Hybrid Glowworm Swarm Optimization (HGSO) agent for Qos based Routing in Wireless Networks

Transcription

1 Hybri Glowworm Swarm Optimization (HGSO) agent for Qos base Routing in Wireless Networks T. Karthikeyan1 an B. Subramani2 1Associate Professor, P.S.G. College of Arts an Science, Coimbatore, 2 HoD, Dr.NGP Arts an Science College, Coimbatore. ABSTRACT: Nowaays, the rapily eveloping science of services focuses on service management, which has to take into account the available resources an the user's wishes concerning the esire quality an costs. Quality of Service (QoS) management is perceive as a special aspect of istribute systems management in WSN. Proviing QoS aware routing is a challenging task in this type of network ue to ynamic topology an limite resources. The main purpose of QoS aware routing is to fin a feasible path from source to estination which will satisfy two or more en to en QoS constrains. Therefore, the task of esigning an efficient routing algorithm which will satisfy all the quality of service requirements an be robust an aaptive is consiere as a highly challenging problem. Intelligent software agents are employe to monitor the changes that occur in network structure, network communication flow an each noe s routing state. These agents can after that participates in network routing an network maintenance. Swarm intelligence techniques have been extensively use in optimization problem for solving many optimization issues in WSNs. To solve this problem previously Hybri Genetic Firefly Algorithm (HFGA) was propose which can be use for solving optimization problems. However, parameters are set fixe an they o not change with the time. In aition Firefly algorithm oes not memorize or remember any history of better situation for each firefly an this causes them to move regarless of its previous better situation, an they may en up missing their situations. In orer to avoi this problem, in this work a new efficient an energy aware multipath routing algorithm esigne calle as a novel hybri glowworm swarm optimization (HGSO) algorithm. The presente algorithm embes preatory behavior of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm. This propose Routing Algorithm will increase network lifetime an ecrease packet loss an average en to en elay that makes this algorithm suitable for real time an multimeia applications. The results show that HGSO algorithm has faster convergence spee, higher computational precision, an is more effective for solving constraine engineering esign problems. Keywors: Wireless Sensor Network, Routing Algorithm, Synthetic Qos, Agent, Artificial Fish Swarm Algorithm, Glowworm Swarm Optimization 1. INTRODUCTION A Wireless Sensor Network (WSN) is compose of a large number of small evices with limite power, processing an communication capabilities that are ensely eploye insie a phenomenon or very close to it [1]. Sensor noes have two main functionalities: monitor the environment an sen the sense ata to a special noe, calle the sink. Sensor noes can sen the monitore ata perioically (perioic ata reporting) or when an event occurs (event-base reporting). Some applications (e.g. Forest fire monitoring) nee a mixture of both perioic an event-base ata reporting. In this case, each sensor noe monitors the environment an besies sening perioical measurements to the sink, it also informs the sink when a specific event occurs. There are multiple types of packets that flow through a WSN for a mixe ata reporting application. Perioical ata reporting an event-base packets are the two main packet types. The event-base packets usually alert the sink when a critical event occurs, an therefore these packets have to be transmitte as soon as possible, with higher priority than the perioic ata reporting. Many routing protocols for WSNs have been propose in the literature, but there are some challenges that have not been resolve yet. One of them is integrating Quality of Service (QoS) requirements in the routing protocols for mixe ata reporting applications. Due to the ynamic nature of the network, the existing QoS protocols for wire networks cannot be applie irectly to WSNs. Congestion control mechanisms are essential in WSNs. A typical sensor network comprises a large number of multifunctional, low-cost, an low-power noes that are eploye ensely an ranomly in an environment for monitore sensing to control the environment, perform local processing, an communicate results with a base station that performs most of the complex processing. One of the