Efficient Routing Protocol via Ant Colony Optimization ...

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

Reza Khoshkangini, Syroos Zaboli

International School of Information Management (ISIM)University of Mysore, India

Email: {reza.khosh, syroos}@isim.net.in

Mauro Conti

Department of MathematicsUniversity of Padua, Italy

Email: [email protected]

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 in

WSNs; considering the fact that a small change or loss in

data could lead to major problems in some applications.

For example, delay or poor quality of transmission in dam

building, military, nuclear or healthcare applications, could

manipulate a set of valuable information related to critical

decision making, leading to serious damage.

WSNs have a number of limitations such as increase

in the rate of energy consumption as the WSN begins its

communication state, which results in reduction in network

lifetime [1]. Low bandwidth and communication failure are

the 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 their

priority with respect to their quality of service (QoS) re-

quirement in a specific application. For instance, security and

data transmission speed are often high priorities in a military

application [2],[3]. On the other hand, the designed routing

algorithm must be capable of increasing the quality of data

transmission, as well as handling certain communication

problems [4]. Nowadays, the previously designed protocols

for WSNs have lost their usability due to presence of new

technologies which lead to higher information transmission

and bigger networks. Therefore, there is a demand for the

current platforms to sustain their proper functionality by

applying the right protocols and efficient algorithms.

Marco Dorigo introduced the first ACO algorithm [5] in

order to solve combinational optimization problems such as

the Traveling Salesman Problem (TSP). Moreover, its other

variations as solutions to finding the shortest path on the

graph [6], and then enhancing the network lifetime and load

balancing in WSNs. In this paper we use this an artificial

intelligence algorithm to enhance the network performance

in WSNs, taking into account the characteristics of ACO

such as Positive Feedback [7] and Greedy Heuristic [8],

to find the best path to the base station and data packet

transmission. The structure of the network is as shown in

Figure 1.

Figure 1: ACO multi-path routing

2014 IEEE International Conference on Internet of Things (iThings 2014), Green Computing and Communications (GreenCom

2014), and Cyber-Physical-Social Computing (CPSCom 2014)

978-1-4799-5967-9/14 $31.00 © 2014 IEEE

DOI 10.1109/iThings.2014.69

375

2014 IEEE International Conference on Internet of Things (iThings 2014), Green Computing and Communications (GreenCom

2014), and Cyber-Physical-Social Computing (CPSCom 2014)

978-1-4799-5967-9/14 $31.00 © 2014 IEEE

DOI 10.1109/iThings.2014.69

374

The black lines indicate the connection between the nodes

and their cluster head which the nodes (cluster members)

use, to send gathered data to their cluster head. Dotted lines

show the connection between cluster heads and intermediate

cluster heads, as well as cluster heads and sink where they

send their ants to carry the data toward the sink. τ is the

pheromone value of links between cluster heads and sink

which is explained in Equation 2.

By applying the graph search algorithm of Breadth First

Search, the selection accuracy of hops in transferring data

is enhanced with minimum transmission time and the least

energy consumption, moreover avoiding the probability of

starvation in the WSN. Starvation in wireless sensor net-

works mostly occurs due to the presence of expired nodes

and the failure of live sensor nodes in finding the right path

to transfer data to the base station [9].

The rest of the paper is organized as follows. Section

II surveys the pertinent literature. Section III describes the

proposed method; while Section IV discusses the simulation

results. Finally, Section V concludes the paper.

II. RELATED WORK

There has been quite a significant amount of work done

on different methods of routing using ACO, which turns

out to be one of the most suitable methods for multi-path

routing and a dynamic network for data transmission. For

example, Ad-Hoc networks, WSNs and telecommunications

networks [10].

Jing Yang et al. [11] introduced a Multi-path Routing

Protocol (MRP) which consists of three main steps as fol-

lows. First dynamically generating a cluster format, second

to search multiple paths to the base station using ACO

and finally dynamic selection of a single path for data

transmission. MRP makes use of three types of ants; the

search ant (SANT) which is used to capture the information

of paths and nodes on it’s way, the backward ant (BANT)

which has the responsibility of updating the pheromone

values and resending the collected information (such as path

length, energy consumption and residual energy) back to the

source node, and finally the abnormal ant (ABANT) which

is used to prevent stagnation of the protocol.

