Ant Adhoc2005

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Ant Routing Algorithm for Mobile Ad-hoc NetworksBased on Adaptive Improvement

Zeng Yuan-yuanHe Yan-xiang

School of Computer Science, Wuhan University

School of Computer Science, Wuhan UniversitySchool of Computer and State Key Lab of Software

School of Computer and State Key Lab of SoftwareEngineering, Wuhan University

Engineering, Wuhan UniversityWuhan, Hubei Province, China

Wuhan, Hubei Province, [email protected]

[email protected]

Abstract A mobile ad-hoc network is a collection of� mobile uncontrolled overheads to solve the routing problem. This large

nodes without any existing infrastructure or

central routing overhead affects the scalability of thenetwork and

administrator. A lot of research work has been developed to findaffects the network performance [1,2]. The protocol presented

a path between end points, which is aggravatedthrough the in the paper tries to solve the above drawbacks on some degree.

flexible node mobility. In this paper, an ant colonyalgorithm [5,6,7]. The ant colony algorithm based mobile ad-hoc routing

adaptive improvement on routing is presented (ARAAI).It is schemes has been presented. They can be divided intotwo

based on swarm intelligence and especially on the ant

colony classes. One is the ant-like mobile agent based routing scheme,based meta-heuristic. Considering the stagnation behavior of ant

which is a multi-agent system. Each ant is a simple agent withcolony algorithm, the method of adaptive

parameters intelligence, and migrating among nodes with ability to retrieve

coordination is put forward to construct a globallyoptimizing

node routing table to fulfill the routing function. [3,4,5,6,8].algorithm. The ns-2 simulation results show the routing protocol

The second class is ant optimization routing algorithm. Ant

is highly efficient and scalable comparing with existingAODV

and DSR protocol.searching food process is used for routing process and the

pheromone is used for next hop selecting, also can representKeywords- Ant algorithm; ad-hoc networks; routing

protocol, Qos parameter of network, such as energy [7]. The above two

routing schemes have their own characters in solving routingAdaptive

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problems. ARAAI is a protocol combining the two schemes to

I. INTRODUCTIONachieve their advantages. At the same time, due to the inherent

drawback of ant colony algorithm itself that the algorithm isA mobile ad-hoc network (MANET) is a set of

mobile slow constringed and tends to sink into local

information,nodes, which communicate over radio and do not need

any

improvement is still needed to upgrade global capability.basic infrastructure or central administrator. This

kind of

ARAAI will combine advantage of proactive routing and on-network is very flexible in the topology structure and suitable

demand routing advantages, providing multi-path, and offeringfor temporary communication links, such as military

work,

adaptive control.field medical emergency, and disaster applications. The nodesare operated by low batteries and have limit transmission areasof wireless devices. The each node in ad-hoc network

is not III. ARAAI ALGORITHMonly a wireless host but also a router device. Due to the node s�

Studies [9,10] show that ants have the ability to find themobility and dynamic topology, the routing algorithm

has shortest path between their nest and the food source.The

become a key problem to solve the application

and ability is depended on a kind of substance called pheromone,transmission quality in mobile ad-hoc networks. In this paper,

which is deposited on the path by ants. When an ant walkingthe ARAAI algorithm for mobile ad-hoc routing is

proposed on a path, it deposits pheromone on it.With time the

which is self-configured, self-built and distributedrouting concentration of pheromone decreases due to diffusion effects.

algorithm. ARAAI uses adaptive ant colony algorithminto The probability of ants backing source to choose path depends

mobile ad-hoc routing process. Considering theslow on the amount of pheromone. So the more ants visit, the larger

astringency of ant colony algorithm, the adaptive ant

colony amount of pheromone is deposited. Each node in themobile

routing algorithm is brought forward. It shows great advantagead-hoc network contains a routing table to record routing

for the mobility and dynamic topology network environment.information and a neighbor table to maintain local connectivity.

The structure of node routing table can be represented asII. RELATED WORK

(initial node, last node, heuristic value). initial node records� �

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the leaving initial place of ants, and last node records the� �

Current routing protocols for mobile ad-hocnetworks

address of the previous node. The heuristic value is local nodesuffer form certain inherent shortcomings. On one side,

the

energy information collection, and it is used for selecting routeproactive routing schemes are not suitable for

dynamic when existing multi-path. Neighbor table isrepresented as

topology, on the other side the on-demand routingschemes connection between local and other nodes. It isdefined as:

lead to great communication delay. Both of them needlarge

(neighbor, time, pheromone). Among it, neighbor is the� �

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neighbors of the current node. And time is a monitor� � that In the forward ants process, current node willselect one of

maintains the connections with its neighbors in the wayof the neighbor nodes as next hop randomly.Then the ants will

sending a hello broadcast every specific second to see ifthe update node table with last node

and pheromone. Forneighbors are still there. The pheromone represents the local� �

destination D, the probability of selecting aneighbor j is

link situation and quality, and it s a reflection of link reliability.� formula (1).

