Citation: Jannoud, I.; Jaradat, Y.;
Masoud, M.Z.; Manasrah, A.; Alia, M.
The Role of Genetic Algorithm
Selection Operators in Extending
WSN Stability Period: A
Comparative Study. Electronics 2022,
11, 28. https://doi.org/10.3390/
electronics11010028
Academic Editors: Alvaro Araujo
Pinto and Hacene Fouchal
Received: 22 November 2021
Accepted: 20 December 2021
Published: 22 December 2021
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Article
The Role of Genetic Algorithm Selection Operators in
Extending WSN Stability Period: A Comparative Study
Ismael Jannoud
1
, Yousef Jaradat
1,
* , Mohammad Z. Masoud
1
, Ahmad Manasrah
2
and Mohammad Alia
3
1
Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman 11733, Jordan;
ismael.jannoud@zuj.edu.jo (I.J.); m.zakaria@zuj.edu.jo (M.Z.M.)
2
Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman 11733, Jordan;
ahmad.mansrah@zuj.edu.jo
3
Computer Science Department, Al-Zaytoonah University of Jordan, Amman 11733, Jordan;
dr.m.alia@zuj.edu.jo
* Correspondence: y.jaradat@zuj.edu.jo
Abstract:
A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to
improve the performance of specific implementations. Parent selection, crossover, and mutation
are examples of these operators. One of the most important operations in GA is selection. The
performance of GA in addressing the single-objective wireless sensor network stability period
extension problem using various parent selection methods is evaluated and compared. In this
paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic
universal sampling, tournament, and truncation. According to the simulation results, the truncation
selection operator is the most efficient operator in terms of extending the network stability period and
improving reliability. The truncation operator outperforms other selection operators, most notably the
well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput
by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in
terms of the network residual energy after each protocol round.
Keywords:
genetic algorithm; roulette wheel; exponential selection; rank selection; tournament
selection; stochastic selection; truncation selection; WSN stability
1. Introduction
As a result of rapid advancements in the field of micro-electro-mechanical systems
(MEMS), small sensor nodes have become inexpensive and self-sufficient [
1
]. These sensor
nodes can sense and monitor the environment, analyze and aggregate data, and communi-
cate data to one other or to a central point, commonly known as the sink. Sensor nodes
can be interconnected to serve an application-specific purpose through the use of wireless
sensor networks (WSN) [2,3].
Sensor nodes have a certain amount of battery power, and these batteries are rarely
rechargeable. Sensor nodes often use the most energy for their communication functions [
4
].
When a node’s energy source runs out, the node is declared dead and is no longer useful.
WSNs can be used in a wide variety of real-world situations. They are employed in a
variety of fields, including agriculture, industry, health care, surveillance, target tracking,
and security management, in both the civilian and military sectors [
5
–
7
]. Figure 1 shows a
typical sensor node block diagram [8].
Sensor nodes have the ability to send their collected data straight to the sink node [
9
],
but this consumes more energy and leads to the premature death of the node; as a result,
other issues are introduced into the network, such as the coverage/hole problem [
10
].
Using clustering, sensor nodes can be balanced in terms of energy consumption. Clustering
is based on putting together nodes that are near each other or have similar characteristics
or functions. Cluster heads (CH) and member nodes (MN) are the two types of nodes that
Electronics 2022, 11, 28. https://doi.org/10.3390/electronics11010028 https://www.mdpi.com/journal/electronics
