Self-checking method for fault tolerance solution in wireless sensor network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 4, August 2020, pp. 4416~4425
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i4.pp4416-4425  4416
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Self-checking method for fault tolerance solution
in wireless sensor network
Muayad Sadik Croock, Saja Dhyaa Khuder, Zahraa Abbas Hassan
Computer Engineering Department, University of Technology, Iraq
Article Info ABSTRACT
Article history:
Received Jan 6, 2020
Revised Mar 22, 2020
Accepted Mar 28, 2020
Recently, the wireless sensor network (WSN) has been considered in
different application, particularly in emergency systems. Therefore, it is
important to keep these networks in high reliability using software
engineering techniques in the field of fault tolerance. This paper proposed
a fault node detection method in WSN using the self-checking technique
according to the rules of software engineering. Then, the detected faulted
node is covered employing the reading of nearest neighbor nodes (sensors).
In addition, the proposed method sends a message for maintenance to solve
the fault. The proposed method can reduce the time between the detection
and recovery of a fault to prevent the confusion of adopting wrong readings,
in which the detection is making with mistake. Moreover, it guarantees
the reliability of the WSN, in terms of operation and data transmission.
The proposed method has been tested over different scenarios and
the obtained results show the superior efficiency in terms of recovery,
reliability, and continuous data transmission.
Keywords:
Fault tolerance
Self-checking technique
Software engineering
WSN
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Muayad Sadik Croock,
Department of Compute Engineering,
University of Technology,
Al-sinaa Street, Baghdad, Iraq.
Email: 1
Muayad.S.Croock@uotechnology.edu.iq; 2
120099@uotechnology.edu.iq;
3
120052@uotechnology.edu.iq
1. INTRODUCTION
It is well known that the WSN has been considered in numerous fields of the modern life, such as
smart buildings and emergency systems [1]. These applications provide the WSN with high importance and
should be more reliable, flexible and extendable. These properties have been addressed by designing
the software algorithms according to the software engineering techniques [2]. The fault tolerance method is
one of the most important part in the software engineering that can guarantee the operation of a system with
high reliability. To be more precise, the self-checking technique can be considered for checking the status of
such system based on the readings with the time. The concepts of this technique are built on allocating upper
and lower threshold values for right operation to any part of a system. These values are used for checking
the validity of the system, in which the fault can be detected efficiently [3].
In general, the fault in WSN are occurred mainly in the nodes (sensors) [1]. These faults can be
classified into hardware and software. These two classifications are also being under the permanent and
temporary faults. As mentioned above, the self-checking approach is used for detecting the fault of nodes and
the monitoring system sends a message for maintenance for solving the occurred problems [2]. Throughout
the maintenance procedure, the system can cover this error in different ways depending on the design and
status of the related conditions [3].
In this paper, a monitoring system for WSN is proposed based on software engineering techniques.
The proposed method adopts the fault tolerance approach for managing the operation of nodes inside

Int J Elec & Comp Eng ISSN: 2088-8708 
Self-checking method for fault tolerance solution in wireless sensor network (Muayad Sadik Croock)
4417
the WSN. In the fault detection part of the proposed method, the self-checking technique is considered with
upper and lower threshold for each node included in a cluster. The designed WSN contains numerous clusters
and each of which includes different nodes. At a time that the fault is occurred in a node, the proposed
method takes the neighbor nodes reading to tackle the problem in an efficient way. The recovery operation is
performed by calculating the average value of nearest neighbor nodes until the fault is solved. The proposed
system is tested under different conditions and the achieved results prove the satisfactory in terms of
accuracy and efficiency.
2. RELATED WORK
The applications of WSN and related supported systems attract the attentions of numerous
researchers in distinct fields. Each group of them tackled a side of appeared problems, such as faults, data
transmission. All these researches focused on introducing robust systems with high accuracy, reliability,
flexibility and extendibility. Therefore, the concepts and approaches of software engineering were adopted.
