A Mobile Multi Agent System for Routing in Adhoc Network

M. A. Riahla

, K. Tamine

, A. Mifdal

and M. Mezghiche

LIMOSE Laboratory, M'hamed Bougara University, South Campus 35000 Boumerdes, Boumerdes, Algeria

XLIM Laboratory, Limoges University, Limoges, France

Keywords: Dynamic Network, Mobile Agent, SMA, MANET, Routing Protocol, Distributed Algorithm.

Abstract: Dynamic networks are a challenge for the deployment of distributed applications on autonomous machines.

But, these networks can meet problems with implementation of services such as routing and security in

general. In this sense, the multi-agent systems are well suited for the design of distributed systems where

several autonomous agents interact or work together to perform some set of tasks or satisfy some set of

goals and moving the problem of analyzing from a global level to a local level and then reduce the

complexity of the design (Ferber, 1997) In this paper we present a generic model Multi Agent system that

we adapt to develop a new routing protocol for ad hoc networks. Wireless ad hoc networks are

infrastructureless networks that comprise wireless mobile nodes able to communicate each other outside

wireless transmission range. Due to frequent network topology changes in one hand and the limited

underlying bandwidth in the other hand, routing becomes a challenging task. In this paper we present a

novel routing algorithm devoted for mobile ad hoc networks. It entails both reactive and proactive

components. More precisely, the algorithm is based on ant general behavior, but differs from the classic ant

methods inspired from Ant-Colony-Optimization algorithm (Dorigo, Birattari and Stutzle, 2006). We do not

use, during the reactive phase, a broadcasting technique that exponentially increases the routing overhead,

but we introduce a new reactive route discovery technique that considerably reduces the communication

overhead.

1 INTRODUCTION

Dynamic networks are a challenge for the

deployment of distributed applications on

autonomous machines. But, these networks can meet

problems with implementation of services such as

routing and security in general.

We have taken as a case study in ad hoc

networks. For this we focused on Multi Agent who

has a particular interest in the distributed problems

in general and for which it is difficult to prevent all

situations. Why the multi-agent systems are well

suited for the design of distributed systems where

several autonomous agents interact or work together

to perform some set of tasks or satisfy some set of

goals and moving the problem of analyzing from a

global level to a local level and then reduce the

complexity of the design. In this paper we present a

generic model MultigAgent system that we adapt to

develop a new routing protocol for ad hoc networks.

In multi-hop wireless ad hoc networks

(MANETs) (Royer and Toh, 1999), mobile nodes

cooperate with each other to form a network without

a fixed infrastructure such as access point or base

stations in which nodes perform routing discovery

and routing maintenance in a self-organized way.

The routing is particularly a challenging task in

MANETs. Indeed, because of the frequent changes

in the network topology triggered by nodes

displacements, establishment of new nodes

connections and nodes disconnections, the routes

discovery process is unstable. Practically speaking,

efficient routes may quickly become inefficient or

even unusable ones. To tackle this problem by

ensuring a suitable routing through reliable

algorithms, one important way is to update routing

information more regularly than in wired networks.

However, this requires more routing control

packets, which is specifically an issue in MANETs,

since the bandwidth of the wireless medium is very

limited and the medium is shared.

Beyond the routing overhead problem, our

proposed protocol also attempts to solve the

problems of packet delivery ratio and end-to-end

delay. For this purpose, we propose a hybrid method

Riahla M., Tamine K., Mifdal A. and Mezghiche M..

A Mobile Multi Agent System for Routing in Adhoc Network.

DOI: 10.5220/0004697500310039

In Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2014), pages 31-39

ISBN: 978-989-758-000-0

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

that entails both proactive and reactive processes.

The routes are established and periodically

maintained with a constant number of mobile agents.

The Agent is periodically created by each node and

thus the number of agents in the network can

continually be controlled. However, when a

connection is planned to be established by a node

with another one within the lack of a route in its

routing table, the considered node makes a route

request by setting a local variable available for

Agents passing through it.

