Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utilization in Wireless Sensor Network

International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.4, July 2023
DOI:10.5121/ijcnc.2023.15405 73
INVESTIGATION OF ANT COLONY OPTIMIZATION
ALGORITHM FOR EFFICIENT ENERGY
UTILIZATION IN WIRELESS SENSOR NETWORK
Shanmugavalli R1
and Subashini P2
1
Department of Computer Science, Avinashilingam Institute for Home Science and
Higher Education for Women, Coimbatore, Tamil Nadu, India
2
Department of Computer Science, Centre for Machine Learning and Intelligence,
Avinashilingam Institute for Home Science and Higher Education for Women,
Coimbatore, Tamil Nadu, India
ABSTRACT
Maintaining the energy conservation is considered as an important approach to increase the lifetime of
WSN. In fact, an energy reduction mechanism is considered as the main concept to enhance the lifespan of
the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant
Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This
network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes.
Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious
performance, which is comparatively better than ACO algorithm, because it improves the routing
efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO
algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%),
packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence
rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
KEYWORDS
Nature Inspired Computational (NIC) Algorithms, Energy Efficiency, Route Discovery, Convergence Rate
and Wireless Sensor Network (WSN)
1. INTRODUCTION
This WSN is one of the emerging technologies which helped to gather highly meaningful
information from various sources. In WSN, greater amount of sensor nodes are incorporated
wirelessly and deployed in the problem area. Further, every sensor is an intermediator which
supports successful data transmission by creating network connection between source and
destination. The sensor node is an electronic device that contains four types of elements, namely,
sensing unit, processing unit, communication unit and power unit. The sensing unit collects the
required information and converts the analogue signal into digital signal. Moreover, the
processing unit controls all other functionalities of the sensor node, using the microcontroller as
well as microprocessor, and it maintains a tiny memory. The communication unit is responsible
for transmitting and receiving the data packets in wireless sensor network. Energy unit distributes
the power to all other components of sensor node which is considered an important part in WSN.
Figure 1 illustrates the components of wireless sensor node. Energy utilization is an essential
factor in WSN, because, every wireless sensor node contains tiny batteries for energy utilization.

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Figure 1. Components of Wireless Sensor
Many researchers solve these problems using different optimization techniques such as protocol
optimization, route selection method, algorithms-based optimization, route discovery mechanism,
node deployment methodology, etc. The route discovery mechanism mostly helps to minimize
the nodes energy usage as well as improve the lifespan of WSN by selecting an efficient route
[1][2]. Routing is a process to select the appropriate route to transmit the data from sender to
receiver in WSN. Routing is divided into two types, namely, single-hop routing and Multi-hop
routing. In single-hop communication, source and destination are directly communicated without
using any intermediate nodes in single-hop communication. In multi-hop routing, the source node
communicates that destination with the help of intermediate nodes. The sensor node is unable to
communicate other nodes larger than radio range of a single node. The multi-hop routing is
mostly used in WSN because it extends the network coverage and improves the connectivity.
Figure 2. Multi-hop Routing in WSNs
Figure 2. illustrates the multi-hop routing in WSN scenario. This network contains a source and
destination node to transmit the data packets. In this scenario, line indicates the path between
source to destination with the intermediate nodes. In this path, all the sensor nodes, except source
and destination, are considered as intermediate nodes which help successful data transmission. In
this case, the network takes four hops to reach the destination in wireless sensor network.
This case investigates the comparative analysis of ACO and m-ACO algorithms to minimize the
energy utilization and improve the WSN performance by route discovery mechanism. ACO
algorithm is widely applied for scientific and complicated optimization problems like Traveling
Salesman Problem (TSP), Quadrative assignment problem, Vehicle routing, Binary knapsack
problem, Network routing and QoS (Quality of Service) [3]. Moreover, ACO algorithm is a
major inspiration for developing several energy-efficient routing algorithms [4]. Further, ACO
algorithm can identify the most optimal path to destination as well as reduce energy usage and
increase network lifetime [5]. ACO algorithm is considered as a good solution to enhance the
lifetime of WSN [6]. As per this analysis, m-ACO extends the performance more than traditional
ACO such as stability and energy load balancing, and eventually, enhanced the network lifespan
and minimized the energy utilization [7]. This conventional mechanism uses threshold value to