In [4], the authors presented a developed management method for WSN based on software
engineering rules. They classified the situations of WSN in terms of infrastructure and found the suitable
adapting software. While the authors of [5] proposed a real time oil and gas monitoring system based on
WSN using the software development as a part of software engineering. The presented system took care of
different equipment, such as pumps, sensors and metering infrastructure. In addition, it managed the flow of
oil and gas sequentially. In [6], a new framework for WSN was presented. The introduced framework
was built based on software engineering challenges including flexibility and scalability. It worked as
a middleware that could manage the data mining, energy consumption and self-organization. In addition,
the authors of [7] performed a modeling framework for analyzing the features in terms of variation and
commination. These features provided a complete view for the WSN based agriculture system. All proposed
algorithms and software were designed under the rules of software engineering. In [8], a searching based on
software engineering was implemented in WSN. The implemented method adopted the multi-objective
algorithm to be solved using genetic method. It also considered the fixed obstacles in the searching process.
The authors of [9] introduced hardware and software infrastructure for WSN based on the regulation of
software engineering, particularly the software requirements. The focused on the receiver and how it
described itself to the others nodes of WSN. The work was done in physical layer in real time situation.
It is important to note that the test results of the literature research works were clear enough to prove
the claim of authors.
At the other hand, the fault tolerance of software engineering was adopted in solving the problem of
reliability. In [10], a survey on fault detection and the solutions were presented. It considered the relation
between different types of faults in WSN nodes and the choosing of suitable fault detector as well as fault
tolerance method. This relation could lead to the root of faulted node, in which the solution was presented.
The authors of [11] proposed a management architecture of WSN based on fault tolerance approach.
The proposed architecture tackled two sides of challenges inside WSN. The first one is the power
consumption, while the other side is the fault tolerance to cover the self-organized network. This was to
increase the reliability of the WSN and become fault tolerance. In [12], a new trend of guaranteeing
the reliability of WSN was presented based on fault tolerance method. Both theory and applications were
considered in the oriented research. In [13], a cluster head based fault tolerance system was proposed.
The objectives of the proposed system were to keep eye on the WSN throughout the operation in terms of
data transmission, mobility and fault occur.
The solutions were also provided to guarantee the reliability. The authors of [14] used an agent
factor to propose a fault tolerance method for WSN. The platform of the proposed method worked across
different levels including sensors, cluster heads and base stations. In [15], the researchers employed the fault
tolerance method for reducing the redundancy and packet loss over the broken links (paths) of WSN and
finding the solutions to keep the WSN in operation. Moreover, they supported the security of the data
transmission using cryptography methods. The authors of [16] designed a software engineering based fault
detection method for optimization method that was proposed. In [17], a list of the recent methods of fault
detection and diagnosing was presented. It included a real survey on fault detection in WSN. The researchers
of [18] proposed a fault detection based clustering network. The proposed method involved two sides; firstly,
the cluster configuration, while the other is fault detection and recovery. In [19], a method of solving
the problem of node isolation in AGR-MAC protocol was presented. The proposed method used fault
tolerance technique for maintaining the isolation problem. The authors of [20] proposed a guaranteed
algorithm of bioinformatic connectivity in WSN based on fault tolerance technique for ambulance system.
In [21], a WSN based wildfire detection system was proposed based on self-organization and fault
tolerance approaches.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 4416 - 4425
4418
At the other hand, a system based virtual biological community of nature living style has been
proposed. The evolutionary game method was adopted to introduce a fault tolerance procedure for managing
the problems of WSN [22]. In [23], a fault tolerance method was presented for solving the problem of cluster
head failures. This was done by planning the solution based on virtual heads and the collected information.
The researcher of [24] proposed a data transmission protocol in save situation using fault tolerance method.
This protocol used the residual information regarding the links of WSN to build a plan for tolerant the faults.
In [25], the cluster head selection method was proposed based on fault tolerance and fuzzy-logic approaches.
The soft computing method provided the system with optimality in allocating the cluster head, in which
the failure is covered well. In [26, 27], different methods for WSN structure and clustering methods have
been proposed to solve numerous problem. They used software engineering regulations in building
the proposed methods.
