Efficient Routing Protocol via Ant Colony Optimization (ACO)and Breadth First Search (BFS)

Reza Khoshkangini, Syroos ZaboliInternational School of Information Management (ISIM)

University of Mysore, IndiaEmail: {reza.khosh, syroos}@isim.net.in

Mauro ContiDepartment of Mathematics

University of Padua, ItalyEmail: conti@math.unipd.it

Abstract—Wireless Sensor Networks (WSNs) consist of manysensor nodes, which are usually distributed across areas diffi-cult to be accessed in order to collect and send the data to themain sink location. Despite the fact that a number of protocolshave been proposed for routing and energy management, WSNsstill face problems in selecting the best path with efficientenergy consumption and successful delivery of the packets.In particular, these problems occur when WSNs are subjectedto critical situations such as node or link failure, and it iseven more critical in sensitive applications such as nuclearand healthcare.

In this paper, we propose the Ant Colony Optimization(ACO) combined with Breadth First Search (BFS) to searchand find the best and shortest path in order to improve datatransmission with the least amount of energy consumption,as well as reduce the probability of data loss. Using ourproposal, a balance between number of packets, time andenergy consumption can be determined which leads to increasethe network performance. Therefore, the main goal of the paperis to decrease energy consumption which leads to increaseof the network’s lifetime and enhancement of the number ofsuccessfully transmitted data with respect to other multipleants-based routing protocols. Moreover, the number of antsare optimized within the network to avoid network congestion.

Keywords-Sensor Network; Ant Colony Optimization (ACO);BFS; Routing; Cluster-Head;

I. INTRODUCTION

It is important to have efficient data transmission inWSNs; considering the fact that a small change or loss indata could lead to major problems in some applications.For example, delay or poor quality of transmission in dambuilding, military, nuclear or healthcare applications, couldmanipulate a set of valuable information related to criticaldecision making, leading to serious damage.

WSNs have a number of limitations such as increasein the rate of energy consumption as the WSN begins itscommunication state, which results in reduction in networklifetime [1]. Low bandwidth and communication failure arethe other limitations which occur in WSNs affecting the per-formance directly. Hence from a network design perspective;

Mauro Conti is supported by a Marie Curie Fellowship for the projectPRISM-CODE: Privacy and Security for Mobile Cooperative Devicesfunded by the European Commission (grant PCIG11-GA-2012-321980) andby the PRIN project TENACE: Protecting National Critical InfrastructuresFrom Cyber Threats (grant 20103P34XC) funded by the Italian MIUR.

one must consider various factors such as memory, security,accuracy, speed and effective distance ranges, as well as theirpriority with respect to their quality of service (QoS) re-quirement in a specific application. For instance, security anddata transmission speed are often high priorities in a militaryapplication [2],[3]. On the other hand, the designed routingalgorithm must be capable of increasing the quality of datatransmission, as well as handling certain communicationproblems [4]. Nowadays, the previously designed protocolsfor WSNs have lost their usability due to presence of newtechnologies which lead to higher information transmissionand bigger networks. Therefore, there is a demand for thecurrent platforms to sustain their proper functionality byapplying the right protocols and efficient algorithms.

Marco Dorigo introduced the first ACO algorithm [5] inorder to solve combinational optimization problems such asthe Traveling Salesman Problem (TSP). Moreover, its othervariations as solutions to finding the shortest path on thegraph [6], and then enhancing the network lifetime and loadbalancing in WSNs. In this paper we use this an artificialintelligence algorithm to enhance the network performancein WSNs, taking into account the characteristics of ACOsuch as Positive Feedback [7] and Greedy Heuristic [8],to find the best path to the base station and data packettransmission. The structure of the network is as shown inFigure 1.

Figure 1: ACO multi-path routing

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The black lines indicate the connection between the nodesand their cluster head which the nodes (cluster members)use, to send gathered data to their cluster head. Dotted linesshow the connection between cluster heads and intermediatecluster heads, as well as cluster heads and sink where theysend their ants to carry the data toward the sink. τ is thepheromone value of links between cluster heads and sinkwhich is explained in Equation 2.

