to evaluate performance of proactive and reactive routing protocols in MANETs is presented by Xu, et al., [17] which provides a parametric view of protocol performance, and in turn provided a deeper insight into protocol operations revealing the compounding and interacting effects of protocol logic and network parameters. Both classes of routing protocols essential behavior and scalability limit in network size were captured by the model which provided valuable guidance on performance of reactive/proactive routing protocols with varied network configurations and mobility conditions. The proposed model’s analytical results agreed with simulation results from discrete-event Qualnet simulations. A novel channel adaptive routing protocol which extended Adhoc On-Demand Multipath Distance Vector (AOMDV) routing protocol to accommodate channel fading was proposed by Chen, et al., [18]. The proposed Channel-Aware AOMDV (CA-AOMDV) used channel

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An adaptive routing Fuzzy based Balanced Power Aware Routing Algorithm (FBPRA) incorporating path maintenance mechanism and a stable route using fuzzy logic was proposed by Abirami, et al., [10]. Path maintenance mechanism reduced path breakages by establishing new path through neighboring nodes, before packet transmitted path broke due to node mobility. Performance evaluation was through simulation to compare the proposed fuzzy logic approach and classical methods. The results revealed that the algorithm improved network performance effectively. A survey of multicast routing protocols to provide a comprehensive understanding of multicast routing protocols designed for MANETs paving the way for further research was discussed by Tavliand Heinzelman [11]. Based on primary multicast routing selection principle all protocols can be placed under one of 2 outing selection categories: application independence based multicast routing and application dependence based multicast routing. A scheme titled HEADS (a hybrid mechanism enforcing node cooperation in MANETs) to make misbehavior unattractive was proposed by Guo, et al., [12]. HEAD is an improvement of Observation-based Cooperation Enforcement in Adhoc Networks (OCEAN) that uses first-hand information and works on top of Dynamic Source Routing (DSR) protocol. By interacting with DSR, HEAD detects misbehaving nodes in packet forwarding process and isolates them in route discovery. To detect misbehaving nodes HEAD introduced warning messages.


International Review on Computers and Software, Vol. 9, N. 2

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average nonfading duration as routing metric for stable links selection for path discovery applying a preemptive handoff strategy to ensure reliable connections exploiting channel state information. Paths can be reused using the same information when they are available rather than discarding them. Simulation/theoretical results showed that CA-AOMDV greatly improved network performance over AOMDV. A novel optimized data delivery framework - Fuzzy Logic based Delivery Framework (FLDF) using Fuzzy logic for ICAMNs message delivery was presented by Ma, et al., [19] where two nodes meet in the network and each employs Fuzzy logic to evaluate delivery preferences of possible messages based on fuzzy sets. Following evaluation nodes select/store messages with future higher delivery preference. Simulation demonstrated the proposed FLDF’s enhanced delivery performance over existing SCF schemes. A reliable fuzzy-logic based routing algorithm to find a reliable reactive protocol in MANETs was proposed by Ghalavand, et al., [20]. Nodes with more trust value and maximum energy are selected as routers based on a parameter called “Reliability Value” during route discovery. This formed a reliable source to destination route, increased network life and decreased packet loss during transmission. A novel approach called fuzzy ant colony based routing protocol using fuzzy logic and swarm intelligence for optimal path selection by considering multiple objectives optimization while retaining the swarm based intelligence algorithm’s advantages was proposed by Goswami, et al., [21]. Simulation revealed that the new protocol was superior to current swarm intelligence based routing protocols for MANET routing. A technique to analyze exposure to attacks in AODV, specifically Black Hole attack and to develop specification based Intrusion Detection System (IDS) using GA was proposed by Sujatha, et al., [22]. The new process was based on GA which analyzed every node’s behaviors and provided details of the attack. Genetic Algorithm Control (GAC) was sets of rules based on AODV’s vital features like Request Forwarding Rate, Reply Receive Rate and others. MANET performance analysis was GAC based. A fuzzy logic-based approach to averaging Angle Of Arrival (AOA) information was derived by Wang and Inkol [23]. The new approach showed that Maximum Likelihood (ML) approach to AOA averaging is considered a special fuzzy logic approach case. Computer simulations revealed that ML and fuzzy logic approaches produced similar results if fuzzy logic approach membership functions were appropriately constructed. An efficient Trust based Multipath Route Discovery with improved Route Lifetime algorithm (TSRD-RL) to provide trust based solution for the Security attacks which affects the routing protocol performance is proposed by S. Priyadarsini et al., [24]. The proposed algorithm is implemented in AODV and the performance

