  Coverage hole detection is an important research problem in wireless sensor
network research community. However, distributed approaches proposed in recent
years for coverage hole detection problem have high computational complexity.
In this paper, we propose a novel approach for coverage hole detection in
wireless sensor networks called FD-TL (Force-directed and Transfer-learning)
which is based on layout generation capability of Force-directed Algorithms and
image recognition power of Convolutional Neural Network with transfer learning.
In contrast to existing approaches, the proposed approach is a pure
topology-based approach since FD-TL can detect both triangular and
non-triangular coverage holes from a wireless sensor network based on the input
network topology without relying on the physical locations of the anchor nodes.
In FD-TL, a Force-directed Algorithm is used to generate a series of possible
layouts from a given input topology. Next, a Convolutional Neural Network is
used to recognize potential coverage holes from the generated layouts. During
the training phase, a transfer learning method is used to aid the recognition
process. Experimental results show that FD-TL method can achieve 90%
sensitivity and 96% specificity for coverage hole detection in wireless sensor
networks.
