Simulation of Mobility and Routing in Ad Hoc Networks using Ant Colony

Algorithms

Tarek H. Ahmed

Institute for Computer Science and Business Information Systems

University of Duisburg-Essen, Germany

E-mail: tarek@informatik.uni-essen.de

Abstract

Mobile Ad-hoc Networks (MANET’s) have

recently attracted a lot of attention in the research

community as well as the industry. This technology

has become increasingly important in

communication and networking. Routing is one of

the most important and difficult aspects in ad hoc

network since ad hoc network topology frequently

changes. Conventional routing algorithms are

difficult to be applied to a dynamic network

topology, therefore modeling and design an efficient

routing protocol in such dynamic networks is an

important issue. One of the meta-heuristic

algorithms which is inspired by the behavior of real

ants is called Ant Colony Optimization (ACO)

algorithm, it can definitely be used as a tool to tackle

the mercurial scenarios present in this dynamic

environment. In this paper, I have designed a model

which combines ant colony behavior and queuing

network analysis to evaluate End-to-End packet

delay in MANET.

1. Introduction

Mobile ad hoc network (MANET), or simply ad

hoc network,is one of the most innovative and

challenging areas of wireless networking, one which

promises to become increasingly present in our lives.

Ad hoc network consists of nodes that are freely and

dynamically self-organized (i.e. nodes are

autonomous) into arbitrary and temporary network

topology without any infrastructure support [5].

Nodes are computing and communication devices

which can be laptop computers, PDAs, mobile

phones or even sensors. Ad hoc networks offer a

large degree of freedom at a lower cost than other

networking solutions. The ease and speed of

deployment of these networks make them ideal

for disaster recovery (such as a hurricane,

earthquake or flooding), business associates

sharing information during meeting, conferencing,

and military communications as in a battlefield.

One of the main problems in mobile ad hoc

networks is to find a best route between the

communication end-points, which may be difficult

due to node mobility. Several routing algorithms

have been proposed in the literature, some of these

are DSDV,AODV,DSR,TORA and several others

[6]. The goal of every routing algorithm is to direct

traffic from sources to destinations, maximizing

network performance while minimizing cost.

In this paper, I will present a new approach for an

ad hoc routing algorithm, which is based on Ant

Colony Optimization (ACO) algorithm and its

combination with queuing network analysis. The

basic idea of the ACO meta-heuristic is taken from

the food searching behavior of real ants. While

walking, ants deposit pheromone, which marks the

route taken as they move from a food source to

their nest, and foragers follow such pheromone

trails. The concentration of pheromone on a certain

path is an indication of its usage. These pheromone

trails are used as a simple indirect form of

communication. The process of emerging global

information from local actions through small, in-

dependent agents not communicating with each

other is called Stigmergy [2]. This behavior of the

ants can be used to find the shortest path in networks.

Especially, the dynamic component of this method

allows a high adaptation to changes in mobile ad hoc

network topology, since in these networks the

existence of links are not guaranteed and link

changes occur very often.

The simple ant colony optimization meta-heuristic

illustrates why this kind of algorithms could perform

well in mobile ad hoc networks by different reasons.

The main reason is that ACO meta-heuristic is based

on agent systems and works with individual ants.

This allows a high adaptation to the current topology

of the network. One other reason is the way of taking

decision about selecting the next node that is based

on the pheromone concentration on the current node

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which is provided for each possible link. Thus, the

approach supports multipath routing

[7]

. In this

paper, I present the ACO principle in a unifying

framework which also includes mobility modeling

and queuing network analysis to evaluate End-to-

End packet delay in MANET.

2. Previous work

A great deal of literature has been published in the

field of mobile ad hoc networks. Early proposals

optimize for traditional metrics such as path length or

energy use. Recently, more efforts have been spent to

use specific network parameters when specifying

routing metrics. There exists relatively little work

with regards to biologically inspired algorithms for

routing in communications networks. However, there

are a number of notable examples which show that

these concepts can provide a significant performance

gain over traditional approaches. The following

paragraphs describe a number of the most prominent

algorithms in this field.

2.1. Ant-Based Control

Ant-Based Control (ABC) is a routing algorithm

for circuit-switched networks in which routes calls

are based on the local interaction of mobile agents

[9]. Mobile agents (ant packets) traverse the network,

updating routing tables at each node depending on

the current state of the routing table as well as the age

of the packet. Routing tables consist of next hop

probabilities for each destination. Ants traveling in

one direction influence the placement of calls in the

opposite direction.