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each node in WSN for energy balancing and controls death nodes. Furthermore, the pheromone
evaporation rate is increased to avoid the frequently used path when the node’s energy is less
than threshold. In this case, the results were differentiated by the metric of residual energy with
different threshold values. This paper focuses on the comparison analysis of ACO and m-ACO
algorithm in 100 iteration as well as differentiates with three increased number of nodes, namely,
50, 75 and 100. Therefore, the performance of both algorithms is investigated with various
performance metrics, namely, energy consumption, packet delivery ratio, throughput,
transmission delay, packet loss and the convergence rate. It inspires to conduct this work. The
potential literature works of ACO and m-ACO in route discovery are cited. The major
contribution of this work is analyses of various number of nodes, scenarios, and performance
metrics.
The formation of the paper is organized as follows: Section II briefly discusses the literature
study of this paper. Section III briefly discusses the comparative analysis of ACO algorithm,
modified Ant Colony Optimization (m-ACO) and route selection process. Section IV presents the
result and analysis of this comparative study. Finally, Section V concludes this paper and
describes the future work.
2. LITERATURE REVIEW
There are more routing approaches based on NIC algorithms as well as optimization techniques
to solve these energy-based routing challenges which illustrates in Table1. The authors have
carried out a lot of optimization using the protocols and meta-heuristic algorithms which applied
in many ways to WSN.
Table 1. Simulation Parameters
S.No Algorithms Performance
Metrics
Comparative
Algorithms
Results
1. Introduced Fuzzy based
Ant Colony Optimization
to find the shortest path.
Average
running time,
Fitness value
and
Convergence
rate of
algorithms
Genetic Algorithm
(GA), PSO (Particle
Swarm
Optimization) and
ABC (Artificial Bee
Colony
Optimization)
The proposed ACO
algorithm has taken
minimum time to
converge the
optimum solution
comparatively than
other algorithms [8].
2. Investigated the
performance analysis of
Ant Colony Optimization
technique incorporated
with gradient based routing
protocol and energy aware
routing protocol to
optimize the effective
energy utilization path for
WSN.
Remaining
energy of the
nodes
Energy aware
routing with ACO
and Gradient based
routing with ACO
Algorithm
Energy aware routing
with ACO algorithm
obtained a feasible
path for routing and
increased the lifetime
of the network
comparatively better
than the other
gradient protocol [9].
3. Proposed an (ACO
Optimized Self-Organized
Tree-Based) AOSTEB
energy balance algorithm
to discover the optimum
path for intra-cluster
communication in WSN.
This algorithm performs
Number of
rounds, Dead
node, Energy
consumption,
Remaining
energy and
Standard
deviation
LEACH,
PEGASIS, TBC,
GSTEB, HEEMP,
MOFCA and IACO
The proposed
algorithm reduced the
higher energy
consumption and
enhanced the lifetime
and stability [2].

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three functionalities,
namely, data transmission,
multi-path creation and
cluster formation.
4. Introduced an effective
hybrid routing algorithm,
namely, ACO with Hop
Count Minimization
(ACOHCM) based on
ACO and minimum hop
count scheme for WSN.
Energy
consumption
and lifespan of
WSN
ACOHCM,
Lifetime Aware
WSN, ACO-GA,
Classic ACO, and
Energy Aware
Ladder Diffusion
(EALD)
The proposed
ACOHCM algorithm
minimized the energy
consumption and
enhanced the network
lifetime
comparatively than
others [3].
5. Introduced a novel routing
protocol based on
clustering technique using
Firefly Algorithm and
ACO algorithms. In this
routing protocol, FA is
used to find the optimal
cluster head, and ACO
algorithm is used to
discover the optimal route
to selected cluster head.
Residual
energy,
Network
stability,
Lifetime and
Throughput
LEACH-C, FIGWO
and PSO algorithms
The proposed
algorithm gives the
highest lifetime than
LEACH-C, FIGWO
and PSO algorithms
[10].
6. Initiated the Dijkstra
algorithm to improve the
ACO for distributing nodes
in a directed graph and it is
used to improve the initial
efficiency and ensure
stability of WSN.
Energy and
Lifecycle of
network
ACO algorithm Proposed algorithm
balancing the energy
consumption and
extending the lifetime
of the network and
improving ACO
algorithm give better
result than basic
ACO algorithm [11].
7. Conducted the comparative
analysis based on ant
colony algorithm and
greedy algorithm for
finding the available routes
which increased the sensor
node count and different
number of ants.
Node’s energy
with two
different
network layouts
and energy of
the node with
various
pheromone
evaporation
rate.
ACO and Greedy
algorithm
ACO algorithm finds
several routes
compared to greedy
algorithm [12].
8. Introduced a new routing
model based on security
and routing efficiency. This
proposed model is a multi-
objective routing technique
that uses ACO algorithm
which integrates the trust-
based security for WSN.
Distance
covered, Node
usage levels and
Overhead
selection time.
TRMA (Trust based
Routing using
Modified ACO) and
PSO based model
The proposed model
effectively discovers
the shortest path with
minimum time
consumption [13].
9. Proposed an ACACO
(Adaptive Chaotic ACO)
algorithm using intelligent
sensor node for clustering
method. This method used
an adaptive strategy to
increase the transition
Energy
Consumption
GA and PSO
algorithms with
three different node
density
ACACO algorithm
gives best results
compared to PSO and
GA algorithms [14].