Our model is based on the ant behavior. A

number of ant-based routing algorithms exist either

in wired (Di Caro and Dorigo,1998) or in wireless

(Caro, Ducatelle and Gambardella, 2005) (Correia,

Vaza ̃ and Lobo, 2009) (Correia and Vaza ̃, 2008)

(Laxmi, Jain and Gaur, 2006) (Bouazizi, 2002)

networks. They are based on the pheromone trail

laying-following behavior of real ants and the

related framework of ant colony optimization (ACO

(Dorigo and al, 2006). In all of these approaches, a

source node broadcasts an Agent whenever it plans

to build a route to a fixed destination. One of Agent

roles is to deposit amounts of pheromone in order to

mark optimal paths between a couple of nodes

namely source and destination nodes. Unlike these

methods, we do not exploit a broadcasting technique

that exponentially increases the routing overhead,

but we introduce a new idea through an ant-based

algorithm that consists in setting a local route

request whenever a node plans to send a data packet.

It is the role of Agent, moving within the network

during the proactive phase, to disseminate this

information and to provide routes towards the

requested destination. It should be noticed that our

protocol doesn’t deterministically establish the best

route, since the Agent are not broadcast. However,

the agents attempt to get as close as possible to the

best route.

We begin by presenting some definitions on self-

organized Multi Agent Systems and their analogy

with dynamic networks. We present in the second

part the principle of ad hoc networks and the

problem of routing with this type of network data. In

the Third part we present the architecture and mode

of operation of a Multi Agent system adapted to

generic case of dynamic systems. We detail the

instantiations of the generic model: a routing

protocol for ad networks. Before concluding, we

discuss in Part VI results and tests of the new

routing protocol using NS2 simulator.

2 MULTI-AGENT SYSTEMS AND

DYNAMIC NETWORKS:

ANALOGIES AND ISSUES

In recent years, a new topic of research has emerged:

dynamic networks (also called autonomous

systems). A dynamic and distributed network

consists of a set of auto-configurable nodes that are

constantly changing (the number of nodes and links

change over time). The topology change is also one

of the properties of these networks, because the

network nodes can join and/or leave the network

spontaneously. The main advantage of this type of

network is the fast and inexpensive deployment and

installation.

A multi agent system (MAS) is a set of agents

operating in a common environment. This set of

agents, not necessarily smart, is a complex system

which appears a collective intelligence. This

collective intelligence comes from the emergence of

a global behavior of all agents. An example of this

collective action is with the behavior of a colony of

ants that act like an entities (ants) with no cognitive

capacity but have achieved a high degree of

organization and adaptation quote. An agent is a

software entity (program) reactive, proactive and

with social skills, able to act autonomously in its

environment. Responsiveness refers to maintaining a

constant link with the environment when a change

occurs. The proactivity means that the system allows

agents to generate and satisfy its goals. Social skills

indicate that the system allows the agent to interact

or cooperate with its environment and / or with other

agents.

It is thus clear that there is an analogy between

dynamic networks and multi-agent systems. Indeed,

each node of a dynamic network is autonomous

because it is not controllable by any other node on

the network, is reactive because it can act as a server

for other nodes, and can also be proactive in the case

of his client node status, and finally it is social

because it communicates and cooperates with other

nodes in the network.

In this context, an inherent issue in the

management of dynamic networks is: definition of a

data routing protocol in the case of ad hoc networks.

3 ROUTING ISSUES IN AD HOC

NETWORK

In multi-hop “wireless” ad hoc networks (MANETs)

(Royer and Toh, 1999), “mobile nodes” cooperate

PECCS2014-InternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

with each other to form a network “without a fixed

infrastructure” such as access point or base stations

in which nodes perform routing discovery and

routing maintenance in a self-organized way. Due to

frequent network topology changes in one hand and

the limited underlying bandwidth in the other hand,

routing becomes a challenging task.

Several types of routing protocols have been

specifically designed for ad hoc networks and have

been classified into two main categories: reactive

and proactive protocols. In reactive routing protocols

such as AODV (Perkins and Royer, 1997) (Ad Hoc

On demand Distance Vector) and DSR (Johnson

Maltz and Broch, 2001) (Dynamic Source Routing),

the routes are only discovered when required in

order to save node and network resources, while in

proactive routing protocols such as OLSR (Clausen

and Jacquet, 2003) (Optimized Link State Routing

Protocol) and DSDV (Perkins and Bhagwat, 1993)

(Destination Sequenced Distance Vector) the routes

are established in advance, avoiding consequently

the delays that occur during the discovery of new

routes. The problem raised by proactive protocols

consists in the routing overhead, especially when

there are frequent topology changes. This is highly

inefficient when updating routes that rarely carry

traffic. A reactive protocol is, in contrast, much

more appropriate for such situations, since it

generates lower overhead in terms of used

bandwidth.

