BAT-based Optimized Routing Protocol in Cognitive Radio Ad Hoc Network

8/20/2019 BAT-based Optimized Routing Protocol in Cognitive Radio Ad Hoc Network

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International Journal of Application or Innovation in Engineering& Management (IJAIEM)Web Site: www.ijaiem.org Email: [email protected]

Volume 4, Issue 12, December 2015 ISSN 2319 - 4847

Volume 4, Issue 12, December 2015 Page 116

ABSTRACT Different kinds of optimization algorithm have been developed based on different context and i t is highly essential in field of communication, particularly in routing protocol in Cognitive Radio Ad Hoc Network. Many authors have placed their optimized routing techniques in their application to make the communication in bet ter and efficient way. In this paper, i t has been discussed the BAT algorithm optimization technique and implement the technique in the field of routing protocol inCognitive Radio Ad Hoc Network to make the better and optimize one. It has been designed a new routing protocol CRAODV-Opt and compared with conventional routing protocol CRAODV in Cognitive Radio Ad Hoc Network environment. The

performance of proposed routing protocol is observed better than existing one.Keywords: Cognitive radio (CR); Routing; Optimization; CRAODV-Opt; BAT algorithm;

1. INTRODUCTION Now a day, optimization is widely used in the routing problem. Optimization is the study of planning and designing problems using mathematical tools to find best solutions and optimally use all the valuable resources. It is considered asmaximization and minimization problem. In general, optimization problem is defined as[19]:

Minimize f i(x) i=(1,2,…,M )subject to h j(x)=0, j=(1,2,..., J )and g k (x)≤0, k =(1,2,...,K )

ORMaximize f i(x) i=(1,2,…,M )subject to h j(x)=0, j=(1,2,..., J )and g k (x)>=0, k =(1,2,...,K )

where f i(x), h j(x) and gk (x) are the functions of design vector and x is the decision variables[21]. The function f i(x)where i= 1,2 ,3,……,M is the objective function. The equalities of h jand the inequalities for gk is known as constraints[19]. The inequality is also defined as greater than and equal to zero and the objectives are formulated as maximization problem.Cognitive Radio (CR) is projected to use radio spectrum enthusiastically. Every single node in CR has transceiver toexamine spectrum in order to discover which spectrum is exploited and which is not. CR is the technology that permits a cognitive radio ad-hoc network (CRAHN)[21] to exploit the spectrum in a dynamic approach. The crucial

idea of the cognitive radio is to gain the most prominent accessible spectrum. CRAHN is one of the classifications ofCR network [21]. It does not have any communication stamina. Thus, a CR consumer can communicate with other CRusers on both licensed and unlicensed spectrum bands throughout the ad hoc connection. Particularly it is better to useoptimized routing protocol in CRAHN. CRAODV-Opt, an optimized routing protocol has been developed by usingmeta-heuristic Bat algorithm and compared with existing CRAODV in CRAHN environment.The rest of the paper is as follow. Optimization Algorithms discussed in the section 2, followed by Optimal RoutingProtocol in Cognitive Radio Ad-Hoc Network in section 3. Bat algorithm has been discussed in section 4. Section 5discussed optimized routing protocol in CRAHN, followed by result and discussion in section 6. Finally it has beenconcluded in section 7.2. O PTIMIZATION ALGORITHMS To solve the optimization problem, efficient search and optimization algorithms are required. There are manyoptimization algorithms which are divided depending upon the characteristics [21,22] as in fig.-1.

BAT-based Optimized Routing Protocol inCognitive Radio Ad Hoc Network

SangitaPal 1,Srinivas sethi 2

Department of CSEA, IGIT, Saranag, Orissa.






Fig.-1: Different kinds of Optimization Approaches Based on the focus:

Gradient based algorithm is very efficient algorithm which uses derivative information.For example hillclimbing. As the function has discontinuities it becomes difficult to calculate the derivatives accurately.

Gradient free algorithm is very useful as it uses the values of the function instead of derivative information. Depending on the perspective:

Trajectory-based algorithms: As the iteration continues, it uses only one solution and trace out a path. Forexample hill climbing which relates the source point with the destination point in a crisscross way.