2.2. AntNet

AntNet is an adaptive routing algorithm inspired

by ant colonies to solve routing problems in wired

networks [3]. An AntNet node maintains

probabilistic entries in the routing table, indicating

the goodness of each output link for each destination.

Each node periodically sends a forward ant packet

to explore paths to a random destination. Forward

ants explore the network to find a feasible and low-

cost path, recording every node it visits. Once it

arrives at the destination, it is converted into a

backward ant. The backward ant returns to the source

node following the path in reverse. Each

intermediate node updates its routing tables with

the information from the backward ant. Ants

interact and communicate indirectly by updating the

routing tables, thus collaboratively solve the global

network routing optimization problem.

2.3. Mobile Ants Based Routing

Mobile Ants Based Routing (MABR) is

introduced as the first routing algorithm for

MANET’s inspired by social insects [8]. The

approach presented in AntNet is extended to ad hoc

networks by abstracting the network into logical

links and nodes based on relative node location.

Location data is assumed from positioning

devices. An optimized greedy routing algorithm is

used to forward messages between logical nodes.

2.4. Ant Colony Based Routing Algorithm

This algorithm (ARA) presents a detailed routing

scheme for MANET’s, including route discovery

and maintenance mechanisms [7]. Route discovery

is achieved by flooding forward ants to the

destination while establishing reverse links to

the source. A similar mechanism is employed in

other algorithms such as AODV. Routes are

maintained primarily by data packets as they flow

through the network. In the case of a route failure,

an attempt is made to send the packet over an

alternate link. Otherwise, it is returned to the

previous hop for similar processing. If the packet is

eventually returned to the source, a new route

discovery sequence is launched.

3. Simulation algorithm

In this section, I will describe in details the

proposed framework for routing algorithm based on

ACO algorithms and Kleinrock’s delay analysis to

find the best route with minimum End-to-End packet

delay in a MANET. Figure 1 illustrates a complete

scenario of the simulation process using both ant and

delay analysis algorithms.

3.1. The initialization step

3.1.1. Mobility models. The simulation environment

consists of a set of mobile nodes (MN’s) with bi-

directional wireless links that are moving on the

simulation area (rectangular area) according to one of

two different mobility models, Random Waypoint

Mobility (RWM) model and Boundless Simulation

Area Mobility (BSAM) model [10].

RWM model states that in the beginning of the

simulation, each MN picks a random destination in

the area, traverses to that destination in a straight line

at a uniform speed, then each MN is staying in its

location for a certain period of time called pause

time. When this time does expire, each MN chooses a

new random destination, then they travel towards the

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newly chosen destination by the same way and so on

as shown in Figure 2. RWM model attempts to make

the movement of the nodes more realistic by allowing

the nodes to remain stationary for a period of time

(the pause time) before moving to a next location.

Figure 1. Flowchart for complete scenario

In the BSAM a relationship between the previous

and current movement direction ș and velocity v

exists in order to limit the change in direction and

speed per time unit to generate more realistic

movement patterns. Both the velocity vector

V = (v,ș) and the MN’s position (x,y) are updated at

every ¨t time steps according to formulas in [10] to

generates a new topology. An other speciality of this

mobility model has given it its name, the rectangular

simulation area is folded to form a torus with the

effect that nodes moving out of the simulation area

enter it again at the opposite side thus creating a

boundless simulation area (e.g. a node leaving the

simulation area at the top enters at the bottom again)

as shown in Figure 3. Since the changes in direction

and speed are limited, the resulting moves lack of

abrupt changes in direction and speed, which makes

the movement patterns more realistic. This realism

only persists, if the components working with the

resulting movements are aware of the speciality of

the boundless simulation area.

Figure 2. RWM model

Figure 3. Rectangular simulation area

mapped to a torus in the BSAM model

3.1.2. Timers. We define two different timers at the

beginning of the simulation, T

Simul

is a timer for the

whole simulation time and T

ant

= pause time is the

time between changing from one network topology to

another topology.

3.1.3. Initial routing tables. Every node has a table

of next-hop probabilities to each destination. Each

row in this table corresponds to a destination and

each column corresponds to a neighbor within its

current transmission range. The entries in the table

are the probabilities of taking a next-hop at a certain

node to eventually reach a certain destination.