There is another kind of protocol that combines

both reactive and proactive approaches called hybrid

routing protocols. In this paper, we focus on a

particular class of hybrid routing protocols based on

an optimization technique known as ant colony

optimization (ACO) (Caro, Ducatelle and

Gambardella, 2005) (Correia, Vaza ̃ and Lobo,

2009) (Correia and Vaza ̃, 2008) (Laxmi, Jain and

Gaur, 2006) (Bouazizi, 2002) which are inspired

from the foraging general behaviour of some ant

species. The ant underlying behavior can be

summarized as follows: ants deposit pheromone on

the ground in order to mark some favorable paths

that should be followed by other members of the

colony, for instance, ants walking to and from a food

source deposit on the ground a substance called

pheromone. Other ants perceive the presence of

pheromone and tend to follow paths where

pheromone concentration is higher. Through this

mechanism, ants are able to transport food to their

nest in a significant effective way.

Several properties belonging to ant-based routing

algorithms are strongly appropriate to address the

problems inherent in MANETs: they are highly

adaptive to network changes, robust to agent

failures, and provide multipath routing. However,

since they mainly rely on repeated path sampling, a

significant overhead can be induced within native

routing algorithms. Several ant-based routing

algorithms for MANET have been proposed in state

of the art previous work. However, within the

attempt to limit the overhead caused by the ants,

these algorithms considerably loose the inherent

proactive sampling and exploratory properties

belonging to the ants behavior adopted in the

original ant-based algorithms.

4 A MULTI AGENT SYSTEM FOR

GENERIC AND DYNAMIC

NETWORKS

4.1 Architecture and Operating Mode

for Multi Agent System

The Generic Multi Agent System that we propose to

manage services in dynamic networks is composed

of two types of agent communities (see figure 1).

Both agent communities interact through different

types of communication which we will present later.

The first community of agents is SMA_Node

will manage the functions of mobile nodes in the

dynamic network. Each agent will be called node,

then represent a network node whose features will

be explained below. Each node agent will be able to

provide a set of network data packets are called

packet. This set of data packets will be the second

community of agents that will call SMA_Packet.

Packet agents will travel along the dynamic network

in a completely random way according to some

metric contained in the node agents and packet

agents at the interaction between these two types of

agents.

Figure 1: General scheme of our model.

AMobileMultiAgentSystemforRoutinginAdhocNetwork

Moving a packet agent to a node agent network

is defined by a behaviour of packet agent which we

will define later.

Each node agent SMA_Node from community

will be defined generically by the following

behaviours:

 Detect_neighbors () return Liste_node

This function allows the node agent to be able to see

all the node agents that are its "scope" and with

which it can communicate directly.

 Connect (v: node)

This function allows the node agent to start a

connection with its neighbor node V in order to

establish a communication with him.

 Disconnect (v: node)

This function allows the node agent to delete a

connection with a neighbor node. This function also

has the effect of removing the node agent v from its

list of neighbor nodes.

 Connect ()

This function allows a node agent to join a network

of node agents.

 Disconnect ()

This function disconnects the agent from network.

 Generate () return packet

This function allows the agent to create and

distribute in the network a new packet agent.

 Read_info (p: packet, info: information)

This feature allows the agent to read and get

information broadcast over the network and carried

by the packet agent p as it moves from node to node.

 Write_info (p: packet, info: information)

This feature allows the agent to write information

into the packet agent p so that it will be distributed

in the network.

 Move (p: packet, n: node)

This function allows the node agent to send the

packet agent p to another node neighbor agent (node

agent n). It is this feature that allows the distribution

of packet agents in the network.

Each agent from community SMA_packet will

be defined generically by the following behavior:

 Create ()

This feature allows the packet agent to be created by

the creator node agent;

 Delete ()

This feature allows the packet agent to be destroyed

by its creator node agent;

 Transfert (n: node, info: information)

This feature enables the packet agent to transfer the

information carried to the current node where it is

located. This feature will be used by the current

node agent to perform a read of the information in

the network;

 Update_packet (n: node, info: information)

This function allows the packet agent to update its

data with information provided by the current node

agent. This feature will be used by the current node

agent in order to broadcast information in the

network;

 Choice_ displacement (n: node) return node

This feature lets you know randomly and according

to some metric contained in the node agent n and in

the current packet agent, the next node where the

current packet agent will move;

 Move (init: node, final: node)

This function allows the packet agent to move from

the node agent init to node agent final. The move

action will be achieved through the move () function

of init node agent.