By applying the graph search algorithm of Breadth FirstSearch, the selection accuracy of hops in transferring datais enhanced with minimum transmission time and the leastenergy consumption, moreover avoiding the probability ofstarvation in the WSN. Starvation in wireless sensor net-works mostly occurs due to the presence of expired nodesand the failure of live sensor nodes in finding the right pathto transfer data to the base station [9].

The rest of the paper is organized as follows. SectionII surveys the pertinent literature. Section III describes theproposed method; while Section IV discusses the simulationresults. Finally, Section V concludes the paper.

II. RELATED WORK

There has been quite a significant amount of work doneon different methods of routing using ACO, which turnsout to be one of the most suitable methods for multi-pathrouting and a dynamic network for data transmission. Forexample, Ad-Hoc networks, WSNs and telecommunicationsnetworks [10].

Jing Yang et al. [11] introduced a Multi-path RoutingProtocol (MRP) which consists of three main steps as fol-lows. First dynamically generating a cluster format, secondto search multiple paths to the base station using ACOand finally dynamic selection of a single path for datatransmission. MRP makes use of three types of ants; thesearch ant (SANT) which is used to capture the informationof paths and nodes on it’s way, the backward ant (BANT)which has the responsibility of updating the pheromonevalues and resending the collected information (such as pathlength, energy consumption and residual energy) back to thesource node, and finally the abnormal ant (ABANT) whichis used to prevent stagnation of the protocol.

In spite of the fact that MRP improves data transmissionreliability and network lifetime in WSN, the algorithm speedis quite low; moreover, it requires an amount of overheadin dynamic areas to find the best path [12]. Every clusterhead sends a SANT in order to get information of itsneighborhood using Equation 1.

Pij =ταij(t)× ηβij

Σταij(t)× ηβij(1)

Yang Sun and Jingwen Tian [13] introduced anothermulti-directional path algorithm by integrating the geneticalgorithm (GA) into the ant colony optimization algorithm,

where the initial solution is generated by the ACO as thepopulation for the genetic algorithm and next, the bestsolution is searched by further iterations of genetic algo-rithms using crossover and mutation. Although this approachis effective in the long term in the case of multiple-pathsearches, ACO sometimes faces the problem of generatingthe best population for GA (starvation), moreover, GA needsa large number of fitness function evaluations based on thenumber of nodes. Hence, due to the GA’s drawback thealgorithm may take a long time to find the best path. Inaddition, there is no guarantee that GA will find the bestsolution.

Ruud Schoonderwoerd et al. [14] proposed an adap-tive routing algorithm for telecommunication network usingACO algorithm by introducing two specific kinds of agentscalled ants. The first kind is the load management ant thattakes the responsibility of the lowest level of control; theseants are launched from a particular node to search for themost appropriate route from the source node to other nodesusing the ACO algorithm. The second kind are the parentagents which are applied to the next level of control; theseparent agents move randomly in the networks based on theheuristics and information gathered in the network, Hencethey have the ability to handle and fix the specific locationswhich are experiencing congestion. This method is onlyeffective in case of optimal routing and organized publictransportation schemes in telecommunication networks.

Nuria Gomez et al. proposed a local routing method [17]using ant colony algorithm, which keeps track of the in-formation sent to the destination node instead of storing thewhole information on the network. In this method each nodestores information about its neighbourhood nodes such as thepheromone value, the MAC/ID of the source and destinationnodes which are used to transmit data packets from thesource to the destination node in their routing table. Despitethis method being suitable for memory limitation applica-tions, it has the drawback of high energy consumption.

Many ACO algorithms use ant memory to save a listof visited node, whereas in other ACO approaches whichare suitable for static nodes (Selcuk’s method) [1], the datacarried by the ant can be limited, hence energy is preserved.Here, the node’s memory is used in order to save informationrelated to other neighbouring nodes visited by an ant as wellas the pheromone values, which gives a node the choice toaccept or deny an incoming ant by looking up the tabu list.Once an ant reaches the sink, an acknowledgement messageis sent back to the node through the same path. Althoughthe method is very useful to search for the best links ina large number of nodes, it has prematurity and memoryproblems, this is due to the fact that a large number of antsin the network may lead to much higher traffic than the othermethods.

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III. PROPOSED METHOD

Link failure is a common occurrence in WSNs, whichresults in problems such as repetitive back and forth trans-mission of data between source and sink of a failed link dueto a dead node. Causing the failure of a message in reachingits destination, thus leading to reduction in bandwidth rangeand wastage of energy.