In spite of the fact that MRP improves data transmission

reliability and network lifetime in WSN, the algorithm speed

is quite low; moreover, it requires an amount of overhead

in dynamic areas to find the best path [12]. Every cluster

head sends a SANT in order to get information of its

neighborhood using Equation 1.

Pij =ταij(t)× ηβij

Σταij(t)× ηβij(1)

Yang Sun and Jingwen Tian [13] introduced another

multi-directional path algorithm by integrating the genetic

algorithm (GA) into the ant colony optimization algorithm,

where the initial solution is generated by the ACO as the

population for the genetic algorithm and next, the best

solution is searched by further iterations of genetic algo-

rithms using crossover and mutation. Although this approach

is effective in the long term in the case of multiple-path

searches, ACO sometimes faces the problem of generating

the best population for GA (starvation), moreover, GA needs

a large number of fitness function evaluations based on the

number of nodes. Hence, due to the GA’s drawback the

algorithm may take a long time to find the best path. In

addition, there is no guarantee that GA will find the best

solution.

Ruud Schoonderwoerd et al. [14] proposed an adap-

tive routing algorithm for telecommunication network using

ACO algorithm by introducing two specific kinds of agents

called ants. The first kind is the load management ant that

takes the responsibility of the lowest level of control; these

ants are launched from a particular node to search for the

most appropriate route from the source node to other nodes

using the ACO algorithm. The second kind are the parent

agents which are applied to the next level of control; these

parent agents move randomly in the networks based on the

heuristics and information gathered in the network, Hence

they have the ability to handle and fix the specific locations

which are experiencing congestion. This method is only

effective in case of optimal routing and organized public

transportation 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 the

whole information on the network. In this method each node

stores information about its neighbourhood nodes such as the

pheromone value, the MAC/ID of the source and destination

nodes which are used to transmit data packets from the

source to the destination node in their routing table. Despite

this 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 list

of visited node, whereas in other ACO approaches which

are suitable for static nodes (Selcuk’s method) [1], the data

carried by the ant can be limited, hence energy is preserved.

Here, the node’s memory is used in order to save information

related to other neighbouring nodes visited by an ant as well

as the pheromone values, which gives a node the choice to

accept or deny an incoming ant by looking up the tabu list.

Once an ant reaches the sink, an acknowledgement message

is sent back to the node through the same path. Although

the method is very useful to search for the best links in

a large number of nodes, it has prematurity and memory

problems, this is due to the fact that a large number of ants

in the network may lead to much higher traffic than the other

methods.

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III. PROPOSED METHOD

Link failure is a common occurrence in WSNs, which

results in problems such as repetitive back and forth trans-

mission of data between source and sink of a failed link due

to a dead node. Causing the failure of a message in reaching

its destination, thus leading to reduction in bandwidth range

and wastage of energy.

In this paper we propose a method using the ACO

algorithm along with BFS which is a tree-based search used

to enhance the accuracy of the best path selection. Here the

ACO consists on three type of ants, namely the frontward

ant, the Bfrontward ant and the backward ant [14], [15].

A. Frontward Ant

The frontward ant has the responsibility of finding the

best and shortest path by looking up the information on

neighboring nodes from the routing table. This ant considers

two distance factors; first is the distance between the current

node to its neighbourhood nodes and the second is the

distance of all the nodes (except the current node) from the

sink, which is placed at the center of area. It is important

for an ant to know each and every length between the nodes

and 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 25

y — x:15, y:53 0.6 30 z 31

ID, MID and LCN are the identification numbers, the

Mac addresses of each sensor node and the coordinates of

each cluster-head respectively. LCN is used to calculate the

distance between a cluster-head and the sink as well as the

distances between nodes. The pheromone value (PHV) of

each link increases every time as the frontward ant passes

through that link. DTS is the distance between the cluster

head and the sink. The tabu list (TBLST) contains the IDs of

nodes that a frontward ant arrives from. DTN is the distance

of a node to its neighbor. A frontward ant chooses and moves

towards the sink from one cluster head to another, based on

the 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 head

and τij is the pheromone value of a link between node i and

node j which can initially be assigned to 0 or 1. ηij , Ej are

the distance heuristic and energy level of node j respectively.

α, β, γ are the three controlling elements of the pheromone

value [1].

ηij =1

dij(3)

Where dij is the distance between nodes i and j, which

is shown in Equation 3. The shorter the distance, the higher

the probability, hence the frontward ant can select the closest

cluster-heads to the base station in its path.