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probability and updated the
local pheromone by
releasing the advanced
ants. This chaos factor
modifies the current
pheromone content.
11. Introduced an AEACO
(Adaptive Elite Ant Colony
Optimization) algorithm to
reduce the energy usage in
High-density WSN
(HDWSN) routing. This
algorithm used two
operators to accelerate the
convergence speed,
namely, Adaptive and
Elite.
Energy
consumption
GA and PSO with
various node
density
The results show that
AEACO reduced
30.7% energy when
compared with
Genetic Algorithm
and 22.5% energy
reduced when
compared with PSO
algorithm [15].
12. Developed an Improvised
Energy Efficient Multipath
ACO based Routing
Protocol (IEEMARP) to
find the optimal path for
dynamic sensor network.
Packet delivery
ratio, Routing
Overhead,
Energy
consumption,
Pend to End
Delay and
Throughput
ACEMA,
Ant Chain,
EMCBR and IACR
The proposed
algorithm gives the
better performance in
all metrics compared
with other algorithms
[16].
13. Proposed a EMUC
(Energy-efficient multi-hop
routing with Unequal
Clustering) approaches for
creating clusters in various
sizes based on node
distance. This approach
focuses on increase the
lifetime of network.
Cluster
generation and
lifespan of
network
Various clustering
formation
The proposed
algorithm gives the
better lifetime and
minimum energy
consumption as well
as balance the node’s
energy [17].
In this literature study, ACO algorithm was used to various routing problems to increase the
network lifetime in WSN. Moreover, it was applied in different approaches, namely, fuzzy-based
technique, routing mechanism, clustering methods, hybrid technique and optimization. In this
investigation, the optimized and hybrid ACO algorithms give better performance when compared
with ACO algorithm. However, sill some network problems are not completely solved in wireless
sensor network. The results were taken in different performance metrics according to the
problem. The next section briefly describes ACO and m-ACO algorithm and this comparison
analysis.
3. ROUTE DISCOVERY METHOD USING ACO AND M-ACO ALGORITHMS
3.1. Route Discovery using ACO Algorithm
The ACO algorithm is a heuristic approach and it applied to multiple computational problems for
finding the optimum solutions by graphs. In this algorithm, the artificial ant is categorized into
two types, namely, forward and backward ants. These ants support to find optimum route to reach
the target in WSN. Concurrently, each node maintains a unique routing table for storing
information on the neighbour nodes. They are, distance between node, neighbour node ID, nodes

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balance energy and the pheromone concentration of the path. Following that, this process will
continue to occur till it reaches its destination.
In all the available routes, high level of pheromone concentration is considered the best route
during the route discovery process. The ACO algorithm followed the probability selection
method which is expressed from zero to one. Here, zero indicates non-occurrence and one
indicates the probability of occurrence. In ACO algorithm, the probability selection is used for an
ant selecting the neighbour nodes which represent the next hop node and the hop count.
Moreover, the following equation (Equation (1)) is used to compute the probability selection of
next hop selection [7].
𝑃𝑖𝑗 =
[𝑇𝑖𝑗]
∝
∗[𝛿𝑖𝑗]
𝛽
∑ [𝑇𝑖𝑗]
∝
∗[𝛿𝑖𝑗]
𝛽
𝑛𝜖𝑁𝑖
𝑗𝜖𝑁𝑖 (1)
In this equation, Pij is the probability of next hop from node i to j which is considered the hop
count and 𝑖 is represent the current node of route discovery and 𝑗 is the next node. In this
selection, the decision was made based on pheromone levels and heuristic data. 𝑇𝑖𝑗 denoted the
pheromone concentration in the route of 𝑖 and 𝑗. 𝛿𝑖𝑗 described the heuristic energy of route node
𝑖 and 𝑗. Moreover, ∝ and 𝛽 indicate the various weight factors, and the pheromone concentration
controlling weight indicated ∝. The weight of inspiration energy is indicated by 𝛽. The next hop
node set is denoted by 𝑁𝑖. Those ants can select only next hop nodes of 𝑖is stored except the
passed nodes.
Figure 3. Example Scenario of WSNs
Example network scenario is created to explain the working process of ACO algorithm which is
shown in Figure 3. This scenario totally contains eight nodes, and the nodes numbers is
represented there. The node 1 (1) is the sender and node 8 (8) is the receiver node in the scenario.
The purpose of this scenario is to select the optimum path in the meanwhile. Here, the i
mentioned in equation 1 indicate the source node (Node (1)). Moreover, j indicates the neighbour
nodes of i(Node (2), Node (3) and Node (4)) which is considered as next hop. In this case, node i
chooses the following node j which is based on probability selection using equation 1. For
example, if node3 is probably chosen as the next hop of node 1, it will change the position of i
and j.
The next time node (3) is considered as the i node and neighbour nodes are considered as j such
as node (6) and node (7). The same process is repeated till they find their destination which is
indicated inFigures4 (a) and (b). In Figure 4 (b), the i node finds its destination as the node (8).
Finally, data is transmitted from sender node (Node (1)) to target node (Node (8)).