is evaluated. This protocol improves the network performance and reduces the computation overhead by avoiding frequent route discovery since secured stable multi paths with longest life time is selected. TSRD-RL protocol performs better than the existing stability-based routing protocols with improved packet delivery ratio.

III. Methodology

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MANETs routing must consider important characteristics like node mobility. Work on single path (or unipath) routing in MANETs was proposed. Multipath routing allows establishment of multiple paths between single source and single destination node. Multipath routing is proposed to increase data transmission (fault tolerance) reliability or to ensure load balancing which is important in MANETs due to limited bandwidth between nodes. Fuzzy logic is a classical logic set with degree of membership. Fuzzy Logic was designed to represent fuzziness and vagueness mathematically and provide fundamental concept to handle imprecision intrinsic to issues of subjective evaluation and measurement [25]. Fuzzy set is possibility based instead of probability. It maps input features to output based on data as “IF - Then” rules controller. Fuzzified input data trigger one/many rules in fuzzy model to calculate results. IF - THEN rules map input values to output space regarding implication relation between fuzzy sets in “IF” and “THEN” parts. In this diagram of a generalized fuzzy system is explained in Fig. 1. A key Fuzzy logic feature is handling uncertainties and non-linearity in physical systems, similar to reasoning by human beings, making it attractive for decision making systems [26]. Rule Base

Fuzzification Input

Knowledge Base

Defuzzification

Output

Membership function

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Fig. 1. A diagram of a generalized fuzzy system

A fuzzy logic system has 3 elements: (i) Fuzzification, (ii) Knowledge base (rule and function), and (iii) Defuzzification. Fuzzification determines the membership degree to a crisp input in fuzzy sets. Fuzzy rule base presents input-output fuzzy variables fuzzy relationship. The fuzzy rule base output is based on membership degree specified by fuzzifier. Defuzzification converts outputs to fuzzy rule base into crisp values. Fuzzy Logic Controller (FLC) has two inputs: message precedence and network status, and an output: routing decision. Rules are expressed in Mamdani form [27]: Ri: IF x is Ai and y is Bj THEN z is Ck



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TABLE I DEFINITION POINTS OF MBF "AE"

From Table II and Fig. 3 it is observed that the input variable MBF of “Nd” is given as graph which ranges from units of 0 to 1. Table III explains the definition points of MBF of node leaves the network (Nl) for different term names (decrease, steady and increase) along with its corresponding shape (Linear). From Table III and Fig. 4 it is observed that the input variable MBF of “Nl” is given as graph which ranges from units of 0 to 1. Table IV explains the definition points of MBF of no of RRER generated (Nr) for different term names (Low, Medium, High) along with its corresponding shape (Linear). From Table IV and Fig. 5 it is observed that the input variable MBF of “Nr” is given as graph which ranges from units of 0 to 1. Table V explains the definition points of MBF route preference for different term names (very_small, small, Medium, Large, very_large) along with its corresponding shape (Linear). From Table V and Fig. 6 it is observed that the input variable MBF of “Route Preference” is given as graph which ranges from units of 0 to 1.