Population-based algorithms: It uses more than one solution which traces out multiple paths such as ParticleSwarm Optimization.

Depending on the nature: Deterministic algorithms: It follows a thorough procedure and it repeats the pathway and values of both

variables and functions. The algorithm reaches the same final solution for the same initial point every time thealgorithm is executed. For example hill climbing, where it follows the exactly same path for the same starting point as the program executes. Stochastic algorithms: It always follows some randomness. The algorithm reaches the different final solution forthe same initial point every time the algorithm is executed. For example genetic algorithm and particle swarmoptimization. It is again divided into two levels heuristics and meta-heuristics.

Heuristics: It means to discover or to find by trial and error in order to produce feasible solutions to a complex problem within a stipulated given period of time. This algorithm is efficient and produces quality solutions. Butsometimes it does not produce best solutions and even there is no guarantee that the algorithm will work.

Meta-heuristics: It is the higher level of heuristic algorithm. It uses tradeoff of randomization and local search.This algorithm produces quality solutions within the given period of time, but still there is no guarantee that itwill work all the time. Hence, it is mostly suitable for global optimization. For example genetic algorithm and particle swarm optimization. Hybrid algorithms: This algorithm is the mixture of both deterministic and stochastic algorithms. It usesdeterministic algorithms but the only difference is that it initiates with different source points. For example, hillclimbing when it starts randomly.

Based on the searching: Local search algorithms: It is a meta-heuristic method generally used for solving complex problems such assimple hill climbing. It converges towards a local optimum and this algorithm is often called as deterministicalgorithm and has no ability to escape from local optima.

Global search algorithm: Mostly meta-heuristics algorithms are suitable for global optimization. For example,hill climbing with random restarts turns the local search algorithm into global search capability.

3. O PTIMAL R OUTING P ROTOCOL IN C OGNITIVE R ADIO AD-H OC NETWORK (CRAHN)

The resource allocation and data routing problem in the dynamic spectrum access environment is an optimization problem. Different techniques like computational intelligence paradigm, game theory, meta-heuristic, combiningconcepts, etc., are used to maximizing resource utilization which is discussed by different authors for different contexts.Evolutionary algorithms (EA) can be applied to obtain approximate solutions for optimization problems in a rational






amount of computation time[3].Forexample, genetic algorithms (GA), ant colony optimization (ACO), BAT algorithm,artificial bee colony (ABC), and particle swarm optimization (PSO) are belongs to this category. Similarly, GameTheory provides various favorable criteria for the spectrum sharing problem among network users (both PU andSU)[1][2].Genetic algorithm (GA) is a searching method used to find the near solutions to the optimization problems[5]. GA is