many challenges concerning wireless sensor networks (WSNs) is how to provie Quality of Service (QoS) parameter guarantees in real-time applications. Several approaches an protocols have been propose in the literature for QoS parameter support in these types of networks [2, 3]. Energy consumption is consiere to be the most important constraint in WSNs because of the low power an the processing factors. These factors reuce the QoS an the lifetime of the network. The primary concern is how to properly use resources (for eriving multimeia content) to provie appropriately share ata among all of the transmission raios while maintaining a proper level of imaging an vieo ata transmission. The main goal is the 2011

2 appropriate use of multimeia resources by properly maintaining a level of optimize QoS, which further epens on the performance of the raio. This goal requires careful processing to achieve optimal en-to-en elay, jitter, an energy consumption, as well as acceptable throughput. Different applications of real-time WSNs have ifferent QoS priorities base on the performance of the transmission raio. The requirements epen on the situation for which the application uses the raio service. It is important for each sensor noe in the network omain to consier resource allocation as an optimization problem with ifferent potential goals. First, a sensor shoul attempt to optimize source-base capabilities to maximize its use of resources. Secon, a sensor shoul consier resource utilization from a perspective of nee, that is, the hop information. Thir, resource allocation shoul be consiere from a global perspective in which the utilization of resources by all of the sensor noes is consiere [4]. Thus, the question raise is how to balance the use of resources an transmission raio to provie optimal QoS parameters as well as to avoi the overuse of resources. Swarm intelligence is a relatively novel fiel. It aresses the stuy of the collective behaviors of systems mae by many components that coorinate using ecentralize controls an self-organization. A large part of the research in swarm intelligence has focuse on the reverse engineering an the aaptation of collective behaviors observe in natural systems with the aim of esigning effective algorithms for istribute optimization. These algorithms, like their natural systems of inspiration, show the esirable properties of being aaptive, scalable, an robust. These are key properties in the context of network routing, an in particular of routing in wireless sensor networks. In this work propose an a novel agent-assiste Quality of Service (QoS) routing algorithm of Wireless Sensor Networks (WSNs) base on Hybri Glowworm Optimization algorithm (HGSO). This HGSO algorithm hybris, preatory behavior of artificial Fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm for Qos routing in WSN. HGSO uses the constraint processing technology base on feasibility rules to upate the optimal location of the population, which makes the population rapily convergence to feasible regions an fin better feasible solution. The rest of the paper is organize as follows: In Section 2, escribe the existing Qos routing protocols. In Section 3, AFSA, GSO is simply escribe an the HGSO hybri strategy is propose an explaine in etail. Simulation an comparisons base Qos routing base on HGSO are presente in Section 4 an in the en some conclusions in Section RELATED WORK QoS routing has receive attention recently for proviing QoS for multimeia traffic in wireless a hoc networks an some work has been carrie out to aress this critical issue. Here, we provie a brief review of existing work aressing the QoS routing issues in wireless a hoc networks. Multiple paths routing has to achieving a goo trae-off between success probability in route acquisition an protocol overhea. It works by searching multiple paths in parallel for a QoS path. Most of the routing protocols for mobile a hoc networks, such as [5], DSR [6], an TORA [7], are esigne without explicitly consiering quality-of service of the routes they generate. QoS routing in a hoc networks has been stuie only recently [8, 9]. QoS routing requires not only fining a route from a source to a estination, but a route that satisfies the ento- ening QoS requirement, often given in terms of banwith or elay. Quality of service is more ifficult to guarantee in a-hoc networks than in most other type of networks, because the wireless banwith is share among ajacent noes an the network topology changes as the noes move. This requires