# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 2 Related Work
This protocol can recover the coverage hole formed due to the random deployment of the nodes. In [25], the authors use the knowledge of the location of each node for detecting coverage holes in randomly-deployed wireless sensor networks. In [26], the authors present a novel method for coverage hole detection considering the residual energy of the nodes in randomly-deployed wireless sensor networks. In this paper, by calculating the life expectancy of working nodes through the residual energy, the authors make a trade-off between the network repair cost and energy waste in which the working nodes with a short lifetime are screened out according to a proper ratio. As one of the best health indicators of the sensor network, the coverage holes directly decide the quality of the sensor network. In [27], the authors firstly propose an active contour model-based coverage hole detection algorithm for the sensor network, which can accurately evaluate both the number and the size of the holes. The hybrid deployment of WSNs with static and mobile nodes in the monitoring area is an important issue to cover a maximum sensing area with a limited number of nodes. Furthermore, mobile sensor nodes can relocate themselves to improve the coverage area in the network. In [28], the authors propose a method that reduces the complexity of the relocation of the initial deployment and coverage hole healing of mobile sensor nodes in the hybrid WSNs. Their method finds the ways to get the shortest distance movements for the mobile nodes in WSNs. An adaptive threshold distance is used to eliminate some mobile nodes, which are already occupied or situated within the threshold distance from the optimal new positions. In [29], the authors develop distributed algorithms to detect and localize the coverage holes in sensor networks. This paper uses algebraic topological methods to define a coverage hole and develops algorithms to detect a hole. In [30], the authors design a stochastic learning weak estimation-based scheme, namely mobility prediction inside a coverage hole. The main objective of this scheme is that it could be able to correctly predict the mobility pattern of a target inside a coverage hole with low computational overhead. In [31], the authors propose an intelligent strategy called the improved hybrid particle swarm algorithm to repair the coverage holes. Taking a hybrid sensor network, the authors consider the displacement of mobile sensors, energy consumption and energy balancing together. Their proposed protocol can schedule the redundant mobile sensors effectively to repair the coverage holes by moving them to appropriate locations. Besides, they improve the event detection rate to calculate the event detection lost or the detection error and coverage holes that seriously damage the quality of monitoring in WSNs. In [32], the authors design a localized coverage force division algorithm to find the coverage quality in WSNs. However, their algorithm can neither detect the coverage holes nor find the shape and size of a hole. In [33], the authors propose a novel method for describing the coverage holes graphically. Their proposed graphical hole description method is divided into two phases, namely coverage hole detecting and coverage hole describing. In [34], the authors present a coverage hole healing algorithm taking the existing nodes of the WSN to cover the holes. Their algorithm resolves a full coverage while minimizing the overlapped area of the sensing disks and deploying a minimum number of nodes. It is to be noted that wireless sensors are deployed to monitor certain regions remotely for a wide range of potential applications, such as environment monitoring, object tracking, habitants monitoring and traffic control. If one or more sensors are dead in the network, the whole purpose of deploying sensors over the monitoring area becomes useless. As shown in Figure 1a, if the sensor \(D\) is dead, no communication is established among the sensors.