Hybrid Glowworm Swarm Optimization (HGSO) agent for Qos ... · this work a new efficient and energy aware multipath routing algorithm designed called as a novel hybrid glowworm swarm

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

Wireless Networks

T. Karthikeyan1 and B. Subramani2

1Associate Professor, P.S.G. College of Arts and Science, Coimbatore,

2 HoD, Dr.NGP Arts and Science College, Coimbatore.

ABSTRACT: Nowadays, the rapidly developing science of services focuses on service management, which has to take into

account the available resources and the user's wishes concerning the desired quality and costs. Quality of Service (QoS)

management is perceived as a special aspect of distributed systems management in WSN. Providing QoS aware routing is a

challenging task in this type of network due to dynamic topology and limited resources. The main purpose of QoS aware routing

is to find a feasible path from source to destination which will satisfy two or more end to end QoS constrains. Therefore, the task

of designing an efficient routing algorithm which will satisfy all the quality of service requirements and be robust and adaptive is

considered as a highly challenging problem. Intelligent software agents are employed to monitor the changes that occur in

network structure, network communication flow and each node’s routing state. These agents can after that participates in network

routing and network maintenance. Swarm intelligence techniques have been extensively used in optimization problem for solving

many optimization issues in WSNs. To solve this problem previously Hybrid Genetic Firefly Algorithm (HFGA) was proposed

which can be used for solving optimization problems. However, parameters are set fixed and they do not change with the time. In

addition Firefly algorithm does not memorize or remember any history of better situation for each firefly and this causes them to

move regardless of its previous better situation, and they may end up missing their situations. In order to avoid this problem, in

this work a new efficient and energy aware multipath routing algorithm designed called as a novel hybrid glowworm swarm

optimization (HGSO) algorithm. The presented algorithm embeds predatory behavior of artificial fish swarm algorithm (AFSA)

into glowworm swarm optimization (GSO) algorithm. This proposed Routing Algorithm will increase network lifetime and

decrease packet loss and average end to end delay that makes this algorithm suitable for real time and multimedia applications.

The results show that HGSO algorithm has faster convergence speed, higher computational precision, and is more effective for

solving constrained engineering design problems.

Keywords: Wireless Sensor Network, Routing Algorithm, Synthetic Qos, Agent, Artificial Fish Swarm Algorithm, Glowworm

Swarm Optimization

1. INTRODUCTION

A Wireless Sensor Network (WSN) is composed of a large

number of small devices with limited power, processing and

communication capabilities that are densely deployed inside a

phenomenon or very close to it [1]. Sensor nodes have two

main functionalities: monitor the environment and send the

sensed data to a special node, called the sink. Sensor nodes

can send the monitored data periodically (periodic data

reporting) or when an event occurs (event-based reporting).

Some applications (e.g. Forest fire monitoring) need a

mixture of both periodic and event-based data reporting. In

this case, each sensor node monitors the environment and

besides sending periodical 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 mixed data reporting application. Periodical data

reporting and event-based packets are the two main packet

types. The event-based packets usually alert the sink when a

critical event occurs, and therefore these packets have to be

transmitted as soon as possible, with higher priority than the

periodic data reporting. Many routing protocols for WSNs

have been proposed in the literature, but there are some

challenges that have not been resolved yet. One of them is

integrating Quality of Service (QoS) requirements in the

routing protocols for mixed data reporting applications. Due

to the dynamic nature of the network, the existing QoS

protocols for wired networks cannot be applied directly to

WSNs. Congestion control mechanisms are essential in

WSNs. A typical sensor network comprises a large number of

multifunctional, low-cost, and low-power nodes that are

deployed densely and randomly in an environment for

monitored sensing to control the environment, perform local

processing, and 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 provide Quality of Service (QoS)

parameter guarantees in real-time applications. Several

approaches and protocols have been proposed in the literature

for QoS parameter support in these types of networks [2, 3].

Energy consumption is considered to be the most important

constraint in WSNs because of the low power and the

processing factors. These factors reduce the QoS and the

lifetime of the network. The primary concern is how to

properly use resources (for deriving multimedia content) to

provide appropriately shared data among all of the

transmission radios while maintaining a proper level of

imaging and video data transmission. The main goal is the

T Karthikeyan et al, Int.J.Computer Technology & Applications,Vol 5 (6),2011-2021

IJCTA | Nov-Dec 2014 Available [email protected]

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appropriate use of multimedia resources by properly

maintaining a level of optimized QoS, which further depends

on the performance of the radio. This goal requires careful

processing to achieve optimal end-to-end delay, jitter, and

energy consumption, as well as acceptable throughput.

Different applications of real-time WSNs have different QoS

priorities based on the performance of the transmission radio.

The requirements depend on the situation for which the

application uses the radio service. It is important for each

sensor node in the network domain to consider resource

allocation as an optimization problem with different potential

goals. First, a sensor should attempt to optimize source-based

capabilities to maximize its use of resources. Second, a

sensor should consider resource utilization from a perspective

of need, that is, the hop information. Third, resource

allocation should be considered from a global perspective in

which the utilization of resources by all of the sensor nodes is

considered [4]. Thus, the question raised is how to balance

the use of resources and transmission radio to provide

optimal QoS parameters as well as to avoid the overuse of

resources. Swarm intelligence is a relatively novel field. It

addresses the study of the collective behaviors of systems

made by many components that coordinate using

decentralized controls and self-organization. A large part of

the research in swarm intelligence has focused on the reverse

engineering and the adaptation of collective behaviors

observed in natural systems with the aim of designing

effective algorithms for distributed optimization. These

algorithms, like their natural systems of inspiration, show the

desirable properties of being adaptive, scalable, and robust.

These are key properties in the context of network routing,

and in particular of routing in wireless sensor networks.