4.2 Application of the Model in the

Case of Routing in Ad Hoc

Networks: PROTOCOL

AgentRouting

The main idea of the protocol AgentRouting is to

build a multi-agent based system where each node

provides several kinds of agents. Regarding the

purposes of the routing task, we design two main

types of agents. A first mobile agent, called Ant-

Agent, is responsible of establishing routes. A

second mobile agent, called Rectifier-Ant, is issued

by a node whenever a change in the network

topology is detected. Our protocol is a complete

multi-agent based system, where an agent works

independently from the others. This fits very well

spontaneous networks such as wireless ad hoc

networks, because of the very high mobility and

self-organization properties of this type of networks.

Our protocol inherits from the advantages of this

kind of model: autonomous work, distributed

intelligence and robustness. Furthermore, the use of

mobile agents allows to easily extending the

functionalities of a protocol by simply adding other

agents or by assigning other functionalities to

existing ones.

AgentRouting protocol is based on a hybrid

algorithm. In the proactive phase, the protocol uses

mobile Agent as follows: each node (Origin-Node)

periodically creates one Ant-Agent that moves

across the network from one node to another and

builds paths from the current node to its Origin-

Node and paths from the current node to the last

visited one when the Ant-Agent returns back to its

PECCS2014-InternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

Origin-Node (the Ant-Agent has two phases: a Go-

phase when it is sent by its Origin-Node and a Back-

Phase when it returns to its Origin-Node).

When a data session is started between a source

node s and a destination node d, s checks whether it

has up-to-date routing information about d. If not,

the node s makes a local route request. In our case,

the route request is not broadcast to every node as it

is the case in a classic ant routing protocol (Di Caro

and Dorigo,1998) (Laxmi, Jain and Gaur, 2006)

(Bouazizi, 2002) , but it is stored on the node s. The

broadcast task is assigned to the Agent (created

during the proactive phase and moving within the

network) that have the responsibility to

''intelligently'' disseminate the route request

throughout the network. These Agent gather

information about the quality of paths they followed,

and at their arrival in node s (which contains the

route request to destination d), they return back to

their source node by tracing back the path and by

updating routing tables.

Before detailing our routing protocol, let's

consider some of these assumptions. Each node must

be able to broadcast hello messages to its one hop

neighbors. We also consider that the links between

the nodes of the networks are always bi-directional.

Moreover, as the protocol must operate in an ad hoc

environment where nodes are highly mobile, the

routing protocol must take into account this

constraint and be responsive to these frequent

changes of the topology. Therefore, our routing

protocol will consist of three modules: neighborhood

discovery module, path discovery module and

managing broken links module. In this paper, we

focus our contribution on the two latter modules.

4.2.1 Proactive Phase

AgentRouting Protocol is a routing protocol for

wireless ad hoc networks based on mobile Agent. To

establish routes between nodes, our protocol uses

mobile agents which are periodically created by each

network node. An Ant-Agent belongs to a single

node called Origin-Node. An Ant-Agent moves

across the network from one node to another. When

it reaches a node, the Agent establishes and builds,

in its memory and in the routing table of the node, a

path between this node and its origin node.

Thereafter, the Agent chooses a next hop among its

neighbors in a stochastic manner and proportionally

to the amount of pheromone deposited by the other

Agent during their Back-Phase.

To avoid routing loops, we assign a unique

identifier <node_ID, Ant_ID> to agents, that is

incremented at each creation of a new Ant-Agent. If

a node receives several times the same Ant-Agent, it

accepts the information given by the first one and

ignores the others.

In order to monitor its Ant-Agent, a node assigns

a configurable Time To Live (TTL) to agents whose

value is proportional to the network dimension and

is decremented at each hop. This means that an Ant-

Agent will have two phases during its life cycle: Go-

Phase where the agent builds a path from the current

node to its Origin-Node and a Back-Phase where the

agent follows a reverse path from the one followed

during its first phase (the Ant-Agent saves in its

memory a reverse path during its Go-Phase). At

each node visited during the Back-Phase, the Ant-

Agent builds and stores in the routing table of this

node a path from this current node to the last node

visited during the first phase (when TTL=0). This is

the first step in the routing discovery process which

is proactive.

