Citation: Akram, J.; Munawar, H.S.;
Kouzani, A.Z.; Mahmud, M.A.P.
Using Adaptive Sensors for
Optimised Target Coverage in
Wireless Sensor Networks. Sensors
2022, 22, 1083. https://doi.org/
10.3390/s22031083
Academic Editors: Alvaro
Araujo Pinto, Hacene Fouchal and
Matteo Anedda
Received: 9 December 2021
Accepted: 28 January 2022
Published: 30 January 2022
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Article
Using Adaptive Sensors for Optimised Target Coverage in
Wireless Sensor Networks
Junaid Akram
1
, Hafiz Suliman Munawar
2,
* , Abbas Z. Kouzani
3
and M A Parvez Mahmud
3
1
Department of Computer Science, Superior University, Lahore 54000, Pakistan; junaidakram@superior.edu.pk
2
School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
3
School of Engineering, Deakin University, Geelong, VIC 3216, Australia;
abbas.kouzani@deakin.edu.au (A.Z.K.); m.a.mahmud@deakin.edu.au (M.A.P.M.)
* Correspondence: h.munawar@unsw.edu.au
Abstract:
Innovation in wireless communications and microtechnology has progressed day by day,
and this has resulted in the creation of wireless sensor networks. This technology is utilised in
a variety of settings, including battlefield surveillance, home security, and healthcare monitoring,
among others. However, since tiny batteries with very little power are used, this technology has
power and target monitoring issues. With the development of various architectures and algorithms,
considerable research has been done to address these problems. The adaptive learning automata
algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a
time-saving scheduling method. As a result, each sensor node in the network has been outfitted with
learning automata, allowing them to choose their appropriate state at any given moment. The sensor
is in one of two states: active or sleep. Several experiments were conducted to get the findings of the
suggested method. Different parameters are utilised in this experiment to verify the consistency of
the method for scheduling the sensor node so that it can cover all of the targets while using less power.
The experimental findings indicate that the proposed method is an effective approach to schedule
sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our
technique against the LADSC scheduling algorithm. All of the experimental data collected thus
far demonstrate that the suggested method has justified the problem description and achieved the
project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be
utilised as a useful technique for scheduling sensor nodes.
Keywords:
sensors; wireless sensor network; learning automata; adaptive learning; coverage area;
energy efficiency
1. Introduction
The rate of advancement in the area of wireless communication is steadily rising.
With the increased usage of wireless communication, many devices and applications are
developing. Wireless sensor networks are the most popular and growing area of wireless
technology (WSNs). These sensors are tiny, low-power, low-cost, multi- functional devices
with a short-range communication capability. The network’s sensors main functionalities
include detecting, processing, and communicating [
1
–
3
]. The development of wireless
sensor network technology began while military applications for battlefield surveillance
were being developed. The development and innovation of new methodologies in wireless
sensor networks has expanded the field of application, and it has been used to monitor
various fields such as home, disaster prevention, pollution, environmental monitoring,
health care, temperature, and so on [4–7].
The coverage area is another aspect of wireless sensor networks [
8
–
10
]. This is the
region in which a sensor node observes and tracks the activities of the chosen target. Each
target should also be constantly monitored by at least one of the sensor nodes to ensure
network operation continuity. While doing so, effort has been taken to ensure that energy
Sensors 2022, 22, 1083. https://doi.org/10.3390/s22031083 https://www.mdpi.com/journal/sensors
