Citation: Mishra, M.; Sen Gupta, G.;
Gui, X. Investigation of Energy Cost
of Data Compression Algorithms in
WSN for IoT Applications. Sensors
2022, 22, 7685. https://doi.org/
10.3390/s22197685
Academic Editor: Óscar García
Received: 18 August 2022
Accepted: 7 October 2022
Published: 10 October 2022
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Article
Investigation of Energy Cost of Data Compression Algorithms
in WSN for IoT Applications
Mukesh Mishra * , Gourab Sen Gupta and Xiang Gui
Department of Mechanical and Electrical Engineering, School of Food and Advanced Technology,
Massey University, Palmerston North 4442, New Zealand
* Correspondence: m.mishra@massey.ac.nz
Abstract:
The exponential growth in remote sensing, coupled with advancements in integrated
circuits (IC) design and fabrication technology for communication, has prompted the progress
of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting,
processing, and communicating remotely. Sensor nodes have limited resources such as memory,
energy and computation capabilities restricting their ability to process large volume of data that is
generated. Compressing the data before transmission will help alleviate the problem. Many data
compression methods have been proposed but mainly for image processing and a vast majority
of them are not pertinent on sensor nodes because of memory impediment, energy utilization
and handling speed. To overcome this issue, authors in this research have chosen Run Length
Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can
be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and
energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE,
is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data
compression algorithms, simulations were run, and the results compared with the compression
techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for
five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data
compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual
energy throughout all iterations.
Keywords: data compression; RLE; adaptive huffman encoding; H-RLEAHE; IoT
1. Introduction and Motivation
The 21st century has been characterised by dramatic shifts in the ways that technology,
commerce, and social patterns are organised. The fourth industrial revolution, often
known as Industry 4.0, is the result of the trend toward automation and the subsequent
reduction in human involvement in production across most sectors [
1
]. Wireless sensor
networks (WSN) and the Internet of Things will play crucial roles in the Fourth Industrial
Revolution (IR 4.0 or 4IR). Since IoT devices can move, share, and exchange data without
human interaction [
2
], it enables high flexibility and ease of implementation in a variety of
applications. Wireless sensor networks have a limited lifespan due to the significant impact
power consumption has on their performance [
3
]. Energy-efficient media access control
and routing protocols [
4
] are only a couple of the ideas put up to address this problem.
Long-term environmental monitoring is a primary goal of a large number of wireless sensor
network (WSN) applications. As a result, saving battery power for sensor nodes is an
important consideration. Sensor nodes have two main ways to save energy. The use of
node redundancy can be a solution to this problem, as it allows a subset of sensor nodes to
stay active while the others are put to sleep to save energy. All of the monitored areas must
be covered by the subset of active sensor nodes, and the network must remain connected.
Furthermore, these sensors must ensure that the network performs as effectively as it does
Sensors 2022, 22, 7685. https://doi.org/10.3390/s22197685 https://www.mdpi.com/journal/sensors
