Energy Efficient Routing Design for Target Tracking in Wireless

Sensor Network

DEEPIKA LOKESH

Department of Electronics & Communication,

AMC Engineering College, Bangalore, 560076,

INDIA

N. V. UMA REDDY

Department of AI & ML,

New Horizon College of Engineering,

Bangalore, 560103,

INDIA

Abstract-The wireless sensor networks (WSNs) provides an advance way for connection of various

applications. Most of the applications use the wireless sensor network to transmit the information from the

device to the main base station. The main operation of the wireless sensor network is to first sense the data

using the sensor, then collect the data and finally transmit to the required base station. The data is transmitted in

a timely manner such that the other wireless sensor network transmitting the information should not have any

problem. During the transmission of the data the main objective of the wireless sensor network device is to

provide reliability to the information which is being sent with less latency and to reduce the energy

consumption in order to increase the lifespan of the wireless sensor network. Therefore, this paper presents an

Energy Efficient Routing for the Target Tracking (EER-TT) in the wireless sensor network which provides

reliability, less latency and reduces the energy consumption during the transmission of the information to the

base station. Proposed model also provides a cluster selection method for the routing of the wireless sensor

network devices. The results attained show that our model EER-TT shows better results when compared with

the existing routing-based models.

Key-words: - Target tracking, Routing, Cluster head selection, Network performance, Energy efficiency,

Reliability.

Received: April 12, 2021. Revised: April 14, 2022. Accepted: May 11, 2022. Published: June 21, 2022.

1 Introduction

Using the wireless sensor network devices provides

many benefits because they are small in size, cost is

less and the wireless mode of communication helps

to communicate to any sensor device easily. The

benefits of the wireless sensor networks make the

devices more robust. Due to these benefits of the

wireless sensor networks, it is used in target

tracking application [1]. Moreover, many

applications nowadays use target tracking

application to track the devices, see the location or

to track a target. Hence, wireless sensor network

devices provide various solutions for the target

tracking applications.

Many of the studies have been going on in the

field of target tracking applications and the scholars

attained a result that by using the wireless sensor

network the tracking task can be done efficiently.

But the scholars also found some limitations such

as during the transmission of the data the accuracy

of the target tracking is reduced [2]. To resolve

these issues [3] presented a method which provides

a higher accuracy for the target tracking and

reduces the consumption of energy during the

transmission. Another method was proposed by [4]

in which they have used an automata algorithm

which tries to find the minimum number of nodes

which can reduce the time to transmit the data and

provide better Quality-of-service (QoS)

requirements which in turn also reduces the energy

consumption. Moreover, most of the new wireless

sensor network devices use the target tracking

prediction method to track the target or to find the

location of the target [5]. In [6], they have proposed

a method which uses the signal intensity of the

node to track the node and provide a better cluster

head selection method. During the selection

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Deepika Lokesh, N. V. Uma Reddy

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process, the cluster head is identified and selected

using the communication range of the cluster head,

the amount of information it can carry, and the

amount it can fuse to the base station. One of the

most important problem that has not been resolved

in the wireless senor network for the target tracking

applications is to increase the lifespan of the

device. In order to increase this, the latency of the

wireless sensor device has to be reduced. To

resolve this issue, [16] used the evolutionary

computing method, in [17], they have used a cluster

formation method which reduces the computational

overhead. In [18], they developed a routing method

in which the packets are sent from the node to the

gateway hub utilizing less energy and less packet

loss during the transmission. This method did not

address the problem of the data transmission

latency. Hence, to reduce the network data

transmission latency various models have been

developed in [7], [22], [23], and [24]. In [8] they

have presented a fuzzy method for the selection of

the cluster head and in [9] used the multipath-based

transmission method. In both [8] and [9] they have

attained better results when they have varied with

the existing models. Moreover, these models did

not consider the environmental conditions for the

target tracking applications [10], [11]. This led to

the improper scheduling of packet loss and

consumed more energy and reduced the overall

performance of the target tracking applications

[12], [13], [14].

From all these research work, we have built a

model, Energy Efficient Routing method for the

target tracking applications (EER-TT) for the

wireless sensor network. In this model the wireless

sensor networks are first placed in various regions

of the fixed network area. Then the distance of each

of the wireless sensor network which are adjacent

to each other is calculated. After this process, the

EER-TT model is presented which provides a

selection method for the cluster head which

provides reliability for the transmission of the data,

reduces the energy consumption and increases the

network coverage. This method also reduces the

communication overhead and the data

communication latency and increase the lifespan of

the wireless sensor network device.