In this paper we propose a method using the ACOalgorithm along with BFS which is a tree-based search usedto enhance the accuracy of the best path selection. Here theACO consists on three type of ants, namely the frontwardant, the Bfrontward ant and the backward ant [14], [15].

A. Frontward Ant

The frontward ant has the responsibility of finding thebest and shortest path by looking up the information onneighboring nodes from the routing table. This ant considerstwo distance factors; first is the distance between the currentnode to its neighbourhood nodes and the second is thedistance of all the nodes (except the current node) from thesink, which is placed at the center of area. It is importantfor an ant to know each and every length between the nodesand the sink which is shown in Table I [11].

Table I: Routing Table

ID MID LCN PHV DTS TBLST DTNx — x:20, y:45 0.5 25 y 25y — x:15, y:53 0.6 30 z 31

ID, MID and LCN are the identification numbers, theMac addresses of each sensor node and the coordinates ofeach cluster-head respectively. LCN is used to calculate thedistance between a cluster-head and the sink as well as thedistances between nodes. The pheromone value (PHV) ofeach link increases every time as the frontward ant passesthrough that link. DTS is the distance between the clusterhead and the sink. The tabu list (TBLST) contains the IDs ofnodes that a frontward ant arrives from. DTN is the distanceof a node to its neighbor. A frontward ant chooses and movestowards the sink from one cluster head to another, based onthe Equation 2.

Pij =(τij)α × (ηij)β × (Ej)γ

Σ(τij)α × (ηij)β × (Ej)γ(2)

Where Pij is the selection probability of a cluster headand τij is the pheromone value of a link between node i andnode j which can initially be assigned to 0 or 1. ηij , Ej arethe distance heuristic and energy level of node j respectively.α,β, γ are the three controlling elements of the pheromonevalue [1].

ηij =1

dij(3)

Where dij is the distance between nodes i and j, whichis shown in Equation 3. The shorter the distance, the higherthe probability, hence the frontward ant can select the closestcluster-heads to the base station in its path.

ηij =1

djs(4)

Here djs is the distance between cluster-head j and thesink which is illustrated in Figure 2. This helps the ant todetect the next closest node to the base station. Since thesensors are scattered across the area and the base stationis located at the centre, the movement of an ant starting atany node in the area must be towards the centre taking theclosest cluster heads towards the sink into consideration.

ηij =1

dij+

1

djs(5)

Equation 5 which is derived from Equation 3 and 4, showsthe influence of the distance between nodes i and j as wellas the distance between node j to the sink on selection ofthe next closest cluster head. Equation 6 is used to updatethe pheromone value at the cluster heads links [5].

∆τij = (1− p)× τij +∆τij (6)

B. Backward Ant

Once the frontward ant carrying a data packet reachesthe destination, the base station extracts and processes thereceived data packet. The sink adds the following headers;the source node data header (Mac address and coordinate),the destination data header (header of sender), and the stackvalue of the latest received data packet. A backward antcan take the same path back, as well as the algorithmremarkably adapting to the network changes (this is illus-trated in simulation section in Figure 7). In case of a linkfailure during transmission, it has the ability to search for analternative closest intermediate cluster-head using Equation3 according to the dead node. The proposed method usesthe node memory instead of ant memory for the advantageof reduction in the ant’s packet load and decrease the packetsize which is directly related to the amount of energyconsumption [1]. Hence a tabu list made up of a stack isused to keep a track of the node IDs from which an antcomes from.

C. Energy Consumption Model

The LEACH model [16] is used for energy consumptionand it is implemented using Equation 7. The amount ofenergy consumption depends on the distance between thesender and receiver, as well as the size of the packets. In thepropose method, we use two types of energy consumptionmodels; first is free space (d2 reducing power) and secondis multi-path fading (d4 reducing power).

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Es =

!lEelect + lefpd2 d ≤ ZomlEelect + lempd4 d > Zom

(7)

Where efp and emp are the energy consumption ofamplifying radio and Zom is the threshold value for thedistance.

