## 2 Related Work  
Authors in [16] mainly concentrate on coverage hole detection through participated nodes and coverage hole recovery by maximizing the sensing range of those nodes. For that, this paper presents a heuristic hole healing (H3) algorithm. The H3 algorithm first performs hole detection based on the radius of the nodes participating in the network. Then, the optimal node is selected to repair the hole through the H3 algorithm. The optimal node is selected based on overlapping degree and residual energy level. The H3 algorithm uses a fuzzy inference system (FIS) to select an optimal node. The optimal moving distance and sensing are also determined to heal the coverage hole. Sensing range maximization is a traditional approach to heal the coverage problem. However, this method increases the energy consumption and complexity for the optimal node that is selected to heal the hole. The overall network is considered to be mobile, but the optimal node detection does not consider any mobility metrics. Thus, there will be frequent coverage hole occurrences whenever the healing node moves to another position. In [17], the authors focus on coverage hole detection and enhancement in WSNs. For that, this work presents a hybrid hole healing algorithm (3 HA). This work determines the optimal position to improve the coverage rate. Then, new additional nodes are deployed in the determined position in order to improve the coverage ratio. The 3 HA algorithm is performed in a distributed manner. At first, the coverage hole is detected by constructing a Voronoi diagram for the network. Then, the Voronoi list is reduced to formulate linear programming and a probabilistic sensing model. Nevertheless, adding additional nodes increases the deployment cost and the holes are not fully recovered, and the construction of a Voronoi diagram is a complex process and consumes a large amount of time. In [18], the authors dynamically analyze the network and determine the coverage holes. Firstly, the network topology is analyzed based on a simple subnet. Upon the subnet, the boundary nodes are determined, and the area of polygon is computed. Three categories of subnets such as (i) with simple subnet, (ii) without isolated nodes and (iii) with multiple subnets are considered for a better analysis. As the recovery mechanism, the inactive nodes are activated. For that, the network is initialized with active and inactive nodes. When the coverage hole occurs, then the inactive node is activated to repair the coverage hole. However, the coverage hole recovery process is not efficient since it simply activates the random nodes without considering the network environment and there is a high possibility to form new holes due to the high energy consumption. In [19], the authors propose a cluster-based routing approach to achieve better energy efficiency. For that, a multi-objective fractional particle lion (MOFPL) algorithm is proposed. The MOFPL algorithm combines the particle swarm optimization and lion optimization algorithm with the concept of fractional theory. Initially, the network is clustered, and the cluster head (CH) is assigned in each cluster. Then, the optimal CH is selected to enable routing based on multiple objectives such as energy, distance, delay, density and traffic rate. Through the optimal CH, the data are transmitted to the BS. However, PSO and lion optimization are combined with fractional theory to improve the convergence rate. This results in large time consumption. Initially, CHs are randomly selected which increases energy consumption. Authors in [20] mainly focus on optimal CH selection to improve the energy efficiency. To enable optimal CH selection, a hybrid optimization algorithm is presented. The hybridalgorithm is built with the crow search algorithm and dolphin echolocation algorithm. The objective is formulated to enhance data transmission and also to enhance energy efficiency.