extensive collaboration between the noes, both to establish the route an to secure the resources necessary to provie the QoS. For selection of the QoS, base on the parameter constraine in the network is very ifficult. In many research papers, they propose fuzzy logic technique for QoS preiction. It has the better performance comparatively other technique, but the inputs to the fuzzy system are uncertain [10, 11]. So, we won t get require QoS. We were seen in many applications, the uncertain ata sets are tune to require one by using neural back propagation algorithm an hence in this paper, we use error back propagation algorithm [12]. The first routing algorithms base on swarm intelligence concepts ate back to the secon half of the 90s an were esigne for wire networks. Schoonerwoer s et al. Ant-Base Control (ABC) [13] aresse circuit-switche telephone networks, while the AntNet algorithm of Di Caro an Dorigo [14, 15] was meant for best-effort IP networks [15]. More precisely, both these algorithms were evelope accoring to the principles of Ant Colony Optimization (ACO) [16], a popular metaheuristic for optimization. ACO erives from the reverse-engineering an the aaptation of the shortest path behavior observe in foraging ant colonies [17]. This behavior results from the combine ability of the ants of marking their paths by laying pheromone signals an, at the same time, searching the most promising foraging areas by moving towars the irections locally marke by higher pheromone intensity. The ACO principles that are at the roots of ABC an AntNet have guie, in turn, the esign of a number of other SI algorithms for routing in a variety of ifferent network environments. A etaile iscussion on the mechanisms at work in ACO-base routing, as well as an 2012

3 extensive overview of the characteristics an the performance of a number of ifferent routing algorithms, an in particular of AntNet an of its ifferent versions, were first compile in the Ph.D. thesis of its ifferent versions, were first compile in the Ph.D. thesis of Di Caro [18]. More recently, Farooq an Di Caro [30] have presente a comprehensive review of the most prominent routing algorithms for wire networks an MANETs that have been inspire by insect societies (ants an bees). The review highlights the characteristics specific to SI-base routing protocols an shows why these characteristics make these protocols particularly suitable to eal with the challenges pose by next generation networks. In the review, the authors first efine a novel taxonomy for routing algorithms that takes into account an extensive number of ifferent aspects (e.g. eterministic vs. probabilistic ecisions, global vs. local representations, single-path vs. multiple paths, etc.), an then iscuss the ifferent algorithms with respect to the new taxonomy. They also elaborate the reasons for goo performance of SI-base algorithms as observe in the simulation stuies. However, the authors of [19] also point out the lack of performance evaluations base on the use of real evices an testbes. This fact makes har to assess the effective performance of these algorithms. The comprehensive survey of Wee an Farooq [20] focuses on routing algorithms for wire networks. The main objective of the survey is to unerstan the basic esign principles an the core ifferences existing between routing protocols propose by researchers belonging to ifferent communities, namely the communities of artificial intelligence, SI, an networking. Sim an Sun [21] presente a review of ACO approaches for routing an loa balancing in wire networks. The authors of the review mae some confusion in interpreting the existing work, since they present ACO-base routing an loa balancing as two ifferent aspects, while, in more general terms, in SI-base routing they are the two faces of the same coin. Artificial intelligence (AI), which simulates the way of living or the rule of information processing of animals in nature to actively an aaptively perceive external environment, provies a novel iea to esign of QoS routing algorithm. For instance, artificial neural network (ANN) [22, 23] an ant colony optimization algorithm [24, 25] are important branches of AI routing algorithms. Though such algorithms solve a part of QoS routing problems, they are lacking in systematic researches on the theory of QoS routing, organizational structure an algorithm flow. As another emerging branch of AI, multi-agent system (MAS) presents a systematically analytic tool in complex network conitions. The researches one have shown that swarm intelligence base routing protocols may remove consierable number of problems relate to battery life, scalability, maintainability, survivability, aaptability an so on. Among the intelligent techniques, GSO base approaches have been wiely use in various applications ue to its global optimal solution. 