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where linguistic variables x, y and z represent two process state variables and a control variable (two inputs and one output); Ai, Bj, and Ck are linguistic values (fuzzy sets specify meaning) of linguistic variables x, y, and z in universes of discourse U, V, and W, respectively. Linguistic variables used for fuzzy systems are actual end to end delay (Ae), when a node leaves the network (Nl), number of packets dropped (Nd) and number of RRER generated (Nr). Table I explains the definition points of MemBership Function (MBF) of End to End Delay (Ae )for different term names (Low, Medium, High) along with its corresponding shape (Linear). From Table I and Fig. 2 it is observed that the input variable MBF of “Ae” is given as graph which ranges from units of 0 to 1. Table II explains the definition points of MBF of no of packets dropped (Nd) for different term names (very_small, small, Medium, Large, very_large) along with its corresponding shape (Linear).

TERM SHAPE/PAR. DEFINITION POINTS (X, Y) NAME Low Linear (0, 1) (0.25, 1) (0.5, 0) (1, 0) medium Linear (0, 0) (0.25, 0) (0.5, 1) (0.75, 0) (1, 0) High Linear (0, 0) (0.5, 0) (0.75, 1) (1, 1)

Term Name

decrease steady

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increase

TABLE III DEFINITION POINTS OF MBF "NL" Definition Term Shape/Par. Shape/Par. Points Name (X, Y) linear (0, 1) (0.25, 1) (0.5, 0) (1, 0) (0.5, 1) (0.75, 0) linear (0, 0) (0.25, 0) (1, 0) linear (0, 0) (0.5, 0) (0.75, 1) (1, 1)

Fig. 2. Input Variable MBF of “Ae"

TABLE II DEFINITION POINTS OF MBF "ND" Term Name Shape/Par. Definition Points (x, y) linear

small

linear

(0, 1) (0.16666, 1)

medium

linear

Large

linear

very_large

linear

Fig. 4. Input Variable MBFof “Nl"

(0.33334, 0) (1, 0)

TABLE IV DEFINITION POINTS OF MBF "NR"

(0.33334, 1) (0.5, 0) (1, 0) (0.5, 1) (0.66666, 0) (0, 0) (0.33334, 0) (1, 0) (0.66666, 1) (0.83334, 0) (0, 0) (0.5, 0) (1, 0) (0, 0) (0.16666, 0)

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very_small

(0, 0) (0.66666, 0)

TERM NAME Low medium High

SHAPE/PAR.

DEFINITION POINTS (X, Y)

linear linear linear

(0, 1) (0.25, 1) (0.5, 0) (1, 0) (0, 0) (0.25, 0) (0.5, 1) (0.75, 0) (1, 0) (0, 0) (0.5, 0) (0.75, 1) (1, 1)

(0.83334, 1) (1, 1)

Fig. 5. Input Variable MBF of “Nr" Fig. 3. Input Variable MBFof “Nd"



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TABLE V DEFINITION POINTS OF MBF "ROUTE PREFERENCE" Term Shape/Par. Name very_low linear Low linear medium linear linear

very_high

linear

Definition Points (X, Y) (0, 0) (0.16666, 1) (0.33334, 0) (1, 0) (0, 0) (0.16666, 0) (0.33334, 1) (0.5, 0) (1, 0) (0, 0) (0.33334, 0) (0.5, 1) (0.66666, 0) (1, 0) (0.66666, 1) (0.83334, 0) (0, 0) (0.5, 0) (1, 0) (0, 0) (0.66666, 0) (0.83334, 1) (1, 0)

f  Chi    

Ch represents chromosome fitness value and Cd delay time of each chromosome where Cl represents path cost. Selection (reproduction) operator improves population’s average quality by giving high-quality chromosomes a chance to be copied into next generation. Crossover examines present solutions to find better ones. A partial route connects source node to an intermediate node, and another partial route connects intermediate node to destination node [30]. Crossover is not dependent on nodes position in routing paths. Loops are formed during crossover. Population undergoes mutation by actual change/flipping of one gene of candidate chromosome thereby avoiding local optima.

