Citation: Adday, G.H.; Subramaniam,
S.K.; Zukarnain, Z.A.; Samian, N.
Fault Tolerance Structures in Wireless
Sensor Networks (WSNs): Survey,
Classification, and Future Directions.
Sensors 2022, 22, 6041. https://
doi.org/10.3390/s22166041
Academic Editor: Jaime Lloret
Received: 6 July 2022
Accepted: 10 August 2022
Published: 12 August 2022
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Review
Fault Tolerance Structures in Wireless Sensor Networks
(WSNs): Survey, Classification, and Future Directions
Ghaihab Hassan Adday
1,2,
* , Shamala K. Subramaniam
1
, Zuriati Ahmad Zukarnain
1
and Normalia Samian
1
1
Department of Communication Technology and Network, Faculty of Computer Science and Information
Technology, University Putra Malaysia, Serdang 43400, Malaysia
2
Computer Science Department, Faculty of Computer Science and Information System, University of Basrah,
Basrah 61004, Iraq
* Correspondence: gs59192@student.upm.edu.my or ghaihab.aday@uobasrah.edu.iq; Tel.: +60-11-5993-3674
Abstract:
The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates.
The Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks (WSNs), is
an important subsection component of the IR 4.0. WSNs are a good demonstration of an ambient
intelligence vision, in which the environment becomes intelligent and aware of its surroundings.
WSN has unique features which create its own distinct network attributes and is deployed widely
for critical real-time applications that require stringent prerequisites when dealing with faults to
ensure the avoidance and tolerance management of catastrophic outcomes. Thus, the respective
underlying Fault Tolerance (FT) structure is a critical requirement that needs to be considered
when designing any algorithm in WSNs. Moreover, with the exponential evolution of IoT systems,
substantial enhancements of current FT mechanisms will ensure that the system constantly provides
high network reliability and integrity. Fault tolerance structures contain three fundamental stages:
error detection, error diagnosis, and error recovery. The emergence of analytics and the depth of
harnessing it has led to the development of new fault-tolerant structures and strategies based on
artificial intelligence and cloud-based. This survey provides an elaborate classification and analysis
of fault tolerance structures and their essential components and categorizes errors from several
perspectives. Subsequently, an extensive analysis of existing fault tolerance techniques based on
eight constraints is presented. Many prior studies have provided classifications for fault tolerance
systems. However, this research has enhanced these reviews by proposing an extensively enhanced
categorization that depends on the new and additional metrics which include the number of sensor
nodes engaged, the overall fault-tolerant approach performance, and the placement of the principal
algorithm responsible for eliminating network errors. A new taxonomy of comparison that also
extensively reviews previous surveys and state-of-the-art scientific articles based on different factors
is discussed and provides the basis for the proposed open issues.
Keywords:
Wireless Sensor Networks (WSNs); Fault Tolerance (FT); error detection; error diagnosis;
error recovery
1. Introduction
The exponential growth of the Industry Revolution 4.0 (IR4.0) concept is a fundamental
paradigm that encompasses, among other future industrial innovations, the IoT, the Internet
of Services (IoS), and WSNs [
1
]. WSN is the backbone of the IoT architecture, which allows
us to detect our surroundings, collect vital statistics, and send them to the final destination
called the Base Station (BS) [2]. Therefore, the IoT is highly oriented toward the WSN as a
critical platform for data sensing and communication [
3
]. WSN offers the infrastructure for
the evolving IoT involving a wide spectrum of areas and fields [
4
]. For example, intelligent
products such as sensor devices, camera systems, and wearable technology are used in a
wide range of situations such as intelligent homes and intelligent transportation. Smart
Sensors 2022, 22, 6041. https://doi.org/10.3390/s22166041 https://www.mdpi.com/journal/sensors