2 Energy Efficient Routing Design

for Target Tracking in Wireless

Sensor Network

In this section, the Energy Efficient Routing Design

for Target Tracking Applications in the wireless

sensor network has been discussed. In this model,

the wireless sensor network devices are implanted

with the tracking sensor which is provided with a

battery to provide power and also to carry out all

the sensing operations that a tracking sensor has to

perform. Moreover, the wireless sensor network

devices are mostly placed in various locations such

that it can sense different regions and transmit the

data of each region to the edge device for the

further data calculation and evaluation. The whole

process is given in Fig.1.

Fig. 1: Framework for our proposed model for

target tracking in wireless sensor networks.

2.1 Selection of Cluster Heads for the

Wireless Sensor Networks

In this model, first the distance between the

overlapping wireless sensor network devices is

computed using the wireless sensor network device

distance  which is in the region. The

distance of the overlapping wireless sensor

network is calculated using the following equation

󰇯 



󰇰

(1)

In the Equation (1), the  is evaluated using the

given equation

󰇛 

 󰇜

(2)

The wireless sensor devices which are normally

overlapping is attained using the given equation





(3)

In above equation the parameter  is in range

between  and  which can be described as 

. Here the  is used to depict the mean ratio

of the WSN device cluster head with respect to the

particular time. The evaluation of  is done using

the given equation

 

,

(4)

In improving cluster head selection, the distance is

normalized probabilistically. Therefore, the

normalized coverage is measured as follows

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󰇛󰇜󰇛󰇜

(5)

In Eq. (5), the  is used to describe the average

size of the cluster head. Hence, the improved

threshold value 󰇛󰇜 to select a correct cluster

head for a given wireless sensor network device 

is given using the following equation

󰇛󰇜

󰇱 󰇛󰇜

󰇛󰇜󰇟󰇛 󰇛󰇜 󰇜󰇠



(6)

In Equation (6),  is used to denote the current

session time (round) and is in the range.

 is used to denote the value of wireless sensor

network device which has not been a cluster head

for a long period of time.  is used to denote the

cluster head for a current working session  󰇛󰇜 

Using this method, each of the wireless sensor

network device is elected as a cluster head for a

given instance of time having different

probabilities. After the selection of the cluster head

an efficient routing path is established which

uses less energy and has less latency using the

following equation

󰇛󰇜

(7)

In Equation (7), the  denotes the remaining

energy of the wireless sensor network device. 

denotes the anticipated hop size. In this work the

Equation (7) is minimized that both energy

consumption and number of hops required for

transmission is reduced. This provides reliability

with improved energy efficiency for the wireless

sensor network. The result attained by the Energy

Efficient Routing Design for Target Tracking in

Wireless Sensor Network have been discussed in

the result and discussions section.

3 Results and Discussions

In this section the attained results during the

experimentation have been discussed. For the

execution of the Energy Efficient Routing Design

for Target Tracking in Wireless Sensor Network,

the Intel quad processor having 8 GB of RAM has

been used. Along with theses specifications,

Windows 10 has been used with the SENSORIA

simulator [19] has been considered for the

execution of the code. The code has been scripted

in C# and C++ programming language. The target

tracking application using H-infinity filter [15] is

used for validating model. In [25] the dataset for

complex target maneuvering is taken from [26].

The Table 1 shows the parameter used for

conducting simulation.

Table 1. Parameters considered for the analysis of

performance and reliability for both the proposed

model EELLR-TT and the existing model LEACH

Parameter

Value

Simulation area





Base stations



Number of devices



Transmission range



Sensing range



Initial energy



Radio energy consumption



Data packets length



Bandwidth



Idle energy consumption



Signal amplification

energy consumption

100



Fig. 2: Lifetime of the wireless sensor network

varied with number of nodes.

3.1 Performance of Network Lifetime

In this section the results for the performance of the

network lifetime of the nodes has been evaluated

using some of the parameter set. The performance

has been evaluated by considering a fixed network

area where the wireless sensor network devices

have been deployed in a fixed region of that area.

The results have been shown graphically in the

Fig.2. Proposed model shows an improvement of

 and  for  and

 wireless sensor network nodes respectively

when compared with the existing LEACH model.