Fig. 6. Output Variable MBF of “Route Preference"

IF Nd very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small very_small

Nr Low Low Low Low Low Medium Medium Medium Medium Medium Low Low Low Low Low Medium Medium Medium Medium Medium

Hill-climbing (HC) is a mathematical optimization technique from the Local Search family. It starts with an initial solution (infeasible solution), which is then mutated and if mutation has higher fitness for the new solution than the earlier one, the new solution is retained; or else, current solution is retained. Hill-climbing starts with an infeasible solution continuing till a feasible solution is got; it then returns feasible solution to GA [31]. Hill climbing works by iteratively improving a solution through neighborhood transformations as long as possible. Hill climbing optimization has 4 input parameters like, objective function, starting points, range and step of the search. Search space for hill climbing is spanned by transformation parameter basis. Search space basis is usually an orthogonal set or non-degenerated [32]. Rigid body rotation is orthogonal. Rotation and translation are correlated, as rotation around an arbitrary point can decompose into rotation around origin plus a translation. Affine transformation is not orthogonal, but is nondegenerated. Algorithm for GA-Hill climbing: Create an initial population randomly for i from 1 to generation number for j from 1 to population size select parents create new_solution with crossover and mutation operators ifnew_solution is infeasible new_solution = Hill-Climbing(new_solution) end if end for create next population if stop condition is met stop the algorithm end if

THEN DoS RoutePreference 0.84 very_low 0.77 Low 0.09 Medium 0.86 High 0.99 very_high 0.98 very_low 0.09 Low 0.22 Medium 0.74 High 0.20 very_high 0.84 very_low 0.77 Low 0.09 Medium 0.86 High 0.99 very_high 0.98 very_low 0.09 Low 0.22 Medium 0.74 High 0.20 very_high

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Nl decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease decrease

III.2. Hill-Climbing

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Ae Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low

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The MIN_MAX rules can be explained in Table VI. IF part includes Ae, NI, Nd and Nr and THEN part includes DoS and Route Preference. TABLE VI MIN-MAX RULES

 lP s,r  Cl  Cd 

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High

It is important to ensure shortest path and lowest delay time as primary concern, then choosing is based on buffer size. Each chromosome’s fitness is calculated as:

III.1. Genetic Algorithm

GA is a stochastic algorithm based on natural selection and natural genetics principles successfully, applied in machine learning/optimization problems. GA maintains a population of individuals (called strings/chromosomes) probabilistically modifying population by genetic operators like selection, crossover and mutation, aimed at seeking a near optimal solution [28]. GA design involves many key components: population initialization, genetic representation, selection scheme, fitness function, crossover, and mutation. Each chromosome in GA represents a potential solution having more than one solution initially [29]. Paths from route discovery phase are considered initial chromosomes. Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved


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3

Retransmission Attempts (Packets)

end for Function Hill-Climbing (current_solution) whilecurrent_solution is infeasible do next_solution = expand current_solution if fitness of next_solution> fitness of current_solution current_solution = next_solution end if end while return current_solution end func

2,5 2 1,5 1 0,5

349,2

338,4

327,6

306

316,8

295,2

284,4

273,6

252

262,8

241,2

230,4

219,6

198

208,8

187,2

176,4

165,6

144

Time in s GA-Fuzzy

Hybrid Fuzzy

AODV

160000 140000

Data Dropped Bits/s

120000 100000

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Fig. 8. Retransmission Attempts (packets)

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IV.