3.2. Kleinrock’s delay analysis

We sketch how End-to-End delay through the

network can be calculated using Kleinrock’s

independence assumption. The expected response

time (delay) to send packets from a source node S to

a destination node D is the sum over the response

times at all links and nodes visited along the way [1]:

250 m

Closed Coverage

Area

X

max

, Y

max

X

max

, 0

0, Y

max

0, 0

Each forward ant

move after using

R

oulette Wheel

selection on the

new probabilities

ij

p

′

Start

Initial network topology,

Initial routing table with probabilities P

ij

,

Simulation-Timer T

simul

, Ant-Timer T

ant

Initial parameters Ȝ ,

µ

i

,B

ij

E[V] calculated by

QN-algorithms

Ant Algorithm

New Routing tables

probabilities

ij

p

′

E[V] calculated by

QN-algorithms

N

ew E[V]

less than

Old E[V]

?

End of

T

ant

?

End of

T

ant

?

Each forward ant

move after using

R

oulette Wheel

selection on the

old probabilities

P

ij

End of T

simul

?

Random Waypoint

Mobility Model

N

ew Network

Topology

End

N

o

N

o

N

o

Yes

Yes

Yes

Yes

N

o

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][][)],([

+=

nij

REREDSRE

Where

ijij

ij

C

RE

λµ −

=

1

][

, is the expected delay at

all links,

µ

C

ij

is the number of packets that can be

transmitted over a link ij (packets/sec.), and

λ

ij

is the

arrival rate over a link ij.

][

1

][

][

n

n

nn

n

SE

SE

RE +

−

=

ρ

ρ

, is the

expected delay at n nodes with E[S

n

]=1/

µ

n

and

ρ

n

=

λ

n

/

µ

n

.

3.3. Ant routing algorithm

The work reported here follows the AntNet

algorithm [4], and is informally summarized as

follows:

•

Each node in the network retains a record of packet

destinations as seen on data packets passing

through that node. This is used to periodically, but

asynchronously, launch ‘forward’ ants with

destinations stochastically sampled from the

collected set of destinations.

•

Once launched, a forward ant uses the routing table

information to make probabilistic decisions

regarding the next hop to take at each node. While

moving, each forward ant collects time stamp and

node information in a stack, which is later used to

update the routing tables along the path followed.

The trip time to reach the desired neighbor is

computed using this simple formula:

d

ij

+ (q

ij

+ S

a

)/ B

ij

(1)

Where d

ij

is the link’s propagation delay

(distance/signal propagation speed) between two

mobile nodes i and j. Note that this value can be

neglected because the distance value is very small

in comparison to the value of signal propagation

speed.

The number of data packets waiting in the queue

between nodes i and j is q

ij

and is calculated by

using the M/M/1 equation

ij

ij

ij

q

ρ

ρ

−

=

1

2

,

where

j

ijj

ij

P

µ

λ

ρ

×

=

is the utilization of the link

between two nodes i and j, and

λ

j

and

µ

j

are the

arrival rate and service rate at node j respectively.

P

ij

is the probability of routing from node i to j.

S

a

is the size of the ant packet, and B

ij

is the

bandwidth of the link between two nodes i and j.

Bandwidth is here defined as the amount of data

that can be transmitted in a fixed amount of time,

expressed in bits per second (bps).

Equation (1) represents the time delay for links.

We suppose that the node delay is also determined

by M/M/1 formulas for all nodes;

•

If a forward ant re-encounters a node previously

visited before reaching the destination, it is killed

(in other words, identification of a loop in the

path);

•

On successfully reaching the destination node, the

total trip time is calculated, and the forward ant is

converted into a backward ant;

•

The backward ant returns to the source using

exactly the same route recorded by the forward ant.

Instead of using the data packet queues, the

backward ant uses priority queues;

•

At each node visited by the backward ant, the

corresponding routing table entries are updated to

reflect the relative performance of the path. The

backward ant retraces the path of the forward

ant by popping the stack, making

modifications in the routing tables at each

intermediate node according to the following

learning rules:

IF

(node was in the path of the ant)

THEN

p(i) = p(i) + r [1-p(i)]

ELSE

p(i) = p(i) - r p(i)

Where r

∈

(0,1] is the reinforcement factor central

to express path quality. The reinforcement factor

should be a factor of trip time. This factor is given

by the following relationship, r=t

1

/t

2

where t

1

is the

minimum trip time of all the forward ants, and t

2

is

the trip time of the current forward ant from a node

to the destination node.