200

400

600

800

1000

1200

1400

1600

400 600 800

NUMBER OF ROUND (S)

NUMBER OF NODES

N E T W O R K L I F E T I M E P E R F O R M A N C E

EER-TT

LEACH

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The overall network lifetime performance shows an

improvement of  when compared with the

LEACH routing-based model.

3.2 Performance of Communication

Overhead

In this section the results for the performance of the

communication overhead of the nodes has been

evaluated using some of the parameter set. The

performance has been evaluated by considering a

fixed network area where the wireless sensor

network devices have been deployed in a fixed

region of that area. The results have been shown

graphically in the Fig.3. Proposed model shows a

reduction of  and  for

 and  wireless sensor network nodes

respectively when compared with the existing

LEACH model. The overall network lifetime

performance shows an improvement of 

when compared with the LEACH routing-based

model.

Fig. 3: Performance of Network Communication

Overhead varied with number of nodes.

3.3 Performance of Data processing Latency

In this section the results for the performance of the

data processing latency of the nodes has been

evaluated using some of the parameter set. The

performance has been evaluated by considering a

fixed network area where the wireless sensor

network devices have been deployed in a fixed

region of that area. The results have been shown

graphically in the Fig.4. Proposed model shows a

reduction of  for

 and  wireless sensor network nodes

respectively when compared with the existing

LEACH model. The overall data processing latency

shows an improvement of  when compared

with the LEACH routing-based model.

Fig. 4: Performance of Network Data Processing

Latency varied with number of nodes.

3.4 Performance of Routing Overhead

In this section the results for the performance of the

routing overhead of the nodes has been evaluated

using some of the parameter set. The performance

has been evaluated by considering a fixed network

area where the wireless sensor network devices

have been deployed in a fixed region of that area.

The results have been shown graphically in the

Figure 5. Our model shows a reduction of

for  and 

wireless sensor network nodes respectively when

compared with the existing LEACH model. The

overall routing overhead shows an improvement of

 when compared with the LEACH routing-

based model.

Fig. 5: Performance of routing overhead varied

with number of nodes.

3.5 Comparison of the Proposed EER-TT

Method and other Existing Methods

In this section, we have compared EER-TT model

with the existing models. The results attained by

EER-TT model show that it has outperformed all

0,05

0,1

0,15

0,2

0,25

400 600 800

COMMUNICATION OVERHEAD

NUMBER OF NODES

C O M M U N I C A T I O N O V E R H E A D

EER-TT

LEACH

100

150

200

250

300

350

400

400 600 800

LATENCY

NUMBER OF NODES

D A T A P R O C E S S I N G L A T E N C Y

EER-TT

LEACH

400 600 800

ROUTING OVERHEAD

NUMBER OF NODES

R O U T I N G O V E R H E A D

EER-TT LEACH

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the existing models that either have used the fuzzy-

based routing method or swarm optimization

method such as PFuzzy ACO (Particle-Fuzzy Ant

Colony Optimization) [8], MDT-FCSO (Multipath

Data Transmission Fuzzy Cat Swarm

Optimization) [9], and EER [20]. The comparison

results have been given in the Fig.6 which has been

described below.

Fig. 6: Proposed EER-TT model compared with the

existing routing models.

4 Conclusion

This paper presents an Energy Efficient Routing

Design for Target Tracking (EER-TT) in Wireless

Sensor Network. The main aim of the wireless

sensor network in the target tracking application is

to reduce the consumption of energy. Many

existing methods have been proposed to reduce the

consumption of energy in the wireless senor

network but have failed to provide reliability. Most

of the futuristic applications for the target tracking

require less energy consumption and reliability

such that if any failure occurs it should be able to

transmit the data through some node or an of the

wireless sensor network. Many techniques have

failed to provide the latency also in the wireless

sensor networks. Some of the existing techniques

have used the fuzzy-based methods and swarm

optimization method to resolve these issues by

transmitting the data using a multipath but failed

due to the varying nature of the wireless sensor

network. Hence, due to these problems many

packets are lost. Our EER-TT model provides less

consumption of energy, less latency, more

reliability and computation overhead. Our model

has been compared with the existing system and the

results show that our model has better performance

when compared with the existing routing-based

methods.

For the future development of our model, we

would consider to measure the packets which have

been lost during the transmission and also to

develop a multi-objective routing method in order

to improve the latency in the inter-cluster data

communication. We would also try to evaluate the

routing performance of the target tracking

applications by considering various kind of filters.

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