3. PROPOSED METHODOLOGY With the increasing eman of multimeia applications, efficient an effective support of quality of service (QoS) has become more an more essential. In this work, the banwith, elay, elay jitter, an packet loss ratio constraine has been stuie least-cost QoS routing problem which is known to be NP-complete. In orer to solve the QoS constraine routing effectively an efficiently, the scheme of routing base on HGSO is propose after the analysis of relate works. 3.1 Network Moel The WSNs is enote as a weighte irecte graph G (V, E), where V is a set of sensor noes by a wireless connection. If there are n + 1 noes V, V = v 0, v 1, v 2, v 3,, v n the communication raius of each noe is r i its communication area is A vi an the ege e = v i, v j E represents the two-way wireless connection among two noes ( v i, v j ). The path P (v 1, v n ) in G is an orerly compositing sequence of eges: P v 1, v n = v 1, v 2, v 2, v 3 (v i 1, v i (v n 1, v n )), V i V, 2 n V P v 1, v n is a multi-hop path, the number of eges correspon to the hop istance between noe v 1 an noe v n. Each noe in the path can be regare as an inepenent router. The first noe of the path is the source noe, an the final noe is the estination noe is calle as v s an v.each noe has its ajacent noes. Each ege e = v i, v j E represents v i an v j are the mutual ajacent noes. N vi = {v j e = (v i, v j ) E, i j is a set of ajacent noes of v i ; it is establishe by the iscovery mechanism of the ajacent noes, which is calle as HELLO information exchange. After sening HELLO message, the noe as its QoS parameters to HELLO information. Whereas, provie a pathp v s, v its synthetic QoS metrics can be efine by the elay, banwith an packet loss, that can be reflecte on the noe van the link efor every noev Vthe metrics are elay function Delay(v), ban with function Banwith(v), packet loss function Packet loss(v), an energy function Energy(v). (1) 2013

4 In view of that, in the network, every link e = v i, v j has its corresponing QoS metrics, which are respectively elay function Delay(e), banwithfunction Banwith(e), packet loss function Packet_loss(e), anenergyfunction Energy(e). Banwith- Banwith (B) represents the rate at which an application s traffic must be carrie by the network. When the capacity of the banwith increases it is assure that the performance will be better. Delay - The elay (D) of a network is the time taken by a bit of ata to be transferre from source to sink noe measure in fractions of secons. Delay can be split into several categories namely: i) Processing elay, ii) Queuing elay an iii) Transmission elay. Jitter - The jitter (J) is the variation in the time between packets arriving, cause by network congestion, timing rift, or route changes. Packet Loss- Packet loss (PL) is generally sai to occur when or more number of ata packets transmitte over a network fails to reach the intene estination ue to several issues such as channel congestion, harware fault in the network or problem with network rivers. After efining the QoS metrics of the noe an the link, the QoS metrics of the pathp v s, v can be consiere. Given the source noe v s Van the estination noe v V, the subsequent QoS metrics of path P v s, v are compute as following: Delay p v s, v = Delay(v) v P v s,v + Delay(e) e P v s,v Banwith p v s, v min = Banwith e e P v s, v Packet loss p vs,v = 1 e P v s,v (1 packet loss e ) If the pathp v s, v satisfying all the QoS metrics, it must meet the following requirements: Delay p v s, v = v P v s,v Delay(v) + e P v s,v Delay e < D min Banwith p v s, v = Banwith e e P v s, v > B (2) (3) (4) Packet loss p vs,v = 1 1 packet loss e < PL e P v s,v where D, B, an PL are the QoS guarantees of the WSN network. After representing every QoS function of the routing moel, can start the synthetic QoS moel for every path. In the synthetic QoS moel, in which every QoS inicator shoul meet the QoS constrain, any inconformity will significantly cut own the metrics role an convey the negative an punitive influence to the synthetic QoS. For instance, ifdelay p v s, v < D,the elay of the path may satisfy the constraint conitions, then f elay = 1 1 k Delay p v s,v D Take k close to1, such as 