In this work proposed an a novel agent-assisted Quality of

Service (QoS) routing algorithm of Wireless Sensor

Networks (WSNs) based on Hybrid Glowworm Optimization

algorithm (HGSO). This HGSO algorithm hybrids, predatory

behavior of artificial Fish swarm algorithm (AFSA) into

glowworm swarm optimization (GSO) algorithm for Qos

routing in WSN. HGSO uses the constraint processing

technology based on feasibility rules to update the optimal

location of the population, which makes the population

rapidly convergence to feasible regions and find better

feasible solution. The rest of the paper is organized as

follows: In Section 2, described the existing Qos routing

protocols. In Section 3, AFSA, GSO is simply described and

the HGSO hybrid strategy is proposed and explained in

detail. Simulation and comparisons based Qos routing based

on HGSO are presented in Section 4 and in the end some

conclusions in Section 5.

2. RELATED WORK

QoS routing has received attention recently for providing

QoS for multimedia traffic in wireless ad hoc networks and

some work has been carried out to address this critical issue.

Here, we provide a brief review of existing work addressing

the QoS routing issues in wireless ad hoc networks. Multiple

paths routing has to achieving a good trade-off between

success probability in route acquisition and protocol

overhead. It works by searching multiple paths in parallel for

a QoS path. Most of the routing protocols for mobile ad hoc

networks, such as AODV [5], DSR [6], and TORA [7], are

designed without explicitly considering quality-of service of

the routes they generate. QoS routing in ad hoc networks has

been studied only recently [8, 9]. QoS routing requires not

only finding a route from a source to a destination, but a route

that satisfies the endto- ending QoS requirement, often given

in terms of bandwidth or delay. Quality of service is more

difficult to guarantee in ad-hoc networks than in most other

type of networks, because the wireless bandwidth is shared

among adjacent nodes and the network topology changes as

the nodes move. This requires extensive collaboration

between the nodes, both to establish the route and to secure

the resources necessary to provide the QoS.

For selection of the QoS, based on the parameter constrained

in the network is very difficult. In many research papers, they

proposed fuzzy logic technique for QoS prediction. It has the

better performance comparatively other technique, but the

inputs to the fuzzy system are uncertain [10, 11]. So, we

won’t get required QoS. We were seen in many applications,

the uncertain data sets are tuned to require one by using

neural back propagation algorithm and hence in this paper,

we used error back propagation algorithm [12]. The first

routing algorithms based on swarm intelligence concepts date

back to the second half of the ’90s and were designed for

wired networks. Schoonderwoerd’s et al. Ant-Based Control

(ABC) [13] addressed circuit-switched telephone networks,

while the AntNet algorithm of Di Caro and Dorigo [14, 15]

was meant for best-effort IP networks [15]. More precisely,

both these algorithms were developed according to the

principles of Ant Colony Optimization (ACO) [16], a popular

metaheuristic for optimization. ACO derives from the

reverse-engineering and the adaptation of the shortest path

behavior observed in foraging ant colonies [17].

This behavior results from the combined ability of the ants of

marking their paths by laying pheromone signals and, at the

same time, searching the most promising foraging areas by

moving towards the directions locally marked by higher

pheromone intensity. The ACO principles that are at the roots

of ABC and AntNet have guided, in turn, the design of a

number of other SI algorithms for routing in a variety of

different network environments. A detailed discussion on the

mechanisms at work in ACO-based routing, as well as an



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extensive overview of the characteristics and the performance

of a number of different routing algorithms, and in particular

of AntNet and of its different versions, were first compiled in

the Ph.D. thesis of its different versions, were first compiled

in the Ph.D. thesis of Di Caro [18]. More recently, Farooq

and Di Caro [30] have presented a comprehensive review of

the most prominent routing algorithms for wired networks

and MANETs that have been inspired by insect societies

(ants and bees).

The review highlights the characteristics specific to SI-based

routing protocols and shows why these characteristics make

these protocols particularly suitable to deal with the

challenges posed by next generation networks. In the review,

the authors first define a novel taxonomy for routing

algorithms that takes into account an extensive number of

different aspects (e.g. deterministic vs. probabilistic

decisions, global vs. local representations, single-path vs.

multiple paths, etc.), and then discuss the different algorithms

with respect to the new taxonomy. They also elaborate the

reasons for good performance of SI-based algorithms as

observed in the simulation studies. However, the authors of

[19] also point out the lack of performance evaluations based

on the use of real devices and testbeds. This fact makes hard

to assess the effective performance of these algorithms. The

comprehensive survey of Wedde and Farooq [20] focuses on

routing algorithms for wired networks. The main objective of

the survey is to understand the basic design principles and the

core differences existing between routing protocols proposed

by researchers belonging to different communities, namely

the communities of artificial intelligence, SI, and net-

working.

Sim and Sun [21] presented a review of ACO approaches for

routing and load balancing in wired networks. The authors of

the review made some confusion in interpreting the existing

work, since they present ACO-based routing and load

balancing as two different aspects, while, in more general

terms, in SI-based 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 and adaptively perceive external

environment, provides a novel idea to design of QoS routing

algorithm. For instance, artificial neural network (ANN) [22,

23] and 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 and algorithm flow. As

another emerging branch of AI, multi-agent system (MAS)

presents a systematically analytic tool in complex network

conditions.

The researches done have shown that swarm intelligence

based routing protocols may remove considerable number of

problems related to battery life, scalability, maintainability,

survivability, adaptability and so on. Among the intelligent

techniques, GSO based approaches have been widely used in

various applications due to its global optimal solution.