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Figure 4. Next hop selection in WSNscenario
The following equations describe how to compute the probability selection of next hop node in
WSN.
𝑇𝑖𝑗 =
1
𝑑𝑖𝑗
(2)
The pheromone concentration or pheromone initialization of the route from node 𝑖 to 𝑗 is
calculated by Equation (2) The interspaces of the nodes between node 𝑖 to node 𝑗 is represented
in 𝑑𝑖𝑗. The heuristic energy of the node 𝑖 and 𝑗 is calculated by using Equation (3).
𝛿𝑖𝑗 =
𝑦
∑ 𝐸𝑛
𝑛𝜖𝑁𝑖
(3)
where, 𝑦 indicates the present energy of the node 𝑗 and the overall energy of the node set is
denoted by ∑ 𝐸𝑛
𝑛𝜖𝑁𝑖
. Here, if node j has a higher remaining energy, it has more possibility to be
chosen. Every artificial ant records the information about passed path in its memory. This
information supports the ant in returning to the starting point along the same route and updating
the pheromone level in the path. Pheromone evaporation is an important factor in ACO algorithm
which is to avoid increasing pheromone concentrations on the path. If there is no evaporation, the
paths chosen by the first ants would be overly appealing to the following ones. In this algorithm,
the pheromone evaporation is used to avoid increasing the pheromone concentration rate. The
pheromone evaporation rate is computed by Equation (4).
𝑇𝑖𝑗 = (1 − 𝜌) ∗ 𝑇𝑖𝑗 (4)
where, 𝜌 represents the evaporation rate of pheromone concentration and this range is 0 ≤ 𝜌 ≤ 1.
In this way, the backward ants produce some quantity of pheromone into the path from node 𝑖 to
𝑗 which is calculated by using Equation (5).
Δ𝑇𝑖𝑗 =
𝑤
𝑐𝑖
𝑑 (5)
where, the weight factor is denoted in 𝑤 and it controls the pheromone concentration of the path
(𝑖,𝑗). In this case, the cost of the link from node 𝑖 to node 𝑗 is denoted by 𝑐𝑖
𝑑
. The Equation (6) is
used to compute pheromone discharge level in the path 𝑖 to 𝑗.
𝑇𝑖𝑗 = 𝑇𝑖𝑗 + Δ𝑇𝑖𝑗 (6)
All the nodes participated in route selection process in wireless sensor network when using ACO
algorithm. Figure 5. illustrates the route discovery process with ACO algorithm in WSNs. Thus,

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the node with the minimum residual energy is involved in communication, and hence these
terminals can easily go to the death stage.
Figure 5. Flowchart of ACO algorithm
Also, it considers the best route with the high-level pheromone rate as the optimal route used
frequently for data transmission. Therefore, it creates some network issues in wireless sensor
network. There is link breakage, energy load unbalancing, and too much energy utilization in the
nodes as well as decrease in the network lifespan. This paper introduces a new approach (m-
ACO) solving these problems based on the threshold value, pheromone evaporation rate and the
remaining energy of the nodes. Further, modified ACO (m-ACO) algorithm and their
functionalities are explained below.
3.2. Route Discovery using M-ACO Algorithm
M-ACO algorithm followed the same routing methodology of ACO algorithm. Moreover, m-
ACO algorithm pseudo-code is explained in Algorithm 1. The node energy will be decreased
rapidly in the path when the pheromone level is high, because of the same nodes being used
frequently. Thus, the energy optimized technique is used in this algorithm for minimizing too
much of energy utilization and energy load balancing in wireless sensor network. Then, the
threshold value is applied to each node of the network to avoid the death nodes and follow
uniform energy consumption. Therefore, m-ACO algorithm also prolongs the lifespan of the
network. In this algorithm, ant can be seen selecting the optimal path with high residual energy,
and as if it has minimum pheromone rate in that path.Additionally, it includes the pheromone
evaporation rate in a different way for reducing the high-level pheromone of the path. The packet
structure of ant is illustrated in Figure 6 which contains the information of routing.
In this format, Type represents the ant type like forward ant and backward ant, and Src_Add
illustrates source address. It contains the source node ID. Moreover, serial number of ants is

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illustrated in Seq.No and Visit_Node store the nodes ID that the number of nodes visited in each
node. The Dest_Add indicates the destination address and the ant each time checks whether it is
destination. Time represents the ant travelling time from source to destination.
Figure 6. Packet structure of ant
The following Figure 7 illustrates the table format of sensor node. It stores the neighbor node
information in the route discovery process. Nodes ID is stored in ID and the neighbor nodes’
names stored in Neighbor ID format. Distance is represented by the distance between one node to
neighbor nodes. The node’s residual energy is stored in Res_Energy column. Then the amount of
pheromone contained in each path is stored in Pheromone_Rate7.
Figure 7. The table format of neighbour node
Algorithm1: Pseudo-Code for m-ACO Algorithm
1: Initialize the parameters
// X indicates the current node and Y indicates the neighbor nodes
// M is denotes source node and T is denotes the threshold value
2: for A = X // Select the current node
2: if X <= T //Get the neighbor node of X based on transmission range
3: K = []; //Store the neighbor nodes in K array
4: End if
5: for j = K
6: while Y== N // N is indicates the destination node
7: C = []; // Created the empty array for store the selected neighbor nodes
8: if Y <= Threshold (Check the residual energy of neighbor node)
// if yes, go to the probability selection otherwise increase the pheromone evaporation
rate
9: end if
10: end for
10: end while
11: for Y = i
12: Calculate the probability for selecting the next hop node
13: The same process followed till reach their destination
14: The backward ant goes to starting point and update the pheromone
15: end for
16: end for
The energy optimization of m-ACO algorithm depends on various energy levels, namely, initial
energy, remaining energy, and current energy of the nodes. The Equations (7) and (8)are used
and compute the energy usage which is used to find various energy levels [4][5][7]. The
transmission energy is calculated by using Equation (7), and received energy is calculated by
Equation (8). The structure of these energy models of WSN is described in Figure 8. The
parameters and values of energy consumption formula are presented in Table2.