0.9, then the value of f elay will be between 0.9 an 1. If Delay p v s, v > D, it enotes that the elay inicator of path cannot convince the constraint emans for elay application, then f elay = 1 k Delay p v s, v D These agents can then participate in network routing an network maintenance. Therefore, the algorithm performance can be improve in elay, packet loss, an the synthetic QoS, respectively, with energy consumption. (5) 3.2 Agent-assiste QoS-base routing algorithm In this section, it consiers some assumptions about sensor noes: Each noe has a forwaring table or neighbors list that contains local information about its neighbors. Accoring to this table each noe knows the istance between itself an its neighbors an the remaining energy of its neighbors. Data forwaring table for each noe is set up in the first phase. Another assumption in this paper is that in the first phase that sink broacasts the interest or sensor noes broacast an avertisement for the available ata, they also sen their forwaring table to the sink. On the other han the sink knows the information in the forwaring table of each noe. In our moeling, source generates ata agents for carrying information which shoul be transmitte to the sink. This agent starts from source noe an travel towars the sink. When the agent reaches an intermeiate noe, it looks at forwaring table an chooses its next hop accoring to a (5) (6) 2014

5 certain routing scheme. However it checks some conitions; if the longitue (x) or latitue (y) of the next noe is closer to the sink than the x or y of the current noe it chooses the next noe, otherwise it looks at forwaring table again an chooses another noe for the next hop. After the mobile agent arrives at the sink, it passes the ata to the sink an then ies. In QoS base routing, the synthetic QoS metrics are ae aitionally into the ata structure of agent. Hence, the ata structure of the agent consist of the agent ID an its type, the source noe ID, the estination noe ID, the current noe ID, the hop istance of agent, the start time an reach time, etc., an also comprises of the mobile recors of the agent. In the network structure of the mobile recors, the QoS metrics are efine, such as elay, banwith, packet loss, energy, etc. In the QoS base routing algorithm, the forwar agent an the reverse agent are structure to begin the routing approach of WSN noes. Routing tables are upate as the forwar agent sens a packet from the source noe to the estination noe. Once it reaches its estination, each forwar agent says that the traveling time information an other QoS parameter to the reverse agent, which upates the routing tables as it traces the path of the forwar agent in reverse. The intelligent agent can be calculate a appropriate carrier to use the intelligent algorithm in WSNs; the agent can be applie to apperceive the changes in network structure, the network communication flow, an each noe s energy state, an can also take part in network routing an network maintenance, as shown in Fig.1. Agent is base on objective an abunant with communication languages; it can give more flexible interaction an cooperation moe, an can meet the requirements of the interaction of noe in istribute network environment. Self action Action an Reaction Intelligent Agent External Environment Interaction an Cooperation Qos Base Routing Action an Reaction Intelligent Agent Fig.1. Behavior abstraction of the agent Self action 3.3 Routing Table In the network graph, G = V, E there are V V 1 possible source-estination pairs. A source-estination pair can be connecte by a set of links, which is calle a route. There are usually many possible routes between any sourceestination pair. For example, consier the network shown in Fig. 2; the possible routes between 1 to 4 inclue 1-4, , an so on. Routing table is a table store in router, an plays the role of path iscovery in noe routing (. Since every noe in WSNs acts as a router, every noe contains a routing table. 