At the beginning, the pheromone in the network is a constantthen it varies with ants routing. Ants in the protocol are used

? t a t �for route building through simulating

biological behavior, t ( ) ? dij ( ) ij

j allowed� k ? a ? k�

which are simple agents with certain intelligencemigrating p dij ? ? t ( )t ? dis ( )t is(1)

t allowed

?

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between nodes to communicate computationand recall ? k

?0 otherwise�conclusion. Those ants will retrieve node with new route

tofulfill all the routing function. And the ants can offer multi-path,

tand the selection from lower energy to a higher energy route is

The amount of ants is represented by m, at node i,ij (t) is

possible. As other existing routing algorithm, ARAAI can bethe remaining pheromone in the path at time t, andt ij(0) = C

divided into two parts: route discovery and route maintenance.

(C is a constant) at the initial time. When establishing a route4.1 Route Discovery

discovery, the k (k=1,2 m) kinds of ants are created. ?� ij is the

local heuristic value of the link(i,j) between node i and node j.Route Discovery is used for creating paths from source to

Here it represents link stability because the node mobility anddestination. When source wants to

communicate with

topology can do great effect on ad-hoc routing. The stabilitydestination, it ll look up in the node routing table to see if there�

can be calculated in the following way:have existed some routes firstly. If not, a

route discovery

R d -process is created by source toward destination. The creation of

i ij

? ij Rnew routes requires the use of the forward ants and backward

M . i is the transmission range of nodeants. The ant is a mobile agent, which can

migrate in the dnetwork links to update node information and

record route i, and ij the current distance at time between iand j. M is the

searching situation. Firstly, some numbers of forward ants areaverage velocity of the nodes. The above formula assuming at

dispatched to search the routes from source to destination, andtime t, the node m starts moving outwards with an average

collect paths information and intermediate� nodes local allowed�

information as they travel. When the forward antsarrive at velocity M. The k is theallowed next-hop set for the

destination, all of them are recalled and the backward ants are

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kth ants, but the next node should be prevented form searchingdispatched by destination. The backward ants will carry with

for those who have been visited before. This isused for

allowedcorresponding forward ants information, and�

be sent back constraining to guarantee of loop free. So

the k canfollowing the reverse path of corresponding forward ants.

It be calculated in:will retrieve node routing table. Finally the

routes can bedeveloped. In all, the forward ants phase is the flooding processto searching usable paths, and the backward ants phase is the

nbset{ jallowedj } j node(2) - .� k 1i, 2, l exist

routing developed phase. Sometimes there are multi-path forusers to choose depending on the heuristic value in the

node is theneighbor set of current node i,

nbset j j j { }�routing table. Figure 2 is the ARAAI route discovery process.

1i, 2 , l

which can be searched in local node routingtable. And

Sourcenodeexist is the nodes that have been visited before. Every ant

Backwar d ant s are is a mobile agent with intelligence that has a memory

of visitedrecal l ed, and rout

edispatch Y are est abl i shed.

nodes set. They only search those nodes that theyhaven t�

forward antsNext node is

visited yet. With time goes by, the pheromonewill be

source Nevaporated and lapses at last to simulating the link stability

situation. The parameter 1- ? represents theevaporation

Node routing table Noderouting table degree. After time n, the remainingpheromone on each link

(Source, last node, Intermidiate(Destination, last node,

heuristic value) node heuristicvalue) will use the following formula to compute.

t he forward ant s ar e

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theforward ants are

dispatched to update therecalled and destination

routing table information:dispatch backward ants to

initial node = Source, last? ?

( ) * t ( ) n t ( 0 ,1)�

node=last passing path node. It updatethe routing table t ij + ? t ij + tij ? (3)

information:

is the multi-path routing, soinitial node = Destination, m k

there can be more than one last lastnode=last passing path =? ? t ij ? t ij

nodes recorded. At the same

Next node is node.It is the multi-path k 1

time, update the pheromone. N destinationrouting, so there can be

Ants will retrieve node battery morethan one last nodes

energy as heuristic value for Yrecorded. At the same time, kth

multi-path selecting. updatethe pheromone t ij represents the remaining pheromone ofthe k ants in

the path. ? t is the increment of pheromone in the path (i,j). If

ijDestination

k th ants don t come across the path (i,j), ?t k will be� value 0.

ijFigure 1. The AARA route discovery process

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The calculation of ?t ij , the ant cycle system model is takenThe formula (6) can improve the selecting probability when

? value is too big. And r is a equality-distributed randominto consideration.

thnumber in range (0,1). When ? value is too small, the rule of

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? Q If the k ant uses edge(i,j)coordinating pheromone is taken as formula (6). ?min isthe

k ? between t and t+1(4) minimum of ? (t).=? t ij ? hopcount k

?0? otherwise

t - if - t =�0 . 95

( 1 ) 0 . 95 ( 1 ) 0 . 95 ? ??

?