4.2.2 Reactive Phase

During the proactive phase, a large number of paths

are built. However, when a node s plans to send or

forward data packets to an unknown destination

node d, it triggers a route request process where the

route request is locally saved. When an Ant-Agent,

during its Go-Phase, visits a node which has made a

route request (a node can have several route

requests), the Ant-Agent switches to its Back-phase

and deposits an amount of pheromone on each node

of the reverse path towards its origin node. This

mechanism is used to mark the paths towards the

node s and thus inform the other nodes (and Agent)

about this route request. The amount of pheromone

deposited by the Ant-Agent is defined by the

following equation (1):

= Q

-1

)

+ q (1)

Where Q

is the pheromone level in the node n

time t and q is a positive constant (we choose q=0.1

for our simulations).

During its Go-Phase, a node chooses the next

hop in a stochastic manner and proportionally to the

amount of pheromones; this process increases the

likelihood to select a path towards a claimant node

without penalizing the other paths. Choosing the

next node randomly and proportionally to the

amount of pheromones allows us to increase the

number of agents towards the claimant node s. On

the one hand, this approach increases the chances of

having an Ant-Agent issued from the destination

node d (i.e., the Agent's source node is the

destination d). On the other hand, it allows to

AMobileMultiAgentSystemforRoutinginAdhocNetwork

quickly reaching nodes that have already established

paths towards the destination d.

4.2.3 Stochastic Data Routing

In our protocol, the nodes stochastically forward the

Agent. When a node has several neighbors

concerned by nodes that made a route request, it

randomly selects one of them with the probability p.

Each neighbour can have a quantity of

pheromone related to nodes which made a route

request.

Let's consider N (n) the set of n's neighbors and

the amount of pheromone associated to a

neighbor n

stored in the routing table of the node n

at time t.

The expression that gives the probability p to

select a next hop n

from node n is defined in

equation (2).

P = Q

/ ƩQ

(2)

In order to consider route requests in an equitable

manner leading to a self-organizing system and a

better management of frequent changes in the

network topology, we propose to set up an

evaporation process. This latter allows to no longer

take into account the old route requests already

satisfied. At each time interval, the amount of

pheromone corresponding to each route request is

decreased as defined in equation (3):

= (1-α) * Q

-1

)

(3)

Where Q

is the amount of pheromone related to

a claimant node s, stored in the node n

at time t and

α is a real (0<α<1) (we choose α= 0.1 for our

simulations).

5 TESTS AND SIMULATIONS

RESULTS

5.1 Ad Hoc Simulations and Results

We evaluate our routing protocol through a serie of

simulation tests. We compare its performance with

AODV (Perkins and Royer, 1997), DSDV (Perkins

and Bhagwat, 1993) and AntHocNet (Caro,

Ducatelle and Gambardella, 2005).

We have evaluated our routing protocol under

the NS2 environment.

In each simulation, which lasts 500s, the

maximum speed in the scenario is fixed to 30m/s,

the sending frequency of Agent is set to 0.5s, the

evaporation frequency is set to 0.5s, the evaporation

rate α is set to 0.1 and the updating value of

pheromone q is set to 0.1. The traffic is randomly

generated (the communications are established by

randomly choosing pairs of nodes). A

communication consists in sending 512 bytes

packets by using UDP protocol. The total number of

data packets ranges from 700 to 1000 packets per

simulation.

The following experiments show a comparison

of our protocol with three other routing baseline

protocols: AODV (a reactive protocol), DSDV (a

proactive protocol) and AntHocNet (an ant based

routing protocol). For this purpose, we choose a

network with an area of 500x500 m

. The evaluation

metrics used in the experiments are the following:

 The number of lost packets: this metric

measures the number of packets which are not

delivered to their destinations; it gives us, on

the one hand, quantitative information about

the robustness of our protocol and, on the

other hand, information about the network

congestion;

 The end-to-end delay: this metric represents

the average delay between the packet sending

time and its reception time;

 The total size of control messages generated

by a protocol.

We have computed these metrics by varying,

during the simulation time, the number of nodes, the

number of data packets and the speed of nodes.

