Abstract:
The Wireless Sensor Network (WSN) consists of
many sensors that are distributed in a specific area for the
purpose of monitoring physical conditions. Factors such as
hardware limitations, limited resources, unfavourable WSN
deployment environment, and the presence of various attacks on
nodes can lead to the presence of Faulty Nodes in a WSN. This
raises the problem of detecting Faulty Nodes and avoiding Data
loss. Detecting Faulty Nodes in real-world scenarios will
improve the quality of a WSN. The research was aimed at
developing an algorithm to determine the location of Faulty
Nodes in a WSN. The algorithm uses characteristics of Machine
Learning and Support Vector Machines (SVM), which use the
classification of Data into true and false. A Mathematical Model
for determining Faulty Nodes using the SVM is considered. A
methodology for detecting a Faulty Node is demonstrated, which
consists of Data Collection, Feature Extraction, Training, and
Testing the Model. The Results of simulated experiments that
were conducted with different numbers of nodes from 50 to 320
are shown. The Model is tested on Data very similar to realworld sensing Data to evaluate the ability of the Model to detect
failed nodes and calculate training and testing errors. As a
result, the training error is 4.6667%, the accuracy of detecting
faulty nodes was 97%. The simulation results demonstrate the
high stability of the proposed algorithm and are suitable for
network environments with irregular node distribution or
coverage gaps. In real scenarios, it can provide a high level of
uninterrupted operation of the WSN and lossless data
transmission. Shortcomings and prospects in research on fault
detection in WSN, such as studying real-world hardware issues
and its security, were presented.