3. PROPOSED METHODOLOGY

With the increasing demand of multimedia applications,

efficient and effective support of quality of service (QoS) has

become more and more essential. In this work, the

bandwidth, delay, delay jitter, and packet loss ratio

constrained has been studied least-cost QoS routing problem

which is known to be NP-complete. In order to solve the QoS

constrained routing effectively and efficiently, the scheme of

routing based on HGSO is proposed after the analysis of

related works.

3.1 Network Model

The WSNs is denoted as a weighted directed graph

G (V, E), where V is a set of sensor nodes by a wireless

connection. If there are n + 1

nodes V, V = v0, v1 , v2 , v3 ,… , vn the communication radius

of each node is ri its communication area is Avi and the edge

e = vi , vj ∈ E represents the two-way wireless connection

among two nodes ( vi,vj ). The path P (v1, vn ) in G is an

orderly compositing sequence of edges:

𝑃 𝑣1,𝑣𝑛

= 𝑣1, 𝑣2 , 𝑣2,𝑣3 … (𝑣𝑖−1 ,𝑣𝑖 … (𝑣𝑛−1,𝑣𝑛 )),𝑉𝑖∈ 𝑉, 2 ≤ 𝑛 ≤ |𝑉|

(1)

P v1, vn is a multi-hop path, the number of edges correspond

to the hop distance between node v1and node vn . Each node

in the path can be regarded as an independent router. The first

node of the path is the source node, and the final node is the

destination node is called as vs and vd .Each node has its

adjacent nodes. Each edge e = vi , vj ∈ E represents

vi and vj are the mutual adjacent nodes. Nvi = {vj|e =

(vi , vj) ∈ E, i ≠ j is a set of adjacent nodes of vi ; it is

established by the discovery mechanism of the adjacent

nodes, which is called as HELLO information exchange.

After sending HELLO message, the node adds its QoS

parameters to HELLO information. Whereas, provided a

pathP vs , vd its synthetic QoS metrics can be defined by the

delay, bandwidth and packet loss, that can be reflected on the

node vand the link efor every nodev ∈ Vthe metrics are delay

function— Delay(v), band width function— Bandwidth(v),

packet loss function—Packet loss(v), and energy function—

Energy(v).



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In view of that, in the network, every link e = vi , vj has its

corresponding QoS metrics, which are respectively delay

function— Delay(e), bandwidthfunction— Bandwidth(e),

packet loss function— Packet_loss(e), andenergyfunction—

Energy(e).

Bandwidth- Bandwidth (B) represents the rate at

which an application’s traffic must be carried by

the network. When the capacity of the bandwidth

increases it is assured that the performance will be

better.

Delay - The delay (D) of a network is the time

taken by a bit of data to be transferred from source

to sink node measured in fractions of seconds.

Delay can be split into several categories namely: i)

Processing delay, ii) Queuing delay and iii)

Transmission delay.

Jitter - The jitter (J) is the variation in the time

between packets arriving, caused by network

congestion, timing drift, or route changes.

Packet Loss- Packet loss (PL) is generally said to

occur when or more number of data packets

transmitted over a network fails to reach the

intended destination due to several issues such as

channel congestion, hardware fault in the network

or problem with network drivers.

After defining the QoS metrics of the node and the link, the

QoS metrics of the pathP vs , vd can be considered. Given

the source node vs ∈ Vand the destination node vd ∈ V, the

subsequent QoS metrics of path P vs , vd are computed as

following:

𝐷𝑒𝑙𝑎𝑦 𝑝 𝑣𝑠 ,𝑣𝑑

= 𝐷𝑒𝑙𝑎𝑦(𝑣)𝑣 ∈ 𝑃 𝑣𝑠,𝑣𝑑

+ 𝐷𝑒𝑙𝑎𝑦(𝑒)

e ∈𝑃 𝑣𝑠,𝑣𝑑

(2)

𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝑝 𝑣𝑠 ,𝑣𝑑

=𝑚𝑖𝑛

e ∈ 𝑃 𝑣𝑠 ,𝑣𝑑 𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝑒

(3)

𝑃𝑎𝑐𝑘𝑒𝑡𝑙𝑜𝑠𝑠 𝑝 𝑣𝑠,𝑣𝑑 = 1 −

(1 − 𝑝𝑎𝑐𝑘𝑒𝑡𝑙𝑜𝑠𝑠 𝑒 )e ∈𝑃 𝑣𝑠,𝑣𝑑

(4)

If the path𝑃 𝑣𝑠 ,𝑣𝑑 satisfying all the QoS metrics, it must

meet the following requirements:

𝐷𝑒𝑙𝑎𝑦 𝑝 𝑣𝑠 ,𝑣𝑑 = 𝐷𝑒𝑙𝑎𝑦(𝑣)𝑣 ∈ 𝑃 𝑣𝑠,𝑣𝑑 + 𝐷𝑒𝑙𝑎𝑦 𝑒 < 𝐷e ∈𝑃 𝑣𝑠,𝑣𝑑

𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝑝 𝑣𝑠 , 𝑣𝑑 =𝑚𝑖𝑛

e ∈ 𝑃 𝑣𝑠 ,𝑣𝑑 𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝑒

> 𝐵

𝑃𝑎𝑐𝑘𝑒𝑡𝑙𝑜𝑠𝑠 𝑝 𝑣𝑠,𝑣𝑑 = 1

− 1 − 𝑝𝑎𝑐𝑘𝑒𝑡𝑙𝑜𝑠𝑠 𝑒 < 𝑃𝐿

e ∈𝑃 𝑣𝑠,𝑣𝑑

where D, B, and PL are the QoS guarantees of the WSN

network. After representing every QoS function of the

routing model, can start the synthetic QoS model for every

path. In the synthetic QoS model, in which every QoS

indicator should meet the QoS constrain, any inconformity

will significantly cut down the metrics role and convey the

negative and punitive influence to the synthetic QoS.