mainly inspired by inheritance, mutation, natural selection, and crossover which belong to evolutionary biology. Theroute/ paths from source to destination can be discovered and selected using fitness function of genetic algorithms[6].At the network layer, throughput optimization sub-problem is solved by GA for finding the optimum solution[4]. Thegenetic algorithm is used for optimal resource allocation which maximizes the throughput.Swarm Intelligence (SI) techniques are evolving computational intelligence paradigm combining the concepts and principles from biology, social sciences and mathematics which is generally used for several optimization problems.The collective behaviour of distributed and self-organized swarms is performed in SI[12].Ant colony optimization (ACO)[8] is one of the probabilistic SI method inspired by the behaviour of ant to solvecomputational problems. Generally, it is used to find better paths, by using graphs from colony to the food. The designof the ant colony approach is to imitate the behaviour with “simulated ants” walking around the graph which representsthe defined problem to solve in an optimal way. This approach acknowledges the “pheromone trails” emulation. Otherants follow the trail to reach at their colony instead of travelling at random. The pheromone trail starts to disappearover time and indicates its reducing strength. However, pheromone evaporation has the advantage of avoiding theconvergence to a locally optimal solution. ACO approach can be employed in networks to improve the performance ofrouting protocols[9].Due to dynamic nature of CRN to get optimization solution, ASAR (Ant-based Spectrum Aware Routing) [7] is analgorithm which is robust, adaptive, and self-learned in multi-hop CRN. It is a new routing solution which can observeand learn the dynamic network and then select the route. It discovers route, perform observation and learn by SItechniques. F-ant is used to discover possible paths from source to sink and B-ant is used to collect the information onthe network and revise routing table of the nodes. A strengthening learning function is used to learn the dynamicnetwork. Hence this algorithm controls routing overhead as well as minimizes end-to-end delay.Particle Swam Optimization (PSO) is an evolutionary algorithms (EA) in which optimization is achieved bycooperative behaviour of individuals instead of emphasizing on “survival of the fittest” principle among several population. It is not affected by the nonlinearity and size of the problem. Hence, it can be used to solve multi-objectiveoptimization and combinatorial problems in networking [13, 14], due to which, it can meet to the optimal solution. Itcan also be applied on spectrum sensing and resource allocation in the CRN[15, 16]. Binary Particle SwarmOptimization (BPSO) is used to maximize the total capacity and minimize the power consumption in CRAHN [15].Similarly, PSO is used to assign Orthogonal Frequency Division Multiplexing (OFDM) carriers to SUs to maximizesum rate capacity of the CRN under interference and power constraints[16]. Binary Particle Swarm Optimization(BPSO), Socio Cognitive Particle Swarm Optimization (SCPSO) and Derivation 0 are three variants of PSO, are usedto maximize the throughput for the number of secondary links, which can be acknowledged in the CRN in theoccurrence of primary links under interference constraints[11].Artificial Bee Colony (ABC) is another kind of algorithm, which mimics the foraging activity of honeybees, based onthe exchange of information among bees to get group information[17]. A swarm of virtual bees are generated those areallowed to move arbitrarily in the search space. They interact when finding some target of food source. The possiblesolution to the optimization problem treated as location of a food source and the quality or fitness of the associatedsolution treated as the amount of a food source. The employed bee is considered as solution in the population. The

survival and progress of the bee colony are dependent upon the fast discovery and proficient use of the best foodresources and can be implemented in real time problems at a low computational cost.BAT Algorithm is a meta-heuristic approach based on the echolocation behavior of bats. Frequency and sound tuningtechnique have used in this algorithm to control dynamic behaviors of swam of bats. It is potentially more powerfulthan PSO, GA and Harmony Search[18]. It is used for continuous constrained optimization problems.Cognitive radio techniques improve their performance and optimize the spectrum usage by applying intelligentlearning and searching techniques. Some traditional methods may not perform better to solve optimization problems ina scalable and fast fashion especially when the size of the network increases. Heuristic Harmony Search (HS)algorithm[10] reduce the bandwidth requisite for exchanging information among the nodes. But, the performance ofthe distributed algorithm degrades slightly. It also minimizes the computational complexity and optimizes efficientlythe channel assignmentGame Theory is a mathematical tool that analyzes the strategic interaction among multiple decision makers. Theimportances of studying CRN in a game theory framework are:

To model dynamic spectrum sharing among users like PUs and SUs as games then analyze the behaviours andactions of users in a formalized game structure, through which the achievements in game theory can be fullyutilized.






The optimum spectrum usage is a multi-objective optimization problem and Game Theory provides optimalitycriteria to compute game optimality under different game settings.

The non-cooperative Game Theory uses only local information to derive proficient distributed approaches for dynamicspectrum sharing. It is highly desirable in CRAHN because in CRAHN elasticity self-organized approaches arerequired and centralized control is not accessible.

The cooperative game theory analyzes best possible strategies and advances their positions in a game, by enforcingcollaboration between them. In a non-cooperative spectrum sharing game, each user pays attention for their own benefitand chooses an efficient approach that can maximize their payoff function [1].Coalitional game is a supportive game describing how a set of players cooperate with others, by creating cooperatinggroups and hence it improves their payoff in a game. SUs communally identify the activities of the PUs and sense thespectrum for accessing empty spectrum bands. The games are designed for the allocation of the resources and provideSUs to support and create grand coalition [2].As it is potentially more powerful than PSO, GA and Harmony Search, itis considered the Bat algorithm in this problem.4. BAT ALGORITHMXin-She Yang[18] proposed a BAT algorithm which is based on echolocation behavior of micro BAT. The maincharacteristic of micro bat is; it could search its prey/food and distinguish various types of pray by avoiding obstacles. BAT sends signal with loudness of frequency 20 kHz to 200 kHz to the objects with variable wave length.Their signal