5.1.1 The Packet Loss

The packet loss metric allows us to evaluate and to

compare the robustness and the effectiveness of the

four protocols. Figure 2 and 3 show the variation of

% packet loss considering respectively the variation

of the number of nodes and the total number of data

packets. It is clearly shown in these figures that our

scheme, DSDV protocol and AntHocNet protocol

have less packet loss than AODV protocol. The

number of lost packets increases very quickly in

networks of more than 70 nodes and in networks

with high traffic. We can explain these results by the

fact that the AODV protocol uses a broadcast

mechanism that generates a very important overhead

and this fact is aggravated by the proliferation of

collisions, which overloads the system and generates

a very large number of non-accomplished

transmissions. In the case of our protocol, these

results can be explained by the fact that we use an

efficient reactive route discovery procedure instead

of using a broadcast mechanism. Figure 4, which

PECCS2014-InternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

Figure 2: Packet loss ratio according to the number of

nodes.

Figure 3: Packet loss ratio according to the total number of

data packets.

Figure 4: Packet loss ratio/nodes speed.

represents the variation of % packet loss considering

the variation of nodes speed, shows that our protocol

have less lost packets than AODV and AntHocNet

protocols when we vary the nodes speed. This result

may be partly explained by the fact that, in the case

of our protocol, the link failures are partly supported

by the proactive phase.

5.1.2 The End-to-End Delay

Figures 5, 6 and 7 show the average times of

transmission in each protocol, considering

respectively the number of nodes in the network, the

total number of packets and the nodes speed. We can

see that our protocol and the DSDV protocol

generate less important delays than those generated

by the AODV and AntHocNet protocols. Our

protocol and DSDV protocol are both more efficient

Figure 5: End-to-end delay (s) according to the number of

nodes in the network.

Figure 6: End-to-end delay (s) according to the total

number of data packets.

Figure 7: End-to-end delay (s)/speed (m/s) of nodes.

AMobileMultiAgentSystemforRoutinginAdhocNetwork

than AODV and AntHocNet protocols, especially in

the case of a large number of nodes and data packets,

and more specifically in the case of high nodes

speeds. Regarding our protocol, this may be due to

three main reasons:

 The hybrid character of our route discovery

scheme reduces significantly the transmission

times. Indeed, thanks to the proactive phase, it

is no longer necessary to make, at each time, a

route request; the route could exist in the

routing table;

 As our protocol is multipath, it allows each

node to have several paths (towards the same

destination) whenever it wants to send a data

packet;

 Unlike AODV protocol, our protocol does not

require, during the reactive phase, a

broadcasting technique; indeed, this latter

exponentially increases the routing overhead

and thus overloads the network; consequently it

delays the paths establishment and the data

packets delivery process.

5.1.3 The Communication Overhead

The packets that consume much bandwidth are those

issued during the path discovery and maintenance

phases. Therefore, we have measured the use of

control messages by each studied protocol during

these phases. Figures 8, 9 and 10 show the

communication overhead in terms of, respectively,

control packets size according to the number of

nodes, the node activities and the speed of nodes.

We can see an important difference between our

protocol and both AODV and AntHocNet protocols.

The number of control packets increases slowly and

linearly in our protocol, either by increasing the

number of nodes or by increasing the network

traffic, while it increases rapidly in the case of

AODV protocol. This could be explained by the fact

that in the case of our protocol, the number of agents

is managed and controlled by each node and this

number is still proportional to the number of nodes

in the network and to the transmission frequency of

agents. Besides, we avoid the broadcast technique

that generates a lot of overhead. Instead, we use a

more accurate and a more intelligent technique

which only makes use of the available agents in the

network. In AODV protocol, the number of control

packets depends on many factors, including the

number of route requests that consume much

bandwidth, since they require a significant number

of broadcasts. Moreover, we can expect an important

number of collisions due to the broadcast technique

Figure 8: The communication overhead (total size (bytes)

of control packets) according to the number of nodes in

the network.

Figure 9: The communication overhead (total size (bytes)

of control packets) according to the total number of data

packets.

Figure 10: The communication overhead (total size (bytes)

of control packets) /speed (m/s) of nodes.

and to the network density. The collisions lead to the

increasing of the number of retransmissions, which

consequently induces the increasing of the total

number of packets in the network.

Figures 8, 9 and 10 show also that our protocol

generates less overhead than DSDV protocol. This

proves the effectiveness of our protocol, since the

protocol DSDV is known to be efficient in this type

of network.