ηij =1

djs(4)

Here djs is the distance between cluster-head j and the

sink which is illustrated in Figure 2. This helps the ant to

detect the next closest node to the base station. Since the

sensors are scattered across the area and the base station

is located at the centre, the movement of an ant starting at

any node in the area must be towards the centre taking the

closest cluster heads towards the sink into consideration.

ηij =1

dij+

1

djs(5)

Equation 5 which is derived from Equation 3 and 4, shows

the influence of the distance between nodes i and j as well

as the distance between node j to the sink on selection of

the next closest cluster head. Equation 6 is used to update

the pheromone value at the cluster heads links [5].

Δτij = (1− p)× τij +Δτij (6)

B. Backward Ant

Once the frontward ant carrying a data packet reaches

the destination, the base station extracts and processes the

received 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 stack

value of the latest received data packet. A backward ant

can take the same path back, as well as the algorithm

remarkably adapting to the network changes (this is illus-

trated in simulation section in Figure 7). In case of a link

failure during transmission, it has the ability to search for an

alternative closest intermediate cluster-head using Equation

3 according to the dead node. The proposed method uses

the node memory instead of ant memory for the advantage

of reduction in the ant’s packet load and decrease the packet

size which is directly related to the amount of energy

consumption [1]. Hence a tabu list made up of a stack is

used to keep a track of the node IDs from which an ant

comes from.

C. Energy Consumption Model

The LEACH model [16] is used for energy consumption

and it is implemented using Equation 7. The amount of

energy consumption depends on the distance between the

sender and receiver, as well as the size of the packets. In the

propose method, we use two types of energy consumption

models; first is free space (d2 reducing power) and second

is multi-path fading (d4 reducing power).

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Es =

{lEelect + lefpd

2 d ≤ ZomlEelect + lempd

4 d > Zom(7)

Where efp and emp are the energy consumption of

amplifying radio and Zom is the threshold value for the

distance.

D. Implementation of BFS Technique

In nature, it is hard for an ant positioned at the wrong

location to find and collect food; this happens when some

of the nodes expire sometime after the transmission begins

as we apply ACO to WSN in the nodes. This could be

due to failure of a sensor node, which results in starvation,

reduction in performance and the transfer rate of packets

to the sink and vise versa (this is described in simulation

section IV). As a solution we implement BFS in the ACO

algorithm which overcomes the problem of getting trapped

during exploration unlike the previous multi-path routing

protocols that did not focus on this particular problem [1].

Breadth-First Search (BFS) [18] is a graph search algorithm

used to explore the neighboring nodes, starting from the end

branches towards the main branches level by level. It can

continue this process throughout the connected neighbors

until it finds the solution or a specific node based on its

requirement.

E. Bfrontward Ant

BFS applied to the propose method for an ant (Bfront-

ward) to find its shortest path as shown in Figure 2, such

that, an ant situated at node-i looks for the shortest path to

the sink considering the first set of neighboring nodes. This

can be described as a tree (the whole network) where the

aim of an ant is to find the shortest distance from a leaf

on a branch (source node) to the trunk (sink). For example,

node-j is chosen using step 1 (frontward) and its distance

to the sink is achieved using (frontward) step 2. Next, the

node closest to the sink among all the other neighboring

nodes (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 in

order to determine the selection probability of the next

cluster-head.

ηij =1

dmj(8)

ηij =1

dij+

1

djs+

1

dmj(9)

Where dmj is the minimum distance after node-j to the

sink which is determined by the BFS algorithm, and it is

included 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 the

next node as a path, for example, node-j or node-k in order to

send data for the next level of transportation. Therefore, the

process of node selection to transport the data towards the

sink is carried out with minimum cost of energy, resulting

in a longer network lifetime.

In spite of the fact that implementation of BFS in ACO

leads to additional node memory and time consumption

which are the weaknesses of the method, it increases the

search accuracy in finding the shortest path. Hence achieving

the balance between depth levels, number of moving ants

within the network, energy consumption and time is firmly

essential to enhance the network performance. The time and

memory complexity which is commensurate to the number

of nodes at the depth level expressed Equation 10 [19].

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

Where E is the cardinality of set of edges (number of

edge between each node) and V is the number of nodes. In

order to reduce the node memory consumption and search

time, we limit the ant to only one depth level in the process

of determination of distance from that node to sink as

mentioned in Equation 8 and Figure 2. Moreover, energy

consumption, network lifetime and timing stamp are directly

related to the number of ants moving within the network.