bandwidth differsas per the species, and increased by using more harmonics. The distance S can be calculated whensignal bounces back to the BAT after striking the objects. The BATselects the object with minimum distance. TheMicro BAT also uses the factors like time delay from production and recognition of deflection, loudness variations ofthe deflection, time variation of its two ears. So, it can discover both distance and orientation of the object, types ofobjects, size and speed of objects.Bats fly randomly with velocity at position with a fixed frequency, varying wavelengthor variable frequency with fixed wavelength and loudness to search for prey.5. PROPOSED MODELIn this section, it has been discussed the adaptation of the BAT algorithm to make better routing protocol in CRAHN.The SU is treated as BAT and its population size is numbers of BAT in the network. The loudness of BAT in terms offrequency is A, and the pulse rate of BAT is R. The SU will send the signal or information at a frequency range from25 KHz to 250 KHz at a rate of 8 to 10 m/s.

In this segment, the proposed routing protocol Cognitive Radio Ad hoc On-demand Distance Vector using BAT

algorithm (CRAODV-Opt) has been delivered by describing the route discovery and route maintenance process withoptimized channel selection using BAT algorithm. The proposed optimized routing protocol CRAODV-Opt is designedfor efficient route discovery process using sets of better channel in the route from source to destination in the network.It has been used the necessary functions of control packets like RREQ, RREP, SU_RREQ, SU_RREP and HELLO,RERR, which are similar to CRAODV [23][24] and AODV[25]. The PU-RERR and proper utilization of the spectrum by protocol have been also implemented. The proposed protocol has been compared with conventional CRAODV.

The RREQ is generated and flooding by SU which is treated as BAT in CRAHN and transmitted from the nodes toother nodes in the network to find the route information during the route discovery process. The originating nodespreads a RREQ message to its neighbouring nodeswith defined time to live (TTL) and a specified hop distance, whichfurther communicates to their neighbour nodes if destination node not found. If none of intermediate nodes has theroute information of sink node, they retransmit the RREQ with a next hop number. This process continues until itreaches to the destination node. Then it sends a RREP message with priority of the channel of PU. The priority is

calculated based on occurrence of primary users at its different channels (TIME-ON of primary users) and packet errorrate (PER) to the originated node with the complete stored route information in it. The SU select the channel accordingto highest priority. This may helps to establish the route from source to destination node and transfer the data throughthe established path. Nodes along with the route can be updated their routing table entries.

Now it has been discussed the route request by secondary users (SU_RREQ), which is important for CRAHN to find outthe better channel in the route. It will verify whether a licensed channel is free from PU activities or not. Afterreceiving a SU_RREQ message through the predefined channel it drop the RREQ packet if the licensed channel is notfree from PU activity. Otherwise, it creates a reverse route reply through same channel. A node can keep numerousroutes in CRAODV-Opt. The SU verifies the presence of a PU activity before transfer a packet using a channel. It sendsa secondary user route reply (SU_RREP) to the source node using the same channel. Otherwise, it broadcasts a copy ofthe SU_RREQ message packet through the channel and it provides to check the duplicate SU_RREQ packet anddiscard it if found. Secondary user route reply (SU_RREP) is used in the CRAHN to reply the destination informationregarding PU activity on a channel. The node updates the opposite path and sends SU_RREP through intermediatenodes in the CRAHN.






It built up a path through the same free channel of nodes to the source node as per the priority value of channel. Then itforwards the SU_RREP message along the opposite path using specified channel. The SU will update the onward pathonly if the new SU_RREP message is better forward route received using the same channel.

Similar to route discovery process in the CRAHN, the route maintenance process can be performed as it is a vital rolein routing protocol. There are two classes of route error (RERR) message in CRAODV-Opt. RERRs is one of them fortopology alterations in such dynamic network as it is ad hoc in nature and other one is primary user (PU-RERR) forhandling activity of the PU. The node in the network cancels all the route records using an accessed channel and itnotices the neighbour SUs that used licensed channel is inaccessible with a PU-RERR message when a PU activity isidentified through a node in a same channel. The SUs get a packet nullify the used channel route entries whosesucceeding hop is the PU-RERR sender. In this way, SU nodes in the network affected by the PU activity mustwithdraw from the eventful channel.