•

When the backward ant reaches the source, it dies.

My simulation model can be summarized as follows:

Given a starting topology, then its delay is calculated.

After applying the ant algorithm on the current

topology, the probabilities in routing tables are

altered a little bit and then, the delay is recalculated.

If the new delay is better, it definitely becomes the

starting point for the next iteration and each forward

ant will move by applying roulette wheel selection on

the new probabilities after updating. If the delay is

worse each forward ant will move by applying

roulette wheel selection on the old probabilities

before updating. This has the effect that, nearly every

new solution is adopted, while over time it becomes

more and more likely that only better solutions are

accepted.

This comparison between the delay of each

successive iterations repeats until the ant timer T

ant

finishes, then if the simulation timer T

Simul

hasn’t

finished yet, a new topology of the network will be

established according to one of the mobility models

RWM model or BSAM model and repeats again the

same procedure until the simulation timer finishes.

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4. Performance evaluation

In this section we evaluate the End-to-End delay

E[V] for the packets through the MANET using the

framework described in the preceeding section.

4.1.Simulation model

Simulation scenario consists of a number of nodes

that are initially placed randomly and constantly

moving in a simulation rectangular area 1000

×

800

m

2

according to RWMand BSAMmodels.

During the simulation by RWM model, nodes are

free to move anywhere within this area. Each node

travels towards a random spot, then take a rest period

of time in second. After the rest period, the node

travels towards another randomly selected spot. On

the other hand, in case of BSAM model, all MN’s are

moving according to two parameters, velocity v as

well as direction ș of the MN’s.

The movements process in both mobility models

RWM and BSAM repeats throughout the simulation,

causing continuous changes in the topology of the

underlying network, followed by a simulation of the

ant behaviour yielding an improvement of the routing

tables, which is evaluated by Kleinrock´s delay

analysis. Finally, we get the minimum delay from

source to destination node. The simulation program

has been executed on standard 350 Mhz PC using

Visual Basic 6.0. It needs few seconds of CPU time

for a single simulation run.

The model parameters that have been used in the

following experiments are summarized in Table1.

Table 1. Simulation parameters

Parameter

Value

Number of nodes 10, 20, …, 150

Arrival rate 150 Kbps

Transmission range 250 m

Velocity / Direction 10 m/sec & 45 degree

Packet size 64 byte

Pause time (Ant time) 10, 20, …, 100 sec.

Simulation time 180 sec.

Link bandwidth 1 Mbps

Simulation area 1000 m × 800 m

Mobility model RWM & BSAM models

Routing protocol Ant algorithm + Delay

Analysis by Kleinrock

4.2. Results

The first experiment shows the relation between

increasing the number of nodes and the End-to-End

delay in case of using different numbers of nodes

(10, 20, … , 150) while ant time is constant at 30 sec.

Figure 4 shows that increasing the number of nodes

results in an increase in the delay, because each hop

can contribute a substantial amount of delay in

forwarding traffic. Furthmore, the more nodes, the

more congestion, and the longer it takes to discover

routes.

Ant time = 30 sec

0.00

0.10

0.20

0.30

0.40

0.50

0.60

10

30

50

70

90

110

130

150

Number of nodes

Mean delay

[sec]

Figure 4. Number of nodes vs. delay

The second experiment investigates the relation

between increasing the ant time and the End-to-End

delay in case of using different pause times (10, 20,

…, 100 seconds), and 60 nodes.

Number of nodes = 60

0.068

0.072

0.076

0.080

0.084

0.088

10 20 30 40 50 60 70 80 90 100

Ant time [sec]

Mean delay [sec]

Figure 5. Ant time vs. delay

Figure 5 shows that increasing the pause time

leads to a decrease in the delay, because the ant

algorithm performs more iterations which help to

approach the minimum delay.

The third experiment is done with RWM and

BSAM models for a scenario with 60 nodes

distributed randomly, and an ant time of 60 seconds.

The whole simulation time is 180 seconds and we

observe the packet delays over this time period.