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𝐸𝑇𝑥 = {
𝑙 ∗ 𝐸𝑒𝑙𝑒𝑐 + 𝑙 ∗ 𝜀𝑓𝑠 ∗ 𝑑2
, 𝑑 < 𝑑0
𝑙 ∗ 𝐸𝑒𝑙𝑒𝑐 + 𝑙 ∗ 𝜀𝑎𝑚𝑝 ∗ 𝑑4
, 𝑑 ≥ 𝑑0
(7)
𝐸𝑅𝑥 = 𝑙 ∗ 𝐸𝑒𝑙𝑒𝑐 (8)
where,
𝐸𝑇𝑥 : Energy consumption for data transmission.
𝐸𝑅𝑥 : Energy Consumption for data receiving.
𝑙 : Data bits.
𝐸𝑒𝑙𝑒𝑐 : Circuit energy loss.
𝜀𝑓𝑠 : Free space model.
𝜀𝑎𝑚𝑝 : Multipath Fading model.
𝑑 : Distance between nodes.
Figure 8. Energy analysis of radio energy model
Table2. List of Parameters used to Compute Energy Consumption in m-ACO
Parameter Value
Initial Energy 10J
α and β 1.5
µ 0.8
𝐸𝑒𝑙𝑒𝑐 50 nJ/bit
𝜀𝑎𝑚𝑝 0.0013 pJ/bit/m4
𝜀𝑓𝑠 10 pJ/bit/m2
The following Equation (9) is used to compute the probability of next hop node selection, for
node 𝑗 is the probability that ants will choose node j as the next hop from node i. The target of
this probability selection is maximum of 𝑃𝑖𝑗.
𝑃𝑖𝑗 = (1 − µ) ∗
[𝑇𝑖𝑗]
∝
∗[𝛿𝑖𝑗]
𝛽
∑ [𝑇𝑖𝑗]
∝
∗[𝛿𝑖𝑗]
𝛽
𝑛𝜖𝑁𝑖
+ µ ∗
𝐸𝑗
𝐸𝑖𝑛𝑖𝑡
𝑗𝜖𝑁𝑖 (9)
where, Pij represents the probability selection of next hop node from i to j. Tij represents the
pheromone concentration value in the path of 𝑖 and 𝑗.δij indicates the heuristic energy of the
route. ∑ [𝑇𝑖𝑗]
∝
𝑛𝜖𝑁𝑖
is the pheromone level of between current node and the neighbour node in the
path and ∑ [𝛿𝑖𝑗]
𝛽
𝑛𝜖𝑁𝑖
denoted the reciprocal energy of the nodes. After that, µ is indicates the
weight factor of energy of the node j. Therefore, α and βindicate the weight factors of pheromone

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and distance of the nodes in this equation. Eijand Einitindicate the balance energy of node 𝑗 and the
earliest energy of node 𝑗, respectively.
𝑇𝑖𝑗 = (1 − 𝜌) ∗ 𝑇𝑖𝑗 + Δ𝑇𝑖𝑗(0 < 𝜌 < 1) (10)
Δ𝑇𝑖𝑗 =
𝐸𝑗
𝐸𝑖𝑛𝑖𝑡
∗
𝑤
𝑑𝑗𝑏∗𝐻
(11)
The Equation(10) and Equation (11) are the pheromone updating formula. The ρ indicates the
pheromone evaporation rate and ΔTij indicates the pheromone secreted rate in the route of node 𝑖
to 𝑗 respectively. The pheromone evaporation rate is updated as 0 < 𝜌 < 1 which is used to
control the high level of pheromone concentration. In this, 𝐻 denotes the total number of
intermediate nodes through which node 𝑖 has traversed. The distance between the candidate node
j and the target node b is given by 𝑑𝑗𝑏. 𝐸𝑗 and 𝐸𝑖𝑛𝑖𝑡 indicate the energy of node 𝑗 and remaining
energy ratio respectively.
Figure 9. Flowchart of m-ACO algorithm
This algorithm controls the death node improves the network lifespan and minimizes the energy
consumption. Figure 9 illustrates the flowchart of m-ACO algorithm working process. Nodes
energy is substantially decreased in the current path of data transmission. Thus, there are
chances of death node occurring in high level when the same path is used frequently. Moreover,
the pheromone evaporation technique is introduced to solve this problem and avoid the current
path of data communication. The pheromone evaporation rate is increased substantially when
the energy of the node decreases to its threshold value. This method is used for avoiding the
same path being used frequently and avoid death nodes of this network. In this case, m-ACO