? t

( ) ? ( 7 )

?? min else�

In the backward ants process, the backward ants follow thereverse path of their corresponding forward ants, then update

4.2 Route Maintenancethe node routing table including last hop address and neighbor

In mobile ad-hoc network, the flexible mobility andtable including pheromone values too. The update of routing

communication interference will lead to the invalidationof

table is used for routing establishment, since theexact

some route. For example, a node between sourceand

intermediate nodes are recorded in the node route tablelast� destination is removed from the network, so the source can t�

node item accordingly between source and� destination. If

use the original route path to send data packets. Route

there are several route paths for selecting, theircomplete

maintenance phase takes the burden of supervising networkinformation will all be recorded in routing table. But only one

link state. ARAAI algorithm uses periodical hello broadcastingroute path will be taken when in communication.

The

to maintain local connectivity. The time item in neighbor� �pheromone trails encode a long-term memory about the whole

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table describes the valid period of each link, and ahello

ant search process. The basic principle of ant colony algorithm

message enquiry is taken to get knowledge of the link existenceis to find a balance between exploration and exploitation, and

after the specific time. When the network topology has some

tries to find problem optimization solution range.So the

changes, the removed node can t answer the hello message. If�pheromone value on the established routing path need to be

the current node has another path of the route in therouting

increased additionally using below formula:

table, then a new alternative is taken immediately. Else, the

route maintenance will stop sending packets with the expiredt k Q f h ( ) (5)

route, and forward a ROUTE_ERROR message to the upper=? +

ij jnodes to find an alternative. At the same time, thelink

hopcount k

pheromone value is set to 0. Then the upper node searches for

an alternative link in its routing table to see if thereexists a

h is heuristic value in node routing table, and herethe second link usable. Otherwise the node informs its upper nodes

jbattery energy is only considered. There may exist

several too, hoping that they can relay the packet. Either the packet canroutes established in ARAAI, so route selecting should

take be transported to the destination node or thebacktracking

heuristic value information into consideration. Theseveral continues to the source node. If the packet does not reach the

established routes can be graded by heuristic value, andthe destination, the source has to initiate a new routediscovery

pheromone update should reference the rule.phase.

Ant colony algorithm has the problem of slow astringencyThe route maintenance also should take the situation of

and tending to stagnation behavior. When in stagnation, theloops into consideration. In ARAAI, the problem can be solved

searching ability of forward ants will be constraineda lot. in route discovery ants phase. In formula (2), nodeexist can

When routing process is occurred in a large-scale

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ad-hoc remember the nodes that have been visited before.Because

network, the above problem is more obvious. If the parameterevery ant has intelligence to record searched node. Route path

? is too big, the evaporation degree will be very low. So theloop can be prevented efficiently, since each ant hasthe

link pheromone value is big, then the probability is also big for

memory to visit the nodes that have not been searched before.those next nodes that have been searched before. Nearly all the

allowed k is the set of selecting next hop with permission. Itforward ants select the same route paths for next

hops. If is coordinated dynamically. Data packetsuse the route

? value is too small, the evaporation degree is high. Then thesearched by the ants, so the data packets using the route will be

link pheromone value is very small even tend to value 0. So theprevented from the loop.

global searching ability will be weakened in the situation. Herethe adaptive coordination of the parameters is used to improve

4.3 Evaluation

forward ants global searching ability. When selectingnext The ARAAI protocol in the paper hascombined the

branch, the adaptive method has been added into formula (1) toadvantage of proactive and on-demand routing schemes, and

solve the problem. It combines random node selection withprevented from their drawbacks. ARAAI protocol uses mobile

fixed node selection method. For node i, the next node j can beagents as ants to discovery route, which do not transmit much

selected with:

routing information. It is suitable for flexible mobility and

dynamic topology of ad-hoc network, and less data need to be? t {? a } ?�

transport between nodes. There are no routing tables that arearg max ( ) ( ) = ( )

? t t ifr t �j ? t allowed? k iu iu ( 6 )

interchanged between the nodes, so the expected overhead of?use : formula (1) else�?

ARAAI is very small. The route path selecting is depended on

pheromone, and then the local heuristic value in routing table is

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a factor to choose among several alternative route paths. TheV. CONCLUSION AND FUTURE WORK

adaptive coordination of pheromone is added to improve global

ARAAI is a suitable routing algorithm for solving thesearching ability and prevent ants stagnation.�

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routing problem in mobile ad-hoc networks. It uses the

adaptive ant colony algorithm to realize route discovery andIV. SIMULATION RESULTS

make improvement in global searching optimization. ARAAI isARAAI algorithm is implemented in ns-2 simulator. The

self-built and self-configured optimization algorithm that

simulation environment is that: 50 mobile nodes working withmatches the characteristics of ad-hoc networks. The protocol

IEEE 802.11, the area is . Each mode movehas been tested with ns-2 simulator, and the results turn out

m1000 m 25�with maximal 10m/s, the whole simulation lasts 300s. The

ARAAI has good performance comparing with AODV andnode mobility is expressed by the pause times 0,60,120,300

DSR. The future work is tending to enhance the algorithm toseconds.

get better efficiency and scalability. The route discovery delay

should be reduced using speeding techniques, and the local

heuristic information can contain more Qos factors such as link100

bandwidth, delay, and delay jitter. The other future work aspect) DSR

y 95is to explore the relationship of ant number with protocol

r %e ( AODVv o

performance.i i 90l t

e a AARAID R 85

800 60 120 300

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