3. THE PROPOSED SYSTEM
As mentioned earlier, the proposed system includes two sides of work based on software
engineering methods that are used for guarantee the reliability of the adopted WSN. The first one is related to
detect the occurred fault in a node at the adopted WSN using the self-checking method. While the second one
takes care of finding the solution of the detected fault using the available tolerance in finding the recovery
values within the neighborhood area.
3.1. System structure
In this section, the general structure of the proposed system is explained as a block diagram shown
in Figure 1. It is shown that the collected reading data from the underlying sensors of allocated WSN is
entered to the fault detection unit. This unit is responsible on detecting the fault that can be occurred in
a node using the self-checking approach. This approach is based on upper and lower threshold values for
each sensor as a tolerance range. The received values that fall within the outage tolerance area is classified as
a fault. If there is no detected fault, the data is sent to the system safely.
Figure 1. The proposed system structure
The detected faults are passed to the fault tolerance unit. This unit is responsible on finding
the solution to the problem of fault using the available tolerances. The solution adopted in the proposed
method uses the values of neighbor nodes with correct values for guessing the expected reading of the faulted
sensor node. Whilst the processing of finding the solutions, a communication between fault detection unit
and fault tolerance unit. The reason behind this communication is exchanging the information and correct any
error can be happened in finding the tolerant solution. The results of fault detection and the tolerant solution
is applied to send the corrected readings as well as the estimated reading to the system for continuing
the process.
3.2. Proposed algorithm
It is well known that the software algorithm is necessary to be proposed for managing the fault
detection and finding the solution. In this section, the proposed algorithm is presented as a flowchart,
shown in Figure 2.

4419
Figure 2. Flowchart of the proposed algorithm
Based on the flowchart of the proposed algorithm, the working procedure can be summarized in
the following:
a. Collecting the readings of the sensor nodes in the adopted WSN.
b. Checking the validity of the received readings by the cluster head. In case the readings are not valid,
a request is sent back to the nodes for providing another copy of readings.
c. The valid readings are checked for fault detection using the self-checking process based on the upper
and lower threshold as explained mathematically in (1):
𝜸 𝒖𝒑𝒑𝒆𝒓 ≥ 𝜹(𝒊, 𝒋) ≥ 𝜸𝒍𝒐𝒘𝒆𝒓 (1)
Where 𝜸 𝒖𝒑𝒑𝒆𝒓 and 𝜸𝒍𝒐𝒘𝒆𝒓 are the upper and lower threshold values. In case of fault occur, the proposed
algorithm sends the information to the next step. Otherwise, the readings can be passed to the system.

 ISSN: 2088-8708
4420
It is important to highlight that the 𝜸 𝒖𝒑𝒑𝒆𝒓 and 𝜸𝒍𝒐𝒘𝒆𝒓 values are selected depending on the type of used
sensor as well as the surrounding conditions.
d. The faulted readings are passed to the fault tolerance unit. This unit adopts the valid readings of
the close neighbour nodes. The most important condition is the validity of these reading as could be
happened the even the close neighbour readings have faults as well according to the following equation:
𝛿(𝒊, 𝒋) =, {
𝜹(𝒊 ± 𝒌, 𝒋 ± 𝒌), 𝜸 𝒖𝒑𝒑𝒆𝒓 ≥ 𝜹(𝒊 ± 𝒌, 𝒋 ± 𝒌) ≥ 𝜸𝒍𝒐𝒘𝒆𝒓
𝜹(𝒊, 𝒋) , 𝒐𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆
} (2)
Where 𝜹(𝒊, 𝒋) is the instant reading of a node with location (i,j) in the covered area. 𝜸(𝒊 ± 𝒌, 𝒋 ± 𝒌) is
the next neighbor readings, increased by 𝒌 ∈ 𝑲 total WSN dimension, and k={1,2,3,…,K}.