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algorithm found the more available paths for probable selection when increase the node
density.Moreover,the pheromone evaporation rate is increased and decreased based on the nodes
energy of the route which also reduced the frequent path. However,it works effectively when
usinglarge number of nodescomparatively than minimum node density.
4. RESULTS AND ANALYSIS
The experimental scenario is created to investigate the performance of ACO and m-ACO
algorithms using WSN parameters. Moreover, the experimental scenario and the results are
briefly discussed in the following section.
4.1. Simulation Parameters
In this experimental analysis, there is randomly deployed three different node density on the
targeted region, and the size of the region is 1500*1500 meters. For this analysis, 50,75 and 100
number of nodes are selected, and every node in this network is deployed at various individual
places on the targeted region. Moreover, these nodes are interconnected wirelessly with 500
transmission range to create the connection between them. This study not only focuses on 500
meters, it is inclusive of 200 to 300 meters range. This range is used for the evaluation of the
performance efficiency of this transmission range. Further, the experimental scenario is created
using wireless network parameters to data transmission. Table 3 illustrates the parameters that
create WSN scenarios.
Table 3. Simulation Parameters
Parameter Value
Nodes Density 50,75 and 100 Nodes
Optimization Algorithms ACO and m-ACO
Iterations 100 Numbers
Node Type Wireless Sensor Node
Transmission Range 500 Meters
Terrain Area 1500*1500 Meters
Traffic Type Constant Bit Rate (CBR)
Node Placement Strategies Random Node Placement
MAC Protocol 802.11
Packet Size 2000 bits
Simulation Time 30 Seconds
The following Figure 10 illustrates the experimental scenario with 50 number of nodes for this
experimental analysis. In this scenario, the green color circles are represented in source and
destination nodes. In this case, Node 1 is the source node, which is highlighted with green color,
and node 2 is the destination which is represented with red color. Network transmission range
represents the communication range of one node to another node and the range is 500 meters. A
node communicates with the other node with more than 500 meters apart with the help of
intermediate node. Therefore, intermediate nodes are indicated by blue color dots. Moreover, the
X-axis and Y-axis represent the region of data communication.

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Figure 10. Experimental scenario of 50 nodes
The following experimental scenario (Figure 11) was differentiated with 75 number of nodes
with random deployment. In this scenario, the intermediate node is placed randomly, but source
and destination nodes followed the same region of 50 number of nodes.
The following figure represents the experimental scenario of 100 number of nodes (Figure 12).
This scenario also followed the random node placement strategies for the intermediate nodes, but
source and destination nodes followed the same region of all above experimental scenario.

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The following Figure 13 represents the routing selection of ACO and m-ACO algorithms in the
created WSN scenario. This figure shows the optimal route between source and destination
which is represented with green and red color. Further, the violet color circles illustrate the
intermediate nodes from source to destination. This scenario mainly focused on the optimal path
of data transmission indicated with black color lines. In this case, both algorithms find a greater
number of optimal routes to destination.
Figure 13. Route selection scenario in WSN
4.2. Discussion
The comparative analysis of ACO and m-ACO algorithms is carried out by 100 numbers of
iterations with various node density. In this validation, three different node density, namely, 50,
75, and 100 nodes are used. This study uses 100 number of iterations to evaluate the performance
of algorithms in each iteration. Moreover, the average performance is also analysed in both
algorithms based on the performance of each 10 iteration. The following sections describe the
performance of both algorithms with different node density. In this evaluation, the network

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performance is analysed by various metrics such as throughput, packet loss, transmission delay,
packet delivery ratio, energy consumption and convergence rate. The following figure 14
described the performance of ACO and m-ACO algorithm.
4.2.1. Throughput
The number of data packets are successfully transmitted from source to destination in the given
amount of time. The average throughput performance of ACO and m-ACO algorithms are
illustrated in Figure 14 (a). The throughput performance is decreased in some iterations and
improved at the end of the iteration when using ACO algorithm with 50 number of nodes. In
ACO using 75 number of nodes, it is effectively prolonged till the 90th
iteration is reached when
compared with initial performance. Nevertheless, throughput decrease comparatively than ACO
with 50 nodes end of the simulation but reached the best result in the overall performance. The
analysis of ACO with 100 numbers of nodes is observed to gradually improve until 70th iteration
is reached after which there is decrease until the last iteration. In the analysis of ACO algorithm,
the best throughput value is obtained when placing 100 nodes than others and reaches the nearest
best value of 70th
iteration. In m-ACO algorithm, it was observed that it initially increased, and
then decreased and increased a few times until reaching the 90th iteration when placed 50 nodes.
However, the best throughput is obtained when reaching the 100th
iteration comparatively than
other algorithm with three increased number of nodes. Moreover, m-ACO algorithm has the most
similar performance till 50th
iteration when deployed 75 number of nodes. After 50th
iteration,
there is effective enhancement in most of the iterations, and the nearest best performance is
obtained when 100 iteration is reached. In m-ACO with using 100 nodes, before 50th
iteration the
results effectively improved. There is the highest performance of this experimental analysis in
30th
iteration. Moreover, after 50th
iteration, it is slower than the previous performance and again
effectively improved at the end of the iteration.

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Figure 14. The performance analysis of ACO and m-ACO algorithms with various metrics
4.2.2. Transmission Delay
Transmission delay refers to the amount of time taken for a packet to travel from the source node
to the destination node. The following figure (Figure 14 (b)) demonstrates the performance
analysis of ACO and m-ACO algorithms with three different increased number of nodes. This
delay time is measured by seconds, and the minimum time is considered as the best value. The
performance of transmission delay deeply increased, but after a few runs, it decreased before 50th
iteration when using ACO algorithm with 50 nodes. However, it gradually increased from 50th
iteration but finally reached the best performance of this algorithm when 90th
iteration was
reached. ACO algorithm with 75 nodes shows highest delay in the early stage, and gradually
decreased when reached 40th
iteration, Therefore, poor performance is seen in 50th
iteration, but
after that, iteration gives better performance and reached the highest best value. Moreover, ACO
with 100 nodes deeply decreased but after 50th
iteration it gradually increased until 100 iteration.
However, this algorithm achieved the nearest optimum results in 70th
iteration. In the analysis of
ACO algorithm with three different increased number of nodes, the average minimum delay
obtained from 100 nodes comparatively than 50 and 75 nodes. At the same time, m-ACO
algorithm obtained the maximum best solution in 20th
iteration when using 50 number of nodes.
After that, it gradually increased until it reached 90th
iteration but became deeply slower in a few
iterations. But, finally, better solution was gained compared to others in 100th
iteration. When
using m-ACO with 75 number of nodes, the similar performance was seen in first few iterations,