D. Implementation of BFS Technique

In nature, it is hard for an ant positioned at the wronglocation to find and collect food; this happens when someof the nodes expire sometime after the transmission beginsas we apply ACO to WSN in the nodes. This could bedue to failure of a sensor node, which results in starvation,reduction in performance and the transfer rate of packetsto the sink and vise versa (this is described in simulationsection IV). As a solution we implement BFS in the ACOalgorithm which overcomes the problem of getting trappedduring exploration unlike the previous multi-path routingprotocols that did not focus on this particular problem [1].Breadth-First Search (BFS) [18] is a graph search algorithmused to explore the neighboring nodes, starting from the endbranches towards the main branches level by level. It cancontinue this process throughout the connected neighborsuntil it finds the solution or a specific node based on itsrequirement.

E. Bfrontward Ant

BFS applied to the propose method for an ant (Bfront-ward) to find its shortest path as shown in Figure 2, suchthat, an ant situated at node-i looks for the shortest path tothe sink considering the first set of neighboring nodes. Thiscan be described as a tree (the whole network) where theaim of an ant is to find the shortest distance from a leafon a branch (source node) to the trunk (sink). For example,node-j is chosen using step 1 (frontward) and its distanceto the sink is achieved using (frontward) step 2. Next, thenode closest to the sink among all the other neighboringnodes (except the visited nodes in order to avoid loop trap)is chosen using (Bfrontward) step 3, Equation 8.

Figure 2: Process of finding the minimum distance

Equation 9 shows the distance heuristic calculation inorder to determine the selection probability of the nextcluster-head.

ηij =1

dmj(8)

ηij =1

dij+

1

djs+

1

dmj(9)

Where dmj is the minimum distance after node-j to thesink which is determined by the BFS algorithm, and it isincluded along with the other two distance values.

Using the Bfrontward ant, the current node-i, gets en-hanced with the ability to predict the cost of selecting thenext node as a path, for example, node-j or node-k in order tosend data for the next level of transportation. Therefore, theprocess of node selection to transport the data towards thesink is carried out with minimum cost of energy, resultingin a longer network lifetime.

In spite of the fact that implementation of BFS in ACOleads to additional node memory and time consumptionwhich are the weaknesses of the method, it increases thesearch accuracy in finding the shortest path. Hence achievingthe balance between depth levels, number of moving antswithin the network, energy consumption and time is firmlyessential to enhance the network performance. The time andmemory complexity which is commensurate to the numberof nodes at the depth level expressed Equation 10 [19].

O((V ) + (E)) (10)

Where E is the cardinality of set of edges (number ofedge between each node) and V is the number of nodes. Inorder to reduce the node memory consumption and searchtime, we limit the ant to only one depth level in the processof determination of distance from that node to sink asmentioned in Equation 8 and Figure 2. Moreover, energyconsumption, network lifetime and timing stamp are directlyrelated to the number of ants moving within the network.That is, the number of cluster heads that are to send theants carrying data toward the sink, have to be optimized.Before the ants are sent to the sink by the cluster headsor intermediate cluster heads, the cluster heads should havebeen selected using a protocol such as LEACH [16] orFuzzy Logic [20], [21], etc. where the optimized numberof selected cluster heads send the ants as they receive datafrom their cluster members, per unit of time.

In this paper, At first we use 30, 20, 15 and 10 percentof all nodes as cluster heads to send the ants in order tofind the balance between energy consumption, packets sent,time stamping and the number of ants in move within thenetwork. Then the method compared with two other ant-based multi-path protocols.

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IV. SIMULATION

The simulation is done using MATLAB where the pro-posed method is first evaluated with respect to the numberof ants, and one level depth via BFS within the network.Next, the proposed method compared with two other ant-based methods of literature [1]. The simulation describesthe relationship between, energy consumption, packets sent,time stamping and the number of ants in move within thenetwork. In this part of simulation we use 30, 20, 15 and10 percent of all nodes as cluster heads to send the antsin terms of finding a balance between, energy consumption,packets sent, time stamping and the number of ants in movewithin the network with minimum cost. Table II shows theparameters used for this simulation.