e. It is noted from (2) hat the instant reading of a node can be replaced with the neighbour nodes in case
they are valid. In order to increase the validity of estimation and the accuracy, the average value can be
adopted amongst the valid close neighbours in the estimation process. Therefore, the estimated value
𝐸 𝛿(𝒊,𝒋) of reading of a node 𝛿(𝒊, 𝒋) is mathematically written as:
𝑬 𝜹(𝒊,𝒋) =
𝟏
𝑵 𝒏𝒐𝒅𝒆
∑ 𝜹(𝒊 ± 𝒌, 𝒋 ± 𝒌)𝑲
𝒌=𝟏 (3)
Where 𝑵 𝒏𝒐𝒅𝒆 is the total number of adopted neighbor nodes to the underlying node 𝛿(𝒊, 𝒋).
f. Checking the estimated value of a node 𝑬 𝜹(𝒊,𝒋) if it is within the upper and lower threshold vlues as:
𝜸 𝒖𝒑𝒑𝒆𝒓 ≥ 𝑬 𝜹(𝒊,𝒋) ≥ 𝜸𝒍𝒐𝒘𝒆𝒓 (4)
g. Sending the valid values (sensors’ readings) to the system for future processing.
4. RESULTS
In order to test the proposed method that has been explained in early sections, a simulation model is
adopted. The proposed model includes a WSN with four clusters, each of which involves cluster head and six
sensors, as shown in Figure 3. The cluster heads are connected to the device manager as a base station.
Temperature sensors are adopted in this simulated model that can be changed to different types of sensors.
This model is built using Visual Studio C# language and the interface presents the results in numerous color
for more explanation. The green boxes represent the well working nodes, while the red boxes are the faulted
nodes. Inside each box, the instant sensor reading is shown. The nodes are connected to the cluster head
within each one individually.
Figure 3. Simulated model interface, case study

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Different case studies have been considered to test the system under changeable conditions.
The case study, shown in Figure 3, explains that the cluster one has three faulted sensor nodes, mentioned in
red color. In addition, cluster two involves two faulted sensors with red label. Moreover, cluster three
includes a faulted sensor, while the fourth cluster points three sensor nodes to be faulted. Depending on
the proposed algorithm and based on equations (1-4), the proposed method compensates the faulted readings
with estimated values. It is important to note that the proposed algorithm amongst all clusters checks
the readings of neighbor nodes if they are within the thresholds to be considered. Otherwise, it considers
the next neighbor instead to guarantee the estimation with minimum error occur. The simulation parameters
are built based on the upper and lower threshold of (35o
c) and (55o
c), sequentially, for a whole July month
in Baghdad city at Iraq.
Figure 4 shows the same case study of Figure 3 after applying the proposed algorithm. I can be seen
that the faulted readings of the sensors in all clusters are estimated based on the neighbors and labeled in blue
color. As mentioned earlier, the results are measured for thirty days among July month. The performance of
each sensor in all clusters can be measured accurately. Figure 5 shows the performance of sensor nodes in
cluster one. The case here that there is no fault is detected, and all sensors are working well in cluster one.
It is seen that the whole readings are within the validity range between upper and lower thresholds.
Figure 4. Case study of Figure 3 after applying the proposed algorithm
Figure 5. Cluster one performance in case of normal working

 ISSN: 2088-8708
4422
At the other hand, the same cluster one passes by worst conditions, in which there are three sensors
suffer from faults. This can lead to changing in the readings of these sensors. These faults are detected using
the proposed algorithm that is based on self-checking process. The detected faults are compensated by
applying the proposed fault tolerance method based on the neighbor nodes as explained earlier.
Figure 6 explains the readings of sensors inside cluster one, where the faulted readings exceed the upper
threshold values. It is shown that the faults are started at day seven, twelve and fourteen, sequentially.
Figure 6. Cluster one performance in case of fault detection
Now the proposed method should solve this problem by applying the presented algorithms. This is
done efficiently as shown in Figure 7. The faulted readings are fixed by estimating replaced values depending
on the valid readings of neighbor nodes. To be more precise, sensor node number four in cluster one has been
analyzed in Figure 8. It is shown that the estimated values of this sensor are within the validity range between
thresholds with acceptable error.