Number of nodes = 60

0.076

0.078

0.080

0.082

0.084

0.086

0.088

5 10 15 20 25 30 35 40 45 50 55 60

Ant time [sec]

Mean delay [sec]

Figure 6. Mean delay through 1st

topology

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Figure 6 shows the mean delay for the first topology.

Figures 7 and 8 show the mean delay for the

topology which has been established from the

preceding topology by applying the mobility model.

The following figures do display 95% confidence

intervals.

Number of nodes = 60

0.076

0.078

0.080

0.082

0.084

0.086

65 70 75 80 85 90 95 100 105 110 115 120

Ant time [sec]

Mean delay [sec]

Figure 7. Mean delay through 2nd

topology

Number of nodes = 60

0.068

0.070

0.072

0.074

0.076

0.078

0.080

0.082

125 130 135 140 145 150 155 160 165 170 175 180

Ant time [sec]

Mean delay [sec]

Figure 8. Mean delay through 3rd

topology

Figure 9 summarizes the tracing of the delay over

the whole simulation time of 180 seconds for three

different topologies. It is clear that, there is no

significant effect on the delay between RWM and

BSAM models, but the performance can vary

significantly with other different mobility models as

stated in [10]. After a change in the topology, we

observe a performance improvement due to the work

of the ant algorithm which converges step-by-step

towards the minimum possible delay.

0.065

0.07

0.075

0.08

0.085

0.09

10

30

50

70

90

110

130

150

170

Ant time [sec]

Mean delay [sec]

BSAM

RWM

Number of nodes = 60

Figure 9. Packet delay over 180 sec.

5.Conclusion and future work

A routing algorithm for mobile wireless ad-hoc

networks has been presented together with a hybrid

simulation framework which has been established to

perform model experiments that give insight into the

behavior of the proposed ant routing protocol.

The simulation experiments showed that the

considered ant algorithm is able to cope with this

type of dynamic networks, in particular its ability to

improve the system performance which has been

reflected in the model. It is also showed that, there is

no significant effect on the delay between RWM and

BSAM models.

Experiments with other scenarios (e.g. other

mobility models, other traffic models using multiple

source nodes and multiple destination nodes) are

necessary to show its general applicability. In

particular the comparison with other routing

algorithms will be in the main focus of future work.

6. References

[1] B. Haverkort, “Performance of Computer

Communication Systems, A Model-Based Approach”, John

Wiley & Sons, Ltd., 1998.

[2] D. Corne, M. Dorigo, and F. Glover (Eds.), “New ideas

in optimization”, Maidenhead, UK: McGraw-Hill, 1999.

[3] G. Di Caro, and M. Dorigo, “AntNet: A mobile agents

approach to adaptive routing”, Technical report TR-97-12.

Université Libre de Bruxelles, IRIDIA, 1997.

[4] G. Di Caro, and M. Dorigo, “AntNet: Distributed

stigmergetic control for communications networks”, In

Journal of Artificial Intelligence Research (JAIR) 9, 1998,

pp. 317–365.

[5] I. Chlamtac, M. Conti, and J. Liu, “Mobile ad hoc

networking: imperatives and challenges”, Ad Hoc

Networks, No. 1, 2003.

[6] M. Elizabeth, and T. Chai-Keong, “A Review of

Current Routing Protocols for Ad Hoc Mobile Wireless

Networks”, IEEE Personal Communications, 1999.

[7] M. Günes, U. Sorges, and I. Bouazizi,“ARA-The Ant-

Colony Based Routing Algorithm for MANETs”,

International Conference on Parallel Processing

Workshops (ICPPW’02), IEEE Computer Society Press,

2002, pp.79-85.

[8] M. Heissenbüttel, and T. Braun, “Ants-Based Routing

in Large Scale Mobile Ad-Hoc Networks”,

Kommunikation in Verteilten Systemen (KiVS), 2003.

[9] R. Schoonderwoerd, O. Holland, J. Bruten, and L.

Rothkrantz, “Ants for Load Balancing in Tele-

communications Networks”, Adaptive Behavior, Vol. 5,

No. 2, 1997, pp. 169-207.

[10] T. Camp, J. Boleng, and V. Davies, “A Survey of

Mobility Models for Ad Hoc Network Research”, In

Wireless Communication & Mobile Computing (WCMC),

Vol. 2, No. 5, 2002, pp. 483-502.

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