1 2 Fig.2. A simple 6-noe network 3.4 QoS routing base on HGSO optimization Algorithm Glowworm Swarm Optimization algorithm Glowworm Swarm Optimization algorithm is applie for the simultaneous capture of multiple optima of multimoal functions. The algorithm uses an ensemble of agents, which scan the search space an exchange information concerning a fitness of their current position. The fitness is represente by a level of a luminescent quantity calle luciferin. An agent moves in irection of ranomly chosen neighbour, which broacasts higher value of the luciferin. Unfortunately, in the absence of neighbours, the agent oes not move at all. This is an unwelcome feature, because it iminishes the performance of the algorithm. Aitionally, in the case of parallel processing, this feature can lea to unbalance loas. This paper presents simple moifications of the original algorithm, which improve performance of the algorithm by limiting situations, in which the agent cannot move. In GSO a swarm is compose of N agents calle glowworms. A state of a glowworm i at time t can be escribe by the following set of variables: a position in the search space (x i (t)), a luciferin level (l i (t)) an a neighbourhoo range (N i (t)). Luciferin-upate phase: The luciferin upate epens on the function value at the glowworm position. During the luciferin-upate phase, each glowworm as, to its previous luciferin level, a luciferin quantity proportional to the fitness of its current location in the objective function omain. Also, a fraction of the luciferin value is subtracte to simulate the

6 ecay in luciferin with time. The luciferin upate rule is given by: l i t = 1 ρ l i t + γf x i t + 1 (7) where l i t represents the luciferin level associate with glowworm i at time t, ρ is the luciferin ecay constant (0 ρ 1), γ is the luciferin enhancement constant, an f x i t represents the value of the objective function at agent i s location at time t. Movement phase: During the movement phase, each ' glowworm ecies, using a probabilistic mechanism, to move towar a neighbor that has a luciferin value higher than its own. That is, glowworms are attracte to neighbors that glow brighter. The set of neighbors of glowworm i at time t is calculate as follow: N i (t) = j x j t x i t < r i t ; l i t < l i t where the x is the Eucliean norm of x, an r i represents the variable neighborhoo range associate with glowworm i at time t, which is boune above by a circular sensor range r s 0 < r i t < r s. For each glowworm i, the probability of moving towar a neighbor j N i t is given by: p ij (t) = l j t l i t k N i t l k t l i (t) Let glowworm i select a glowworm j N i (t) with p ij (t) given in (7). Then, the iscrete-time moel of the glowworm movements can be state as: (8) (9) AFSA Algorithm In unerwater worl, fish can fin areas with more foo base on their iniviual or swarm search. Inspire by this characteristic, Artificial Fish (AF) moel is represente by prey, free move, swarm, an follow behaviors. AF searches the problem space by those behaviors. AFSA is a ranom search algorithm base on simulating fish swarm behaviors. AF moel consists of variables an functions. Variables are referre X (current AF position), step (maximum length step), visual (visibility omain), try-number (maximum attempts for fining better positions in visual), an crow factor δ (0<δ<1). Functions consist of prey, free move, swarm, an follow behaviors. Assume that X is the position of artificial fish AF i, y = f(x) is the fitness value at position X, ij = X i X j represents the istance between the AF i an j, Visual an δ represent the visual istance an crow factor of the AF respectively, nf is the number of its fellows within the visual, step is the step of the AF moving, S = X j X i X j < Visual is the set of AF i exploring area at the present position. The typical behaviors of the AF are expresse as follows: (1) AF-Prey: Suppose that X i is the AF state at present prey X i X j X j S is the state of AF attempt within the visual, try number is the maximum number of AF attempts. The behavior of prey can be expresse as follows: = x i + step x j x i x j x i x i + 2ran 1 step if y j > y i else (12) x i t + 1 = x i t + s x j t x i t x j t x i t (10) Here ran is ranom function. where x i t R is the location of glowworm i, at time t, in the -imensional real space R, an s > 0 is the step size. Neighborhoo range upate rule: We associate each agent i with a neighborhoo whose raial range r i t is ynamic in nature. Let r 0 be the initial neighborhoo range of each glowworm (that is, r i 0 = r 0, i). To aaptively upate the neighborhoo range of each glowworm, the rule as follows: r i t + 1 = min r s, max 0, r i t + β n i N i t (11) where β is a constant parameter an n i is a parameter use to control the number of neighbors. (2) AF-Swarm: Suppose that X i is the AF state at X present, an X c = i X i S is the center position of nf the AF within the visual. The behavior of swarm can be escribe as follows: swarm X i = X i + step x j x i x j x i prey X i if y c nf > δy i else (13) (3) AF-Follow: Suppose that X i is the AF state at present, an y y_ max = max f(x j ) X j S. The behavior of follow can be expresse in the following equation: 2016