That is, the number of cluster heads that are to send the

ants carrying data toward the sink, have to be optimized.

Before the ants are sent to the sink by the cluster heads

or intermediate cluster heads, the cluster heads should have

been selected using a protocol such as LEACH [16] or

Fuzzy Logic [20], [21], etc. where the optimized number

of selected cluster heads send the ants as they receive data

from their cluster members, per unit of time.

In this paper, At first we use 30, 20, 15 and 10 percent

of all nodes as cluster heads to send the ants in order to

find the balance between energy consumption, packets sent,

time stamping and the number of ants in move within the

network. 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 number

of 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 describes

the relationship between, energy consumption, packets sent,

time stamping and the number of ants in move within the

network. In this part of simulation we use 30, 20, 15 and

10 percent of all nodes as cluster heads to send the ants

in terms of finding a balance between, energy consumption,

packets sent, time stamping and the number of ants in move

within the network with minimum cost. Table II shows the

parameters used for this simulation.

Table II: Parameters

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

Location of Sink Center

Location of Cluster Head Static

Packet Size 4000bit

Transmit Amplifier Type Efs,Emp

Coordinate Area 150×150

ETX, ERX 50×0.000000001jRound 800

Pheromone Value 0.5

Figure 3 describes the total round trip time to send and

receive data packets from nodes to sink and vice versa. The

X-axis shows the number of round and the Y-axis shows

the total round trip time (unit of time) taken by the different

number of ants, where, 30 and 10 percent ant are the worst

and 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% Ant20% Ant15% Ant10% Ant

Figure 3: Time stamp

Figure 4, indicates the impact of number of ants carrying

the 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

Num

ber

of p

acke

ts s

end

to S

ink

30% Ant20% Ant15% Ant10% Ant

Figure 4: Packets sent

percent of the total number of nodes, dramatically has the

highest number of data packets sent. Subsequently, it is

absolutely clear in Figure 5 that the 30 percent (number

of ants) runs out of energy in 150th iteration as it has the

highest energy consumption with respect to the rest, with the

result of network failure. Having said that, more number

of ants initially send more packets toward the sink, they

consume more energy and take much time. Furthermore,

the network goes down faster than the others. On the other

hand, the network with 15 and 20 percent ants has a better

performance in terms of sending packets, time stamp and

energy 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

Ene

rgy

Con

sum

ptio

n

30% Ant20% Ant15% Ant10% Ant

Figure 5: Energy consumption

The next part of the simulation is done with respect to two

important factors in WSNs; First, the energy preservation

and second the number of packets sent per round during the

network lifetime. Table III shows the parameters used for

simulation.

The nodes are randomly distributed across the area of size

150× 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 20

Location of Sink Center

Location of Cluster Head Randomly Distibutes

Packet Size 4000bit

Transmit Amplifier Type Efs, Emp

Coordinate Area 150×150

ETX, ERX 50×0.000000001jRound 800

Pheromone Value 0.5

three methods with a node initial energy value of 1.5j and

default pheromone value of 0.5. The packet size is fixed to

4000 bit for every transaction.

Figure 6 shows that the proposed method results in much

more energy preservation with respect to the other two

methods by the end of the simulation; Where although all

three methods start with the same amount of energy level

30, by the 800th iteration the ANT-BFS method preserves

significantly 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

Num

ber

of P

acke

ts S

ent t

o S

ink

ANTANT−BFSANT−Selcuk

Figure 6: Packets sent

The proposed method consumes much less energy; as a

result the number of packets sent to the sink are larger,

which leads to a longer network lifetime. Figure 7 having the

number of sent packets on y-axis and number of rounds on

x-axis describes the results of simulation comparison where

the ANT-BFS method is leading in terms of higher number

of successfully transmitted data packets with respect to other

two methods.

V. CONCLUSIONS

One of the major challenges in WSNs is to determine

the best path to transmit data from nodes to the base station

which directly affects the amount of energy consumption and

network lifetime. In this paper, the ACO algorithm with three

types 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

Ene

rgy

Rem

inde

ANTANT−BFSANT−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 and

shortest path to the destination, in order to carry and transfer

the data packets to the base station and vice versa. This

method resulted in the least amount of energy consumption

and loss of data packets. As the future work, we will apply

ant colony algorithm to enhance the security of the location

of the base station from external attackers in WSN.

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