An image classification approach for hole detection in wireless sensor networks

Hole detection is a crucial task for monitoring the status of wireless sensor networks (WSN) which often consist of low-capability sensors. Holes can form in WSNs due to the problems during placement of the sensors or power/hardware failure. In these situations, sensing or transmitting data could be...

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Bibliographic Details
Published in:arXiv.org 2022-03
Main Authors: Se-Hang Cheong, Kim-Hou, Ng, Si, Yain-Whar
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Datasets
Image classification
Layouts
Network topologies
Nodes
Sensors
Wireless networks
Wireless sensor networks
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Summary:Hole detection is a crucial task for monitoring the status of wireless sensor networks (WSN) which often consist of low-capability sensors. Holes can form in WSNs due to the problems during placement of the sensors or power/hardware failure. In these situations, sensing or transmitting data could be affected and can interrupt the normal operation of the WSNs. It may also decrease the lifetime of the network and sensing coverage of the sensors. The problem of hole detection is especially challenging in WSNs since the exact location of the sensors is often unknown. In this paper, we propose a novel hole detection approach called FD-CNN which is based on Force-directed (FD) Algorithm and Convolutional Neural Network (CNN). In contrast to existing approaches, FD-CNN is a centralized approach and is able to detect holes from WSNs without relying on the information related to the location of the sensors. The proposed approach also alleviates the problem of high computational complexity in distributed approaches. The proposed approach accepts the network topology of a WSN as an input and generates the identity of the nodes surrounding each detected hole in the network as the final output. In the proposed approach, an FD algorithm is used to generate the layout of the wireless sensor networks followed by the identification of the holes in the layouts using a trained CNN model. In order to prepare labeled datasets for training the CNN model, an unsupervised pre-processing method is also proposed in this paper. After the holes are detected by the CNN model, two algorithms are proposed to identify the regions of the holes and corresponding nodes surrounding the regions. Extensive experiments are conducted to evaluate the proposed approach based on different datasets. Experimental results show that FD-CNN can achieve 80% sensitivity and 93% specificity in less than 2 minutes.
ISSN:2331-8422