7 follow X i = X i + step x max x i x max x i prey X i if y max nf > δy i else (14) Fig.3. Start Initialize N glowworm swarm Hybri Glowworm Swarm Optimization Algorithm (HGSO) In the basic GSO algorithm, each glowworm only in accorance with luciferin values of glowworms in its neighbor set, selects the glowworm by a certain probability an moves towars it. However, if the search space of a problem is very large or irregular, the neighbor sets of some glowworms may be empty, which leas these glowworms to keep still in iterative process. To avoi this case an ensure that each glowworm keeps moving, we will introuce preatory behavior of AFSA into GSO an propose hybri GSO (HGSO) algorithm. The iea of HGSO is as follows: the glowworms whose neighbor sets are empty are carrie out preatory behavior in their ynamic ecision omains. Assume that N represents population size, x i (t) = x i 1 t, xi 2 t,, xi t enotes the position of the i-th glowworm at the t-th iteration. The flowchart is shown in Perform the praying behavior Upate Luciferin Calculate Neighbors N i (t) Is N i (t) empty? Yes Perform the Swarming behavior No No Perform the searching foo behavior Calculate P ij (t) Move x i (t) Upate r i Max. no. of Iteration is met Yes Display optimum value Stop Fig.3. Flowchart for HGSO The proceure of HGSO can be escribe as follows: Step 1: Let l i 0 = l 0, r i t = 0 ; here, t enotes the number of GSO iterations. Ranomly initialize the position x i (t) (i = 1; 2,, N) of each glowworm in the search space. Calculate the fitness value f x i of each glowworm. Initialize the current optimal position x an the current optimal value f x accoring to the fitness values. Step 2: Upate the luciferin value l (t) of each glowworm accoring to (7) Step 3: Calculate N i t an P ij (t) for each glowworm accoring to (8) an (9). Step 4: For each glowworm, if N i t is not empty, then accoring to P ij (t) an roulette metho, select the j- th glowworm in N i t an move towar it, calculate x i (t + 1) accoring to (10), Or else, otherwise, x i t is use as the initial point AFSA behavior in r i t 2017

8 an get x i (t + 1). If x i t + 1 < a j, then x i t + 1 = a j ; If x i t + 1 > b j, then x i t + 1 = b j, where j = 1, 2,,. Step 5: Calculate the current fitness value f x i t + 1 of each glowworm, if the optimal position an optimal value of the current population are better than x an f x, then upate x an f x, or else, on t upate. Step 6: If the maximum number of iterations is met, x an f x then stop an output x an f x ; or else, calculate r i (t + 1) accoring to (11) an let t = t + 1, return Step EXPERIMENTAL RESULTS AND DISCUSSION CONCLUSION This research work mainly focuses on proviing an efficient QoS base routing through Agent assiste system. This project is simulate using Network Simulator (NS2). The system is installe with Re hat Linux version. In the experiments, WSN parameters were esigne as shown in Table 1. Table 1: NS2 Simulation Parameter Simulation Parameter Number of noes 50 Area size 500 x 500 m Mac Traffic Source CBR Transmit Power 0.02w Receiving Power 0.01w Active Power 240w Inactive Power 2.4w Transmission Range Initial Energy Packet Size Antenna Raio propagation Interface Queue 50m 1J Queue Length 50 Channel Type 512 bytes Value Omni Antenna Two ray Groun Drop tail Channel/Wireless channel The number of noes is from 10 to 100 an the iterative number is 10. The metrics, such as en-to-en elay, packet loss, an the synthetic QoS, were calculate by taking an average of the 10 times value, respectively, from, QoS-PSO, Qos-HABC, an QoS-HGSO algorithm with the increase in the noe number. Figures 4 7 show, respectively, the mean elay curve, the packet loss curve, the synthetic QoS curve an Throughput curve with the increase of noes. Fig.4 shows the performance comparison of the routing approaches base on mean elay. The propose HGSO agent base routing approach is observe to prouce lesser mean elay when compare with the other routing approaches consiere. Mean Delay (s) QoS-EHGA-FF No. of Noes Fig.4. Mean elay vs. the number of noe Table 2 clearly shows that the propose QoS-HGSO algorithm has lesser elay when compare with the other propose algorithms like QoS-PSO-ABC an QoS-EHGA- FF. Table 2: Comparison of Delay QoS-PSO-ABC QoS-HGSO Number Mean Delay of QoS-PSO- QoS- QoS- Noes ABC EHGA-FF HGSO