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and then it increased in 50th
iteration. After that, it decreased deeply, but after 70th
iteration delay
time is seen, highly increased and obtained the minimum delay when reached 100 iterations.
Finally, delay was exhibiting poor performance in a few iterations when using m-ACO algorithm
with 100 number of nodes. However, it obviously reached better performance in most of the
iterations, and particularly, this algorithm obtained lowest delay of this experimental analysis in
30th
iteration.
4.2.3. Energy Consumption
Energy consumption represented the quantity of energy consumed to data transmission process
until the data transmission is completed. This is measured by nanojoules, and the minimum value
of this is considered the best value which described in Figure 14 (c).In the initial stage, minimum
energy is consumed, but gradually it increased after 20th
iteration when using ACO algorithm
with 50 nodes. After that, it consumed minimum energy until it reached a few iterations.
Suddenly it increased when it reached the end of the iteration. This analysis shows stable
performance in higher number of iterations but increased few iterations in between when using
ACO algorithm using 75 number of nodes. Using ACO algorithm with deployed 100 number of
nodes shows minimum energy consumption in the beginning. After a few iterations, minimum
consumption is seen comparatively. However, energy consumption effectively decreased in 60th
iteration. After that, stable performance is seen, but it consumes minimum energy compared to
previous node density. In m-ACO algorithm with 50 number of nodes, it gradually decreased in
the beginning, but after a few iterations it gradually increased until it reached the 60th
iteration.
After that, it highly decreased, nevertheless consuming more energy than previous iteration when
reached end of the iteration. In this case, similar energy consumption is seen from beginning to
80th
iteration. It slowly increased when using m-ACO algorithm with 75 number of nodes.
However, it obtained the similar performance of initial stage but it shows more stable
consumption in each iteration. Finally, it obtained stable performance but consumed minimum
energy when reaching 50th
iteration in m-ACO with 100 number of nodes. After 50th
iteration, the
maximum energy consumption is seen, but it effectively decreased in 80th
and 90th
iterations.
Moreover, the maximum energy of data transmission is seen at the end of the iteration. In this
analysis, m-ACO algorithm obtained the minimum energy for data transmission comparatively
than ACO algorithm and other node density.
4.2.4. Packet Delivery Ratio
Packet delivery ratio describes the difference between the amount of data packets successfully
obtained at the receiver and the amount of data packets generated by sender node. This
performance analysis of ACO and m-ACO algorithm are illustrated in Figure 14 (d) which
differentiated with various node density like 50, 75 and 100. In this analysis, packet delivery ratio
shows that it slightly decreased in the first few iterations but improved when using ACO
algorithm with 50 nodes. After 50th
iteration, it gradually increased and nevertheless slowed down
when it reached 100 iteration. However, the highest best performance of this algorithm is
obtained from 90th
iteration. When using ACO algorithm with deployed 75 nodes, it
progressively increased but the performance decreased when it reached 50th
iteration. After this
iteration slightly increased in a few iterations and then decreased when it reached the end of the
iteration. However, ACO with 75 nodes enhanced that performance comparatively than 50 node
density. The performance of packet delivery ratio effectively prolonged and nevertheless
obtained less performance when it reached 70th
iteration in ACO with 100 nodes. Moreover, it
gradually decreased from 70th
iteration to end of the iteration but enhanced the average
percentage when compared with other node density. In m-ACO using 50 number of nodes, it
highly increased in the starting, but after 20th
iteration, the ratio progressively decreased.
However, the performance decreased continuously compared with previous iteration, but reached

