  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.