9 Fig.5 shows the performance comparison of the routing approaches base on packet loss. It is observe that, with the increase in the number of noes, the packet loss increases linearly. The propose HGSO agent base routing approach is observe to prouce lesser packet loss. For instance, at the 100 th noe, the packet loss of the propose approach is observe to be 0.31, whereas, the packet loss attaine by the other approaches is observe to be higher. Packet Loss QoS-EHGA-FF No. of Noes Fig.5. Packet loss vs. the number of noe Table 3 clearly shows that the propose QoS-HGSO algorithm has lesser packet loss rate when compare with the other propose algorithms like QoS-PSO-ABC an QoS- EHGA-FF. Table 3: Comparison of Packet Loss QoS-PSO-ABC QoS-HGSO Number Packet Loss of QoS-PSO- QoS- QoS- Noes ABC EHGA-FF HGSO Qos QoS-EHGA-FF No. of Noe Fig.6. Synthetic QoS graph Vs. the number of noe Fig.6 shows the performance comparison of the routing approaches base on synthetic QoS. The propose HGSO agent base routing approach is observe to prouce significant synthetic QoS. Table 4 shows the quantitative comparison of the synthetic QoS for the noes consiere. Table 4: Comparison of Synthetic QoS QoS-PSO-ABC QoS-HGSO Number Synthetic QoS of QoS-PSO- QoS- QoS- Noes ABC EHGA-FF HGSO

10 Throughput Fig.7. Throughput graph vs. the number of noe Fig.7 clearly shows the throughput comparison. It is note that the propose HGSO approach attains higher throughput when compare with the other techniques at all the stages of iterations. Table 5 shows the quantitative comparison of the synthetic QoS for the noes consiere. Number of Noes Table 5: Comparison of Throughput CONCLUSION QoS-EHGA-FF No. of Noe QoS-PSO-ABC QoS-HGSO Throughput QoS-PSO- QoS- ABC EHGA-FF QoS- HGSO This work mainly focuses on the utilization of swarm intelligent agents to improve the overall performance of the network through QoS base routing. The propose hybri moel applies the synthetic QoS parameters as the objective function such as throughput, elay, packet loss for the hybri intelligent agent to eal with an optimal path for noe routing, an the multi-agent base routing table offers an initial path for QoS-hybri agent algorithm. In this research work, hybri swarm intelligence algorithms are use to overcome the rawbacks of the iniviual algorithm. The hybri algorithm has high spee of convergence an searching capability to solve QoS routing effectively. Finally, compare with the existing approach, the QoS-HGSO algorithm obviously shows its improvement in the quality of service of WSN incluing elay, packet loss an the synthetic QoS. REFRENCES [1] I. Akyiliz, W. Su, Y. Sankarasubramaniam, an E. Cayirci, Wireless Sensor Networks: a Survey, Computer Networks, Vol. 38, No. 4, pp , [2] I. F. Akyiliz, T. Meloia, an K. R. Chowhury, A survey on wireless multimeia sensor networks, Computer Networks, vol. 51, no. 4, pp , [3] K. Akkaya an M. Younis, A survey on routing protocols for wireless sensor networks, A Hoc Networks, vol. 3, no. 3, pp , [4] T. W. Roneau an C. W. Bostian, Artificial Intelligence in Wireless Communications, Artech House, Norwoo, Mass, USA, [5] Susan Rea, Dirk Pesch, Multi-Metric Routing Decisions for AHoc Networks using Fuzzy Logic, 1 st International symposium on wirless communications systems, pp no , Sept [6] D. B. Johnson an D. A. Maltz, Dynamic source routing in a-hoc wireless networks, proc. International Conference on Mobile Computing, vol. 353, pp , [7] Zhi Ren, Wei Guo, Unicast Routing in Mobile A hoc Networks: present an future irections, Proc. 4th International Conference on Parallel an Distribute Computing, Applications an Technologies, PDCAT2003, pp no , Aug [8] Ronal Beaubrun, Baji Molo, Using DSR for Routing Multimeia Traffic in MANETs, proc. International journal of computer networks an communication, vol.2, no.1, pp no , January [9] Chunxue Wu, Fengna Zhang, Hongming Yang, A Novel QoS Multipath Path Routing in MANET, proc. International Journal of Digital Content Technology an its Applications, Vol.4, no.3, pp no , June [10] R.Asokan, A.M.Natarajan, C.Venkatesh, Ant Base Dynamic Source Routing Protocol to Support Multiple Quality of Service (QoS) Metrics in Mobile A Hoc Networks, International Journal of Computer Science an Security, volume (2) issue (3), pp no , [11] Sushmita, Mitra, Yoichi Hayashi, NeuroFuzzy Rule Generation:Survey in Soft Computing Framework, IEEE Transactions on neuralnetworks, VOL. 11, NO. 3, MAY [12] M. Yaghmaei, M. Baraaran, H. Talebiana Fuzzy QoS Routing Algorithm for Communication Network, proc. 2020

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