For instance, ifDelay p vs , vd < 𝐷 ,the delay of the path

may satisfy the constraint conditions, then

fdelay = 1 − 1−k Delay p vs ,vd

D

(5)

(5)

Take k close to1, such as 0.9, then the value of fdelay will be

between 0.9 and 1.

If Delay p vs , vd > 𝐷, it denotes that the delay indicator of

path cannot convince the constraint demands for delay

application, then

fdelay = 1 − k −Delay p vs , vd

D

(6)

These agents can then participate in network routing and

network maintenance. Therefore, the algorithm performance

can be improved in delay, packet loss, and the synthetic QoS,

respectively, with energy consumption.

3.2 Agent-assisted QoS-based routing algorithm

In this section, it considers some assumptions about sensor

nodes: Each node has a forwarding table or neighbors list that

contains local information about its neighbors. According to

this table each node knows the distance between itself and its

neighbors and the remaining energy of its neighbors. Data

forwarding table for each node is set up in the first phase.

Another assumption in this paper is that in the first phase that

sink broadcasts the interest or sensor nodes broadcast an

advertisement for the available data, they also send their

forwarding table to the sink. On the other hand the sink

knows the information in the forwarding table of each node.

In our modeling, source generates data agents for carrying

information which should be transmitted to the sink. This

agent starts from source node and travel towards the sink.

When the agent reaches an intermediate node, it looks at

forwarding table and chooses its next hop according to a



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certain routing scheme. However it checks some conditions;

if the longitude (x) or latitude (y) of the next node is closer to

the sink than the x or y of the current node it chooses the next

node, otherwise it looks at forwarding table again and

chooses another node for the next hop.

After the mobile agent arrives at the sink, it passes the data to

the sink and then dies. In QoS based routing, the synthetic

QoS metrics are added additionally into the data structure of

agent. Hence, the data structure of the agent consist of the

agent ID and its type, the source node ID, the destination

node ID, the current node ID, the hop distance of agent, the

start time and reach time, etc., and also comprises of the

mobile records of the agent. In the network structure of the

mobile records, the QoS metrics are defined, such as delay,

bandwidth, packet loss, energy, etc. In the QoS based routing

algorithm, the forward agent and the reverse agent are

structured to begin the routing approach of WSN nodes.

Routing tables are updated as the forward agent sends a

packet from the source node to the destination node. Once it

reaches its destination, each forward agent says that the

traveling time information and other QoS parameter to the

reverse agent, which updates the routing tables as it traces the

path of the forward agent in reverse. The intelligent agent can

be calculated a appropriate carrier to use the intelligent

algorithm in WSNs; the agent can be applied to apperceive

the changes in network structure, the network communication

flow, and each node’s energy state, and can also take part in

network routing and network maintenance, as shown in Fig.1.

Agent is based on objective and abundant with

communication languages; it can give more flexible

interaction and cooperation mode, and can meet the

requirements of the interaction of node in distributed network

environment.

Fig.1. Behavior abstraction of the agent

3.3 Routing Table

In the network graph, 𝐺 = 𝑉,𝐸 there are 𝑉 × 𝑉 − 1

possible source-destination pairs. A source-destination pair

can be connected by a set of links, which is called a ―route‖.

There are usually many possible routes between any source-

destination pair. For example, consider the network shown in

Fig. 2; the possible routes between 1 𝑡𝑜 4 include 1-4, 1-2-3-

4, 1-3-41-2-6-4-4… and so on. Routing table is a table stored

in router, and plays the role of path discovery in node routing

(. Since every node in WSNs acts as a router, every node

contains a routing table.

Fig.2. A simple 6-node network

3.4 QoS routing based on HGSO optimization Algorithm

3.4.1 Glowworm Swarm Optimization algorithm

Glowworm Swarm Optimization algorithm is applied for the

simultaneous capture of multiple optima of multimodal

functions. The algorithm uses an ensemble of agents, which

scan the search space and exchange information concerning a

fitness of their current position. The fitness is represented by

a level of a luminescent quantity called luciferin. An agent

moves in direction of randomly chosen neighbour, which

broadcasts higher value of the luciferin. Unfortunately, in the

absence of neighbours, the agent does not move at all. This is

an unwelcome feature, because it diminishes the performance

of the algorithm. Additionally, in the case of parallel

processing, this feature can lead to unbalanced loads. This

paper presents simple modifications of the original algorithm,

which improve performance of the algorithm by limiting

situations, in which the agent cannot move.

In GSO a swarm is composed of N agents called glowworms.

A state of a glowworm i at time t can be described by the

following set of variables: a position in the search

space (𝒙𝑖(𝑡)), a luciferin level (𝑙𝑖(𝑡)) and a neighbourhood

range (𝑁𝑖(𝑡)).

Luciferin-update phase: The luciferin update depends on

the function value at the glowworm position. During the

luciferin-update phase, each glowworm adds, to its previous

luciferin level, a luciferin quantity proportional to the fitness

of its current location in the objective function domain. Also,

a fraction of the luciferin value is subtracted to simulate the

1

2

3

4 5

6

External

Environment

Qos Based

Routing

Intelligent

Agent

Intelligent

Agent

Interaction and

Cooperation

Action and

Reaction

Self

action

Self

action

Action and

Reaction



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decay in luciferin with time. The luciferin update rule is

given by:

𝑙𝑖 𝑡 = 1 − 𝜌 𝑙𝑖 𝑡 + 𝛾𝑓 𝑥𝑖 𝑡 + 1

(7)

where 𝑙𝑖 𝑡 represents the luciferin level associated with

glowworm 𝑖 at time t, 𝜌 is the luciferin decay constant

(0 ≤ 𝜌 ≤ 1) , 𝛾 is the luciferin enhancement constant, and

𝑓 𝑥𝑖 𝑡 represents the value of the objective function at

agent 𝑖’s location at time 𝑡.

