## 3 Major Problem Statement  
This section briefly explains the research barriers in enhancing the coverage of the network by the efficient consumption of energy to achieve better QoSensing. The existing works do not provide an overall solution for achieving better QoSensing in which either coverage enhancement or energy consumption is optimized; however, both these factors must be focused on to accomplish the objective. The overall problem in this research topic is mentioned below. Authors in [28] indicate how the quality of sensing (QoSensing) decides the overall performance of the network. In any application, the WSN aims to achieve better QoSensing. In general, the QoSensing is measured in terms of coverage ratio. To enhance the coverage ratio, existing works have focused on two methods: (i) optimal sensor deployment (initial stage) and (ii) coverage enhancement (hole detection and recovery (after deployment)). The first method requires a high cost to find the optimal position for all nodes and is not suitable for large-scale networks. Hence, the coverage enhancement procedure has been followed by many works. However, these methods fail to detect the hole accurately and to recover the hole by using a minimal number of nodes due to poor algorithm design, limited metrics consideration and so on. Besides, the formations of hotspots due to energy consumption of the nodes are the major factors for coverage holes. Thus, improving energy efficiency also helps in enhancing QoSensing by preventing the coverage holes. However, energy efficiency enhancement to improve QoSensing is lightly focused on by the authors. The prevention of coverage holes will reduce the overhead of the coverage hole recovery phase. Authors in [29] aim to improve the coverage ratio in the WSN. The overall sensor network is considered as equal size grids. Then, the grids with the minimum coverage ratio are considered as the candidate grids. Next, the grids which have a coverage ratio lower than the threshold value are considered for the coverage enhancement process. In those grids, the mobile nodes are optimally positioned in order to ensure proper coverage. To find the optimal position for mobile nodes, this work uses the particle swarm optimization (PSO) algorithm. The PSO algorithm considers moving distance as the metric in order to minimize energy consumption. The major problems encountered in this approach are:  
* The PSO algorithm finds the optimal position for the mobile nodes but is not able to find which nodes to move. Considering random mobile nodes in the PSO algorithm increases energy consumption further, i.e., if the selected mobile node is far away from the hole, then the node must travel for a long distance. * Equal grid construction leads to coverage holes due to high energy consumption in particular nodes. * The PSO algorithm has convergence issues which lead to mobile movement non-optimally. According to the authors in [30], the coverage hole detection problem is resolved by using different concepts and theorems. Mainly, this work utilizes the Rips complex concept to detect the coverage hole. To simplify the Rips complex, first the [PERSON]'s theorem is combined with the clustering coefficient. This process removes the redundant nodes by putting them in sleep mode. By removing the redundant nodes, the network is simplified. Next, the concept of a complete graph is utilized to remove the redundant edges. At last, the Rips complex is applied on the simplified network in order to detect the hole detection. The problems faced by this work are as follows:  
* To simplify the network, this work puts the nodes with large clustering coefficients into sleep mode. Although it simplifies the network, it affects the connectivity among other nodes. It also results in large coverage holes. In addition, turning off the node with the higher clustering coefficient also leads to a higher number of holes in the network. * In general, the Rips complex concept is complex and not suitable for the resource-constrained sensor networks.