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better results of this algorithm in the ending iteration. When using m-ACO algorithm deploying
75 number of nodes, the packet delivery ratio obtained the most similar results until 50th
iteration.
After that, the highest best performance of this node density was reached in 70th
iteration. It
slowed down again in a few iterations. Finally, the nearest best performance was obtained in 100
iteration but it was comparatively better than 50 nodes. In m-ACO with placing 100 nodes,
packet delivery percentage improved in most of the iterations comparatively than ACO and all
the other node density. This performance went down in a few iterations but obtained overall best
performance of this experimental analysis.
4.2.5. Packet Loss
This parameter indicates the total number of data packets lost till end of the data transmission in
network. The packet loss is created for some network problem. The following figure (Figure 14
(e)) represents the performance analysis of ACO and m-ACO algorithm with three increased
number of nodes. In this analysis, the minimum loss percentage was obtained from initial stage,
but gradually increased after a few iterations when using ACO algorithm with 50 numbers of
nodes. However, the highest loss rate in most of the iterations nevertheless reduced in the end of
the iteration when compared with other node density in ACO with 75 nodes. Although increasing
in the beginning, it gives lowerloss rate in the next few iterations and suddenly increased in the
ending. In ACO algorithm with 100 nodes, the performance of packet loss gradually decreased
the beginning to until it reached the 70th
iteration. After the 70th
iteration, maximum percentage of
loss was seen until the end of the iteration. The analysis of ACO algorithm obtained the minimum
loss percentage when placed 100 number of nodes comparatively than 50 and 75 nodes. Initially,
it gives best performance comparatively than ACO when using m-ACO algorithm with 50
number of nodes. It gives maximum loss rate in a few iterations but provides the same amount of
highest best value obtained in some iterations. When using m-ACO with 75 number of nodes, the
loss percentage is more stable in the first few iterations, but after 50th
iteration, the minimum
values were obtained until 80th
iteration comparatively than previous iterations. Finally, the
nearest best performance of less percentage of this node density was obtained when the 100
iteration was reached. The maximum best performance is achieved in most of the iteration when
using m-ACO algorithm with 100 number of nodes. Moreover, the highest best performance of
this experimental analysis is obtained from m-ACO with 100 nodes in 30th
iteration. However, it
gives the minimum of loss percentage compared with ACO algorithm and other node density.
4.2.6. Convergence Rate
The convergence rate of an algorithm describes the number of iterations it takes to converge to
the optimal solution. The following figure (Figure 14 (f)) describes the convergence rate of ACO
and m-ACO algorithms with respect to 100 iteration. In this experimental analysis, ACO
algorithm converged in after 70th
iteration when deploying 50 numbers of node. But the same
algorithm converged in the 60th
iteration when placed 75 number of nodes. After that, this
algorithm converged into the 20th
iteration itself when placing 100 nodes. In ACO algorithm, it
quickly converged when using 100 nodes comparatively than 50, and 75 nodes and take more
iteration to converge the solution when using 50 nodes. The performance of m-ACO algorithm,
converged in 11th
iteration when deployed 50 number of nodes comparatively than ACO
algorithm. In m-ACO algorithm, the convergence rate for 75 and 100 number of nodes was
converged on before it reached the 5th
iteration and the 11th
iterations respectively. In m-ACO
comparison, 75 number of nodes quickly converged the optimum solution comparatively than 50
and 100 nodes. After that, takes more time to converge the solution when deployed the 100 nodes
than 50 nodes. However, m-ACO algorithm with 100 nodes find the better solution even though
taking much time to converge the solution comparatively than 50 and 75 number of nodes.

91
The analysis of ACO and m-ACO algorithms effectively improved the performance of WSN
when increasing the node density, because, it leads to increase route counts for data transmission.
Therefore, m-ACO algorithm minimized the energy utilization, and also effectively prolongs the
lifetime of the network than traditional ACO algorithm. This is because, m-ACO algorithm
provides an energy limitation by threshold value to each node which maintains the alive node and
increases the nodes lifetime nodes in this network. Moreover, it includes the pheromone
evaporation technique which support to avoid current path when the energy of a node on that path
falls below the threshold. In this case, m-ACO algorithm selects the next optimum path. This
algorithm is analysed more performance metrics with various perspective when compared
literature study. Moreover, this study is accomplished with large simulation parameters when
compared with literature survey. Finally, m-ACO algorithm obtained the best performance in all
the metrics comparatively than ACO when there is increase in the node density.
5. CONCLUSION AND FUTURE WORK
The route discovery experimental analysis is conducted by ACO and m-ACO algorithm to
minimize the over energy consumption and increase the lifetime of the network in WSN.
Moreover, the optimum route selection improves the network performance and balances the
energy in each node. In this analysis, m-ACO algorithm is modified using energy threshold value
and increasing pheromone evaporation rate. The threshold value is applied in each node to
maintain uniform energy level in each node and lively network. Further, pheromone evaporation
rate controls the routing management as well as avoids the same path being used frequently. In
the comparative analysis, m-ACO algorithm shows better performance in all performance metrics
when compared with ACO algorithm. Moreover, enhanced network performance is seen in both
algorithms when increasing the node density compared to minimum nodes. Finally, it is
concluded that m-ACO algorithm with 100 number of nodes maximizes the network lifetime,
enhances the performance, and keeps the lively network better than ACO algorithm and other
node density. It can be further extended to incorporate other ACO variants as well as different
optimization algorithm and other performance metrics to conduct better investigation of this
study.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
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AUTHORS
Ms. R. Shanmugavalli, Research Scholar in Department of Computer Science,
Centre for Machine Learning and Intelligence at Avinashilingam Institute for Home
Science and Higher Education for Women, Tamil Nadu, India. The author has
published 1 Journal, 1 International Conference and 1 Book Chapter. The Research
area of interest is Wireless Sensor Network. She is currently teaching few machine
learning courses to UG and PG students in various departments.
Professor P. Subashini is working for Department of Computer Science,
Avinashilingam University for Women, Tamil Nadu India, from 1994. She is the
coordinator of the Centre for Machine Learning and Intelligence sanctioned by
Department of Science and Technology. Her research has spanned a large number of
disciplines like Image analysis, Pattern recognition, Neural networks, and
Computational Intelligence. She has authored and co-authored 4 Books, 5 Book
chapters, 1 Monograph, 150 research papers both at international and national levels.
she received ten sponsored research projects worth more than 2.54 crores from
various government funding agencies. She also extended her contribution towards various international
collaborations with universities from USA, Germany and Morocco.

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