Movement phase: During the movement phase, each '

glowworm decides, using a probabilistic mechanism, to move

toward a neighbor that has a luciferin value higher than its

own. That is, glowworms are attracted to neighbors that glow

brighter. The set of neighbors of glowworm i at time t is

calculated as follow:

𝑁𝑖(𝑡) = 𝑗 ∶ 𝑥𝑗 𝑡 − 𝑥𝑖 𝑡

< 𝑟𝑖𝑑 𝑡 ; 𝑙𝑖 𝑡 < 𝑙𝑖 𝑡

(8)

where the ∥x∥ is the Euclidean norm of x, and 𝑟𝑖𝑑 represents

the variable neighborhood range associated with glowworm 𝑖

at time t, which is bounded above by a circular sensor range

𝑟𝑠 0 < 𝑟𝑖𝑑 𝑡 < 𝑟𝑠 . For each glowworm i, the probability of

moving toward a neighbor 𝑗 ∈ 𝑁𝑖 𝑡 is given by:

𝑝𝑖𝑗 (𝑡) =𝑙𝑗 𝑡 −𝑙𝑖 𝑡

𝑙𝑘 𝑡 −𝑙𝑖(𝑡)𝑘∈𝑁𝑖 𝑡

(9)

Let glowworm i select a glowworm 𝑗 ∈ 𝑁𝑖(𝑡) with

𝑝𝑖𝑗 (𝑡) given in (7). Then, the discrete-time model of the

glowworm movements can be stated as:

𝑥𝑖 𝑡 + 1 = 𝑥𝑖 𝑡 + 𝑠 𝑥𝑗 𝑡 − 𝑥𝑖 𝑡

𝑥𝑗 𝑡 − 𝑥𝑖 𝑡

(10)

where 𝑥𝑖 𝑡 ∈ 𝑅𝑑 is the location of glowworm i, at time t, in

the d-dimensional real space 𝑅𝑑 , and 𝑠 > 0 is the step size.

Neighborhood range update rule: We associate each agent

𝑖 with a neighborhood whose radial range 𝑟𝑖𝑑 𝑡 is dynamic

in nature. Let 𝑟0 be the initial neighborhood range of each

glowworm (that is, 𝑟𝑖𝑑 0 = 𝑟0,∀𝑖). To adaptively update the

neighborhood range of each glowworm, the rule as follows:

𝑟𝑖𝑑 𝑡 + 1 = min 𝑟𝑠 , max 0, 𝑟𝑖

𝑑 𝑡 +

𝛽 𝑛𝑖 − 𝑁𝑖 𝑡

(11)

where 𝛽 is a constant parameter and 𝑛𝑖is a parameter used to

control the number of neighbors.

3.4.2 AFSA Algorithm

In underwater world, fish can find areas with more food

based on their individual or swarm search. Inspired by this

characteristic, Artificial Fish (AF) model is represented by

prey, free move, swarm, and follow behaviors. AF searches

the problem space by those behaviors. AFSA is a random

search algorithm based on simulating fish swarm behaviors.

AF model consists of variables and functions. Variables are

referred X (current AF position), step (maximum length

step), visual (visibility domain), try-number (maximum

attempts for finding better positions in visual), and crowd

factor δ (0<δ<1). Functions consist of prey, free move,

swarm, and follow behaviors. Assume that X is the position

of artificial fish 𝐴𝐹 𝑖, 𝑦 = 𝑓(𝑋) is the fitness value at

position X, 𝑑𝑖𝑗 = 𝑋𝑖 − 𝑋𝑗 represents the distance between

the AF 𝑖 and 𝑗, Visual and δ represent the visual distance and

crowd factor of the AF respectively, 𝑛𝑓 is the number of its

fellows within the visual, 𝑠𝑡𝑒𝑝 is the step of the AF moving,

𝑆 = 𝑋𝑗 𝑋𝑖 − 𝑋𝑗 < 𝑉𝑖𝑠𝑢𝑎𝑙 is the set of AF 𝑖 exploring

area at the present position. The typical behaviors of the AF

are expressed as follows:

(1) AF-Prey: Suppose that 𝑋𝑖 is the AF state at present

𝑋𝑗 𝑋𝑗 ∈ 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 expressed as

follows:

𝑝𝑟𝑒𝑦 𝑋𝑖

= 𝑥𝑖 + 𝑠𝑡𝑒𝑝

𝑥𝑗 − 𝑥𝑖

𝑥𝑗 − 𝑥𝑖 𝑖𝑓 𝑦𝑗 > 𝑦𝑖

𝑥𝑖 + 2𝑟𝑎𝑛𝑑 − 1 ∙ 𝑠𝑡𝑒𝑝 𝑒𝑙𝑠𝑒

(12)

Here 𝑟𝑎𝑛𝑑 is random function.

(2) AF-Swarm: Suppose that 𝑋𝑖 is the AF state at

present, and 𝑋𝑐 = 𝑋𝑖

𝑛𝑓𝑋𝑖∈𝑆 is the center position of

the AF within the visual. The behavior of swarm

can be described as follows:

𝑠𝑤𝑎𝑟𝑚 𝑋𝑖

= 𝑋𝑖 + 𝑠𝑡𝑒𝑝

𝑥𝑗 − 𝑥𝑖

𝑥𝑗 − 𝑥𝑖 𝑖𝑓

𝑦𝑐𝑛𝑓

> 𝛿𝑦𝑖

𝑝𝑟𝑒𝑦 𝑋𝑖 𝑒𝑙𝑠𝑒

(13)

(3) AF-Follow: Suppose that 𝑋𝑖 is the AF state at

present, and 𝑦 𝑦_ max = max 𝑓(𝑋𝑗 ) 𝑋𝑗 ∈ 𝑆 . The

behavior of follow can be expressed in the

following equation:



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𝑓𝑜𝑙𝑙𝑜𝑤 𝑋𝑖

= 𝑋𝑖 + 𝑠𝑡𝑒𝑝

𝑥𝑚𝑎𝑥 − 𝑥𝑖 𝑥𝑚𝑎𝑥 − 𝑥𝑖

𝑖𝑓 𝑦𝑚𝑎𝑥𝑛𝑓

> 𝛿𝑦𝑖

𝑝𝑟𝑒𝑦 𝑋𝑖 𝑒𝑙𝑠𝑒

(14)

3.4.3 Hybrid Glowworm Swarm Optimization

Algorithm (HGSO)

In the basic GSO algorithm, each glowworm only in

accordance with luciferin values of glowworms in its

neighbor set, selects the glowworm by a certain probability

and moves towards it. However, if the search space of a

problem is very large or irregular, the neighbor sets of some

glowworms may be empty, which leads these glowworms to

keep still in iterative process. To avoid this case and ensure

that each glowworm keeps moving, we will introduce

predatory behavior of AFSA into GSO and propose hybrid

GSO (HGSO) algorithm. The idea of HGSO is as follows:

the glowworms whose neighbor sets are empty are carried out

predatory behavior in their dynamic decision domains.

Assume that N represents population size, 𝑥𝑖(𝑡) =

𝑥𝑖 1 𝑡 ,𝑥𝑖

2 𝑡 ,· · · ,𝑥𝑖 𝑑 𝑡 denotes the position of the 𝑖-th

glowworm at the t-th iteration. The flowchart is shown in

Fig.3.

Fig.3. Flowchart for HGSO

The procedure of HGSO can be described as follows:

Step 1: Let 𝑙𝑖 0 = 𝑙0 , 𝑟𝑑𝑖 𝑡 = 0 ; here, t denotes the

number of GSO iterations. Randomly initialize the

position 𝑥𝑖(𝑡) (𝑖 = 1; 2,· · · ,𝑁) of each glowworm

in the search space. Calculate the fitness value 𝑓 𝑥𝑖

of each glowworm. Initialize the current optimal

position 𝑥′ and the current optimal value 𝑓𝑥′ according

to the fitness values.

Step 2: Update the luciferin value 𝑙 (𝑡) of each glowworm

according to (7)

Step 3: Calculate 𝑁𝑖 𝑡 and 𝑃𝑖𝑗 (𝑡) for each glowworm

according to (8) and (9).

Step 4: For each glowworm, if 𝑁𝑖 𝑡 is not empty, then

according to 𝑃𝑖𝑗 (𝑡) and roulette method, select the j-

th glowworm in 𝑁𝑖 𝑡 and move toward it, calculate

𝑥𝑖(𝑡 + 1) according to (10), Or else, otherwise, 𝑥𝑖 𝑡

is used as the initial point AFSA behavior in 𝑟𝑖𝑑 𝑡

Start

Initialize N

glowworm swarm

Update Luciferin

Calculate

Neighbors 𝑁𝑖(𝑡)

Is 𝑁𝑖(𝑡)

empty?

Perform the

praying

behavior

Perform the Swarming

behavior

Perform the

searching

food behavior

Calculate 𝑃𝑖𝑗 (𝑡)

Move 𝑥𝑖(𝑡)

Update 𝑟𝑑𝑖

Max. no. of Iteration

is met

Display

optimum value

Stop

No

No

Yes

Yes



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ISSN:2229-6093

and get 𝑥𝑖(𝑡 + 1) . If 𝑥𝑖 𝑡 + 1 < 𝑎𝑗 , then 𝑥𝑖 𝑡 +

1 = 𝑎𝑗 ; If 𝑥𝑖 𝑡 + 1 > 𝑏𝑗 , then 𝑥𝑖 𝑡 + 1 = 𝑏𝑗 ,

where 𝑗 = 1, 2,· · · ,𝑑.

Step 5: Calculate the current fitness value 𝑓 𝑥𝑖 𝑡 + 1 of

each glowworm, if the optimal position and optimal

value of the current population are better than 𝑥∗ and

𝑓𝑥∗, then update 𝑥∗ and 𝑓𝑥

∗ , or else, don’t update.

Step 6: If the maximum number of iterations is met, 𝑥∗ and

𝑓𝑥∗then stop and output 𝑥∗ and 𝑓𝑥

∗; or else, calculate

𝑟𝑖𝑑(𝑡 + 1) according to (11) and let t = t + 1, return

Step 2.

4. EXPERIMENTAL RESULTS AND

DISCUSSION CONCLUSION

This research work mainly focuses on providing an efficient

QoS based routing through Agent assisted system. This

project is simulated using Network Simulator (NS2). The

system is installed with Red hat Linux version. In the

experiments, WSN parameters were designed as shown in

Table 1.

Table 1: NS2 Simulation Parameter

Simulation Parameter Value

Number of nodes 50

Area size 500 x 500 m

Mac 802.11

Traffic Source CBR

Transmit Power 0.02w

Receiving Power 0.01w

Active Power 240w

Inactive Power 2.4w

Transmission Range 50m

Initial Energy 1J

Packet Size 512 bytes

Antenna Omni Antenna

Radio propagation Two ray Ground

Interface Queue Drop tail

Queue Length 50

Channel Type Channel/Wireless channel

The number of nodes is from 10 to 100 and the iterative

number is 10. The metrics, such as end-to-end delay, packet

loss, and the synthetic QoS, were calculated by taking an

average of the 10 times’ value, respectively, from AODV,

QoS-PSO, Qos-HABC, and QoS-HGSO algorithm with the

increase in the node number. Figures 4–7 show, respectively,

the mean delay curve, the packet loss curve, the synthetic

QoS curve and Throughput curve with the increase of nodes.