In this way the route discovery and maintenance can be performed using channel which make it more efficient andeffective. The simulations have been carried out by using simulations through NS-2 [29], based on the CRCNintegrated simulator [30] and evaluate the performance of protocol through throughput, end-to-end delay, packetdelivery ratio(PDR) and normalized network routing load(NRL) as per the table-1.

TABLE-1: Simulation Parameters for CRAODV-Opt and CRAODV

S.No Parameters Values

1 Area size 500m x500 m.2 PU Transmission range 250 m.3 Simulation time 500 s.4 Nodes speed 5 m/s5 Pause times 5 s.6 Data rate 5 Kbps7 Mobility model Random any point.8 Interface 19 No. of channel 5

10 Numbers of SU Node 10,20,30,40,50,60,70,80,90,10011 Numbers of PU Node 1012 No. of Simulation 5

The parameters NRL, Delay, PDR, Throughput, which are used to evaluate the performance of routing protocol can beobtained through the Trace Analyzer in NS2. The total amount of routing loads per total delivery packets in thenetwork system is treated as NRL. The delay refers as the average time taken to receive the data at destination nodefrom source node in the network. PDR is used to calculate the ratio between the number of successfully received packetsat destination nodes and the number of all packets sent by sources nodes. The throughput is the rate at which a networkreceives data packets at receiver end at a particular time period.

6. RESULT AND DISCUSSION

In this paper, BAT algorithm based optimized routing protocol has been designed in the CRAHN and makes therouting protocol more reliable and efficient one. It has been tried to improve the PDR and throughput, which denotesthe efficiency and reliability of routing protocol and good channel efficiency with low routing load and end to enddelay.As per fig.-2, the proposed optimized routing protocol (i.e., CRAODV-Opt) has less routing load as compare toconventional protocol CRAODV. This indicates average network overhead is less as compared to basic cognitive radiorouting protocol CRAODV. The packet collision may be less in the proposed model for CRAHN environment due toless routing overhead. This is significantly better indication of proposed routing protocol in CRAHN.As per fig.-3, the proposed routing protocol CRAODV-Opt uses less transmission time to transmit the data in thenetwork as compared to CRAODV. Similarly,optimized routing model CRAODV-Opt in CRAHN shows better resultsin term of PDR as compared to conventional one (i.e., CRAODV) which is shown in fig.-4. The throughput has beenconsidered for better channel utilization for evaluation of proposed routing protocols, which has been analyzed throughthe fig.-5. It has beenobserved the performance in terms of throughput of CRAODV-Opt is better as compared toCRAODV.






F IG .-2: NRL VS NUMBER OFSUS F IG .-3: DELAY VS NUMBER OFSUS

F IG .-4: PDR VS NUMBER OFSUS F IG .-5: THROUGHPUT VS NUMBER OFSUS

7. CONCLUSIONIn this paper a proposed optimized routing protocol CRAODV-Opt has been developed in the CRAHN environment.The proposed CRAODV-Opt select the optimized and reliable channel selection by using BAT algorithm for better andoptimized route construction. Due to this novel technique the route formation in proposed model CRAODV-Opt is better than the existing routing protocol. It makes the optimized channel selection process in the network system withmore numbers of packet are transmitted as compared to conventional one (ex. CRAODV). It improves the PDR and

system throughput with minimum delay and network routing load as compared to a conventional routing protocolCRAODV in CRAHN environment.REFERENCES[1]. Beibei Wang, Yongle Wu, K.J. Ray Liu,” Game theory for cognitive radio networks: An overview”, in Elsiver

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AUTHORSangita Pal received the B.Tech. andM.Tech. degrees in Computer Science Engineering in 1993 and 2010,respectively.She has 17 years of teaching experience. She has numbers of journal and conference publications.






Srinivas Sethi received MCA and Ph.D. degree from Berhampur University and continue as Faculty in the Departmentof CSEA since 1997. He has numbers of good journals and conference publications.

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BAT-based Optimized Routing Protocol in Cognitive Radio Ad Hoc Network