Figure 7. Cluster one performance in case of fixing faults

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Figure 8. Sensor four analysis regarding normal, fault and fixed
Figure 9 illustrates the performance of the adopted WSN with four clusters. For each cluster,
the average values of normal, faulted and fixed cases are evaluated according to the following equation:
𝑨𝒗 𝜹(𝒊,𝒋)(𝒛) =
𝟏
𝑵 𝑻𝒐𝒕𝒂𝒍
∑ ∑ 𝜹(𝒊, 𝒋)
𝑱
𝒋=𝟏
𝑰
𝒊=𝟏 (5)
where 𝑨𝒗 𝜹(𝒊,𝒋)(𝒛) is the average values of the cluster z, and z={1, 2, 3, …, Z} and Z is the number of clusters
within the WSN. 𝑵 𝑻𝒐𝒕𝒂𝒍 is the number of all readings of sensors inside the cluster z, represented as a matrix
of (6 X 30) for six sensors and thirty days. I is considered as the number of sensors, which is six, while J
represents the number of days (thirty days). These values are compared in this figure and shown that
the estimated values are very close to the normal ones, while the faulted reading increased in notable way.
This means that the proposed algorithm and method are working in efficient way.
Figure 9. The total cluster performance for normal, faulted and fixed cases
Table 1 shows the recorded readings of sensors over thirty days of July month for cluster three at
the present of fault occur, as an example to the proposed system fault monitoring. It can be seen that
the values of readings of the 6th
sensor are increased sharply after the 10th
day as the fault happened.
In addition, the readings of sensor four decreased to become zeros and this is indicated as a fault as the values

 ISSN: 2088-8708
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went down the lower threshold starting from day 11th
. At the other hand, sensor two provides wrong readings
after 23rd
day, where the values are increased notably. The values, highlighted with yellow color,
are recorded as faults due to the exceeding of upper and lower thresholds (25-55o
c), which are the maximum
and minimum temperatures among this month.
Table 1. Cluster 3 sensors after fault (in sensor 2, 4, 6)
Day Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6
1. 36 43 31 38 34 47
2. 40 40 49 40 43 49
3. 44 37 31 34 31 48
4. 39 42 41 44 32 32
5. 47 32 36 49 34 38
6. 50 36 48 43 41 36
7. 46 48 41 49 50 34
8. 46 46 32 30 49 37
9. 32 33 31 45 40 43
10. 49 44 48 45 37 76
11. 39 37 40 0 39 70
12. 40 39 38 0 43 71
13. 34 38 39 0 31 76
14. 33 30 49 0 45 74
15. 39 43 34 0 35 71
16. 41 49 30 0 45 70
17. 47 42 43 0 38 75
18. 41 40 33 0 38 63
19. 41 50 49 0 40 78
20. 45 34 34 0 34 68
21. 47 45 38 0 40 68
22. 32 42 35 0 32 74
23. 46 65 49 0 49 74
24. 38 65 31 0 32 63
25. 45 59 34 0 31 60
26. 33 75 37 0 31 58
27. 35 66 30 0 47 74
28. 37 68 31 0 36 63
29. 34 64 50 0 40 77
30. 33 75 30 0 39 78
It is important to note that the proposed algorithm does not change the complexity of the WSN as
there is no any addition to the hardware side. In addition, in fault appearance cases, the solution is produced
until they are fixed. Therefore, the proposed algorithm consumed a sensible time to recover the errors,
in which the real-time concepts are still satisfied.
5. CONCLUSION
This paper proposed a software engineering based fault tolerance method for fault detection and
introducing a solution in WSN. The proposed method adopted the self-checking process for fault detection.
In addition, the proposed algorithm considered the neighbor sensor nodes to estimate the faulted readings in
efficient way. The average value of the close neighbors was adopted in the proposed algorithm to increase
the accuracy of estimation as well as the reliability. In case of fault occur in the close neighbor node, the next
valid one was considered. It is important to note that the robust response of the proposed method allowed
the use in real-time system with high activity of fault tolerance. The proposed method was tested over
different case studies at an assumed simulation model with different clusters in WSN. The obtained results
showed the proved performance of the proposed method in terms of reliability and accuracy.
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