Experimental Results

80000 60000 40000 20000

The proposed hybrid with GA and hill climbing is used to improve the initial rule-set as well as the membership functions of our routing unit. Figs. 7, 8 and 9 show the performance comparisons of AODV, fuzzy GA routing and proposed fuzzy approach. From Fig. 7 the improvement in the throughput is seen to improve by 5.33% due to the reduction in the packets dropped as nodes with high probability of leaving the network is ignored for the route formation if an alternate route is available. From Figure 8 shows the retransmission attempts made during the simulation time. As nodes leaving the network have low priority to act as an intermediate node, the number of packets retransmitted reduces by 4.44% and hence improving the throughput of the system. From Figure 9 it is observed that the number of bits dropped in a second is 10 times lesser in proposed approach.

122,4 133,2 144 154,8 165,6 176,4 187,2 198 208,8 219,6 230,4 241,2 252 262,8 273,6 284,4 295,2 306 316,8 327,6 338,4 349,2

0

GA-Fuzzy

Time in s Hybrid Fuzzy

AODV

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Fig. 9. Data dropped in bits/s

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Conclusion

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Adhoc networks have dynamic nature and problems like node mobility, power consumption, information delay, authentication etc. when communicating. Use of routing protocols resolves such problems. GA finds the shortest path in less time and is quicker than routing protocols. Fuzzy rule based system is formed by linguistic variables like end to end delay (Ae), when a node leaves the network (Nl), number of packets dropped (Nd) and number of RRER generated (Nr). The proposed model’s performance was compared to performance of original AODV. Performance analysis revealed that fuzzy genetic approach for routing improves performance than current AODV routing protocol.

1200000

Throughput in Bits/s

154,8

The method’s advantage is its simplicity. It was tried for optimum search in exponential domains in problems like GA and clustering. Synchronization between peer nodes containing data and a central node collecting sufficient statistics is required at each hill-climbing heuristic [33] step. The algorithm starts with a random schedule. It makes small changes to the schedule sequentially, improving it a little bit each time. The algorithm reaches a point where it sees no improvement and then it terminates [34]. At that point, ideally, a schedule is found close to optimal without a guarantee that hill climbing will come near the optimal solution.

133,2

122,4

0

1000000

800000 600000 400000 200000 0

122,4 133,2 144 154,8 165,6 176,4 187,2 198 208,8 219,6 230,4 241,2 252 262,8 273,6 284,4 295,2 306 316,8 327,6 338,4 349,2

References

GA-Fuzzy

Time in s Hybrid Fuzzy

[1]

AODV

[2]

Fig. 7. Throughput in bits/s


Wang, Z., Li, C., & Chen, Y. (2011, December). PSR: Proactive Source Routing in Mobile Ad Hoc Networks. In Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE (pp. 1-6). IEEE. Mehedi, J., &Naskar, M. K. (2012). A Fuzzy Based Distributed Algorithm for Maintaining Connected Network Topology in


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[7] [8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35]

[36]

[37]

[38]

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[19]

[26]

[20]

[21]

[22]

[23]

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Authors’ information 1

VidyaaVikas College of Engineering and Technology, Tiruchengode, India. 2

Park College of Engineering and Technology, Coimbatore, India. N. K. Kuppuchamy received the master degree in Computer Science and Engineering in 2008. He is a research scholar of Anna UniversityChennai. Currently, he is a faculty at Vidyaa Vikas College of Engineering and Technology, Tiruchengode. His interests are in MANET Routing using genetic algorithm Approach. E-mail: [email protected]

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Dr. R. Manimegalai received the master degree in Computer Science and Engineering from the Anna University, Chennai, in 1999 and Ph.D. in Computer Science and Engineering from the IIT, Chennai in 2007. Currently, she is a professor at Park College of Engineering and Technology, Coimbatore. Her research interests are in Reconfigurable Computing and

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Distributed Systems. E-mail: [email protected]



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Abstracting and Indexing Information: Cambridge Scientific Abstracts (CSA/CIG) Academic Search Complete (EBSCO Information Services) Elsevier Bibliographic Database - SCOPUS Index Copernicus (Journal Master List): Impact Factor 6.14 Autorizzazione del Tribunale di Napoli n. 59 del 30/06/2006

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