Fig.4 shows the performance comparison of the routing

approaches based on mean delay. The proposed HGSO agent

based routing approach is observed to produce lesser mean

delay when compared with the other routing approaches

considered.

Fig.4. Mean delay vs. the number of node

Table 2 clearly shows that the proposed QoS-HGSO

algorithm has lesser delay when compared with the other

proposed algorithms like QoS-PSO-ABC and QoS-EHGA-

FF.

Table 2: Comparison of Delay

Number

of

Nodes

Mean Delay

AODV QoS-PSO-

ABC

QoS-

EHGA-FF

QoS-

HGSO

10 0.1 0.1 0.1 0.1

20 0.2 0.2 0.2 0.15

30 0.4 0.4 0.3 0.25

40 0.8 0.6 0.5 0.3

50 1 0.85 0.7 0.35

60 1.6 1 0.9 0.42

70 2 1.4 1 0.5

80 3.2 1.8 1.2 0.55

90 4 2.2 1.5 0.62

100 5.1 3.1 2 0.75

0

1

2

3

4

5

6

10 20 30 40 50 60 70 80 90 100

Mea

n D

ela

y (

s)

No. of Nodes

AODV QoS-PSO-ABCQoS-EHGA-FF QoS-HGSO



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ISSN:2229-6093


approaches based on packet loss. It is observed that, with the

increase in the number of nodes, the packet loss increases

linearly. The proposed HGSO agent based routing approach

is observed to produce lesser packet loss. For instance, at the

100th node, the packet loss of the proposed approach is

observed to be 0.31, whereas, the packet loss attained by the

other approaches is observed to be higher.

Fig.5. Packet loss vs. the number of node

Table 3 clearly shows that the proposed QoS-HGSO

algorithm has lesser packet loss rate when compared with the

other proposed algorithms like QoS-PSO-ABC and QoS-

EHGA-FF.

Table 3: Comparison of Packet Loss

Number

of

Nodes

Packet Loss

AODV QoS-PSO-

ABC

QoS-

EHGA-FF

QoS-

HGSO

10 0.23 0.21 0.21 0.2

20 0.28 0.24 0.22 0.2

30 0.32 0.27 0.25 0.21

40 0.4 0.3 0.27 0.21

50 0.45 0.35 0.29 0.23

60 0.52 0.39 0.32 0.25

70 0.54 0.45 0.36 0.27

80 0.58 0.48 0.38 0.27

90 0.58 0.5 0.41 0.29

100 0.59 0.51 0.41 0.31

Fig.6. Synthetic QoS graph Vs. the number of node


approaches based on synthetic QoS. The proposed HGSO

agent based routing approach is observed to produce

significant synthetic QoS. Table 4 shows the quantitative

comparison of the synthetic QoS for the nodes considered.

Table 4: Comparison of Synthetic QoS

Number

of

Nodes

Synthetic QoS

AODV QoS-PSO-

ABC

QoS-

EHGA-FF

QoS-

HGSO

10 0.85 0.9 0.9 0.9

20 0.7 0.86 0.89 0.9

30 0.67 0.77 0.84 0.89

40 0.5 0.7 0.79 0.88

50 0.45 0.68 0.72 0.86

60 0.38 0.64 0.69 0.83

70 0.34 0.6 0.65 0.8

80 0.3 0.59 0.63 0.78

90 0.3 0.58 0.62 0.76

100 0.3 0.58 0.62 0.73

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

10 20 30 40 50 60 70 80 90 100

Pa

cket

Lo

ss

No. of Nodes

AODV QoS-PSO-ABC

QoS-EHGA-FF QoS-HGSO

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10 20 30 40 50 60 70 80 90 100

Qo

s

No. of Node

AODV QoS-PSO-ABC




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ISSN:2229-6093

Fig.7. Throughput graph vs. the number of node

Fig.7 clearly shows the throughput comparison. It is noted

that the proposed HGSO approach attains higher throughput

when compared with the other techniques at all the stages of

iterations. Table 5 shows the quantitative comparison of the

synthetic QoS for the nodes considered.

Table 5: Comparison of Throughput

Number

of

Nodes

Throughput

AODV QoS-PSO-

ABC

QoS-

EHGA-FF

QoS-

HGSO

10 0.15 0.2 0.2 0.3

20 0.25 0.35 0.45 0.6

30 0.45 0.75 0.8 1.5

40 0.7 1.5 2 3

50 1.2 2 3 4

60 1.68 2.5 3.5 4.8

70 2.5 3 4 5.4

80 3.8 3.5 4.5 6

90 4.6 4 5 6.5

100 5.7 4.5 5.5 7

CONCLUSION

This work mainly focuses on the utilization of swarm

intelligent agents to improve the overall performance of the

network through QoS based routing. The proposed hybrid

model applies the synthetic QoS parameters as the objective

function such as throughput, delay, packet loss for the hybrid

intelligent agent to deal with an optimal path for node

routing, and the multi-agent based routing table offers an

initial path for QoS-hybrid agent algorithm. In this research

work, hybrid swarm intelligence algorithms are used to

overcome the drawbacks of the individual algorithm. The

hybrid algorithm has high speed of convergence and

searching capability to solve QoS routing effectively. Finally,

compared with the existing approach, the QoS-HGSO

algorithm obviously shows its improvement in the quality of

service of WSN including delay, packet loss and the synthetic

QoS.

REFRENCES

[1] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E.

Cayirci, Wireless Sensor Networks: a Survey, Computer

Networks, Vol. 38, No. 4, pp. 393-422, 2002.

[2] I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, ―A

survey on wireless multimedia sensor

networks,‖Computer Networks, vol. 51, no. 4, pp. 921–

960, 2007.