PECCS2014-InternationalConferenceonPervasiveandEmbeddedComputingandCommunicationSystems

6 CONCLUSIONS

In this paper we have presented a generic model of

Multi-agent System dedicated to applications in

dynamic networks. We have shown its feasibility in

the case of the routing problem in ad hoc networks.

We have presented a novel ant-based routing

protocol for MANETs. It is a hybrid algorithm

combining reactive route setup with proactive route

probing and exploration.

Considering the simulations results obtained

using the proposed algorithm, we can argue that that

our protocol reduces significantly the routing

overhead and transmission delays. Regarding

routing overhead, our algorithm out performs

AODV and AntHocNet protocols which are

supposed to generate fewer messages than the pure

proactive routing protocols such as DSDV even if

our protocol doesn’t deterministically establish the

best path. This latter fact is mainly due to the

operating principle of our protocol: It doesn’t require

any broadcasting mechanism that leads to increase

the number of control messages. Regarding latency,

our algorithm allows also to achieve better results

than AODV and AntHocNet. This proves the

effectiveness of the proactive facet of our protocol

and the usefulness of the underlying process that

consists in computing several paths for the same

destination.

In future work we plan to improve the

exploratory working of quality of service. Our main

idea relies on the assumption that the movement of

Agent depends on the quantity of pheromone at each

node which represents in our case study, the number

of path requests in the network. Considering this

assumption, we would design an intelligent

distribution of Agent in the network according to the

needs of each zone. More specifically, we plan to

enhance our protocol with an interesting dynamic

property leading to adapt itself according to the

nodes activities in attempt to better exploit the Agent

capabilities. The quantity of Pheromones could also

be used in order to manage the number of Agent in

the network and thus control the network

congestion.

We also plan to use our multi-agent system to

another dynamic environment as a P2P network; the

principle is to use this model to ensure the

anonymity of users in a P2P network.

REFERENCES

Bouazizi, I., (2002). The ant-colony based routing

algorithm for manets. In Proceedings of the 2002

international conference on parallel processing

workshops, IEEE Computer Society, Washington, DC,

p. 79.

Caro, G. D., Ducatelle, F., & Gambardella, L. M., (2005).

An adaptive nature-inspired algorithm for routing in

mobile ad hoc networks. European Transactions ON

Telecommunications, 16, 443–455.

Clausen, T., & Jacquet, P., (2003). Optimized Link State

Routing Protocol (OLSR), IETF RFC 3626.

Correia, F., & Vaza ̃o, T., (2008). Simple ant routing

algorithm Information Networking. International

Conference, pp. 1–8.

Correia, F., Vaza ̃o, T., & Lobo, V. J., (2009). Models for

Pheromone Evaluation in Ant Systems for Mobile Ad-

hoc Networks. In Proceedings of the 2009 First

International Conference on Emerging Network

Intelligence (EMERGING ’09), IEEE Computer

Society, Washington, DC, pp. 85-90.

Di Caro, G., & Dorigo, M., (1998). Distributed

stigmergetic control for communications networks.

Journal of Artificial Intelligence Research, 9, 317–

365.

Dorigo, M., Birattari, M., & Stutzle, T., (2006). Ant

colony optimization. IEEE Computational

Intelligence Magazine, 1(4), 28–39.

Ferber, J., 1997. Les systèmes Multi agents – Vers une

intelligence collective.

Johnson, D. B., Maltz, D. A., & Broch, J., (2001). DSR:

The dynamic source routing protocol for multihop

wireless ad hoc networks. In Ad hoc networking.

Addison-Wesley Longman Publishing CO., INC.,

Boston, MA, pp. 139–172.

Laxmi, V., Jain, L., & Gaur, M. S., (2006). Ant colony

optimisation based routing on NS-2. International

Conference on Wireless Communication and Sensor

Networks (WCSN), INDIA.

Perkins, C., & Bhagwat, P., (1993). Highly dynamic

destination sequenced distance vector routing (dsdv)

for mobile computers pp. 234–244.

Perkins, C. E., & Royer, E. M., (1997). Ad hoc on-demand

distance vector routing. In Proceedings of the second

IEEE workshop on mobile computing systems and

applications, pp. 90–100.

Royer, E. M., & Toh, C.-K., (1999). A review of current

routing protocols for ad hoc mobile wireless networks.

IEEE Personal Communications, 6, 46–55.

AMobileMultiAgentSystemforRoutinginAdhocNetwork