Table II: Parameters

Parameter ValueTotal Energy for all Ants 30jNumber of Cluster Head 10-15-20-30

Location of Sink CenterLocation of Cluster Head Static

Packet Size 4000bitTransmit Amplifier Type Efs,Emp

Coordinate Area 150×150ETX, ERX 50×0.000000001j

Round 800Pheromone Value 0.5

Figure 3 describes the total round trip time to send andreceive data packets from nodes to sink and vice versa. TheX-axis shows the number of round and the Y-axis showsthe total round trip time (unit of time) taken by the differentnumber of ants, where, 30 and 10 percent ant are the worstand the best time respectively to send and receive the packets

0 100 200 300 400 500 600 700 8000

1

2

3

4

5

6

7

8

9

10

Number of Round

Tim

e

30% Ant

20% Ant

15% Ant

10% Ant

Figure 3: Time stamp

Figure 4, indicates the impact of number of ants carryingthe data packets on the performance level of the system,where having the number of ants as many as 15 to 20

0 100 200 300 400 500 600 700 8000

0.5

1

1.5

2

2.5

3x 10

6

Number of Round

Nu

mb

er

of

pa

cke

ts s

en

d t

o S

ink

30% Ant

20% Ant

15% Ant

10% Ant

Figure 4: Packets sent

percent of the total number of nodes, dramatically has thehighest number of data packets sent. Subsequently, it isabsolutely clear in Figure 5 that the 30 percent (numberof ants) runs out of energy in 150th iteration as it has thehighest energy consumption with respect to the rest, with theresult of network failure. Having said that, more numberof ants initially send more packets toward the sink, theyconsume more energy and take much time. Furthermore,the network goes down faster than the others. On the otherhand, the network with 15 and 20 percent ants has a betterperformance in terms of sending packets, time stamp andenergy consumption during the transaction for long time.

0 100 200 300 400 500 600 700 8000

5

10

15

20

25

30

35

Number of Round

En

erg

y C

on

su

mp

tio

n

30% Ant

20% Ant

15% Ant

10% Ant

Figure 5: Energy consumption

The next part of the simulation is done with respect to twoimportant factors in WSNs; First, the energy preservationand second the number of packets sent per round during thenetwork lifetime. Table III shows the parameters used forsimulation.

The nodes are randomly distributed across the area of size150× 150 , and their positions have been maintained for all

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Table III: Parameters

Parameter ValueInitial Energy 1.5j

Number of Cluster Head 20Location of Sink Center

Location of Cluster Head Randomly DistibutesPacket Size 4000bit

Transmit Amplifier Type Efs, Emp

Coordinate Area 150×150ETX, ERX 50×0.000000001j

Round 800Pheromone Value 0.5

three methods with a node initial energy value of 1.5j anddefault pheromone value of 0.5. The packet size is fixed to4000 bit for every transaction.Figure 6 shows that the proposed method results in muchmore energy preservation with respect to the other twomethods by the end of the simulation; Where although allthree methods start with the same amount of energy level30, by the 800th iteration the ANT-BFS method preservessignificantly higher amount of energy.

0 100 200 300 400 500 600 700 8000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

6

Number of Round

Nu

mb

er

of

Pa

cke

ts S

en

t to

Sin

k

ANT

ANT−BFS

ANT−Selcuk

Figure 6: Packets sent

The proposed method consumes much less energy; as aresult the number of packets sent to the sink are larger,which leads to a longer network lifetime. Figure 7 having thenumber of sent packets on y-axis and number of rounds onx-axis describes the results of simulation comparison wherethe ANT-BFS method is leading in terms of higher numberof successfully transmitted data packets with respect to othertwo methods.

V. CONCLUSIONS

One of the major challenges in WSNs is to determinethe best path to transmit data from nodes to the base stationwhich directly affects the amount of energy consumption andnetwork lifetime. In this paper, the ACO algorithm with threetypes of ants, namely Frontward, Bfrontward and Backward

0 100 200 300 400 500 600 700 8000

5

10

15

20

25

30

Number of Round

En

erg

y R

em

ind

e

ANT

ANT−BFS

ANT−Selcuk

Figure 7: Energy saving

ant, is used along with a tree Breadth First Search (BFS)making the ants capable to search and find the best andshortest path to the destination, in order to carry and transferthe data packets to the base station and vice versa. Thismethod resulted in the least amount of energy consumptionand loss of data packets. As the future work, we will applyant colony algorithm to enhance the security of the locationof the base station from external attackers in WSN.

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