[3] K. Akkaya and M. Younis, ―A survey on routing

protocols for wireless sensor networks,‖ Ad Hoc

Networks, vol. 3, no. 3, pp. 325–349, 2005.

[4] T. W. Rondeau and C. W. Bostian, Artificial

Intelligence in Wireless Communications, Artech

House, Norwood, Mass, USA, 2009.

[5] Susan Rea, Dirk Pesch, Multi-Metric Routing Decisions

for AdHoc Networks using Fuzzy Logic, 1st

International symposium on wirless communications

systems, pp no. 234-237, Sept. 2004.

[6] D. B. Johnson and D. A. Maltz, Dynamic source routing

in ad-hoc wireless networks, proc. International

Conference on Mobile Computing, vol. 353, pp. 153181,

1996.

[7] Zhi Ren, Wei Guo, Unicast Routing in Mobile Ad hoc

Networks: present and future directions, Proc. 4th

International Conference on Parallel and Distributed

Computing, Applications and Technologies,

PDCAT2003, pp no. 340-344, Aug 2003.

[8] Ronald Beaubrun, Badji Molo, Using DSR for Routing

Multimedia Traffic in MANETs, proc. International

journal of computer networks and communication,

vol.2, no.1, pp no. 120-138, January 2010.

[9] Chunxue Wu, Fengna Zhang, Hongming Yang, A Novel

QoS Multipath Path Routing in MANET, proc.

International Journal of Digital Content Technology and

its Applications, Vol.4, no.3, pp no. 132-136, June

2010.

[10] R.Asokan, A.M.Natarajan, C.Venkatesh, ‖Ant Based

Dynamic Source Routing Protocol to Support Multiple

Quality of Service (QoS) Metrics in Mobile Ad Hoc

Networks‖, International Journal of Computer Science

and Security, volume (2) issue (3), pp no. 48- 56, 2003.

[11] Sushmita, Mitra, Yoichi Hayashi, ‖NeuroFuzzy Rule

Generation:Survey in Soft Computing Framework‖,

IEEE Transactions on neuralnetworks, VOL. 11, NO. 3,

MAY 2000.

[12] M. Yaghmaei, M. Baradaran, H. Talebiana Fuzzy QoS

Routing Algorithm for Communication Network, proc.

0

1

2

3

4

5

6

7

8

10 20 30 40 50 60 70 80 90 100

Th

rou

gh

pu

t

No. of Node

AODV QoS-PSO-ABC




2020

ISSN:2229-6093

10th IEEE International Conference on Communication

systems, ICCS 2006, pp.1-5, Singapore, October 2006.

[13] R. Schoonderwoerd, O.E. Holland, J.L. Bruten, L.J.M.

Rothkrantz, Ant-based load balancing in

telecommunications networks, Adaptive Behavior 5 (2)

(1996) 169–207.

[14] G.A. Di Caro, Ant Colony Optimization and Its

Application to Adaptive Routing in Telecommunication

Networks, Ph.D. Thesis, Faculté des Sciences

Appliquées, Université Libre de Bruxelles (ULB),

Brussels, Belgium, 2004.

[15] G.A. Di Caro, M. Dorigo, AntNet: distributed

stigmergetic control for communication networks,

Journal of Artificial Intelligence Research (JAIR) 9

(1998) 317–365.

[16] M. Dorigo, T. Stützle (Eds.), Ant Colony Optimization,

MIT press, 2004.

[17] S. Goss, S. Aron, J.L. Deneubourg, J.M. Pasteels, Self-

organized shortcuts in the Argentine ant,

Naturwissenschaften 76 (1989) 579–581.

[18] G.A. Di Caro, Ant Colony Optimization and Its

Application to Adaptive Routing in Telecommunication

Networks, Ph.D. Thesis, Faculté des Sciences

Appliquées, Université Libre de Bruxelles (ULB),

Brussels, Belgium, 2004.

[19] M. Farooq, G.A. Di Caro, Routing protocols inspired by

insect societies, in: C. Blum, D. Merkle (Eds.), Swarm

Intelligence, Introduction and Applications,Natural

Computing Series, Springer-Verlag, 2008, pp. 101–160.

[20] H.F. Wedde, M. Farooq, A comprehensive survey of

nature-inspired routing protocols for telecommunication

networks, Journal of System Architecture 52 (8) (2006)

461–484.

[21] K.M. Sim, W.H. Sun, Ant colony optimization for

routing and load balancing: survey and new directions,

IEEE Transactions on System, Man and Cybernetics 33

(5) (2003) 560–572.

[22] M. Jankowska, M. Schut and F. Schut, ―A Wireless

Actuator-Sensor Neural Network for Evacuation

Routing‖, Proceedings of IEEE SENSORCOMM,

(2009) June 18-23; Athens, Greece.

[23] S. Z. Huang and L. Y. Huang, ―The Optimal Routing

Algorithm of Communication Networks Based on

Neural Network‖, Proceedings of IEEE ICICTA, (2010)

May 11-12, Changsha, China.

[24] B. B. Jiang, H. M. Chen, L. N. Ma and L. Deng, ―Time-

Dependent Pheromones and Electric-Field Model: a

New ACO Algorithm for Dynamic Traffic Routing‖,

International Journal of Modeling, Identification and

Control, vol. 12, no. 1-2, (2011).

[25] X. M. Wang and X. M. An, ―An Energy and Location

Aware ACO Based Routing Algorithm for Wireless

Sensor Networks‖, Acta Electronica Sinica, vol. 38, no.

8, (2010).



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Hybrid Glowworm Swarm Optimization (HGSO) agent for Qos ... · this work a new efficient and energy aware multipath routing algorithm designed called as a novel hybrid glowworm swarm