## 1 Introduction  
A Wireless Sensor Network (WSN) is a distributed collection of tiny sensor nodes which are assumed to be the self-operating and esteemed networks which can process the communication, sensing and distance estimation between the nodes in the network. Node coverage and localization are two of the main concerns involved in WSN for energy-efficient data communication [1]. In many wireless sensor network applications, the location information from which the data were obtained is necessary. As a result, the location of sensor nodes affects WSN performance. Compared to other methods, the original bat optimization technique has a lower mean localization error and takes less time to compute, but its localization efficiency is lower than 100% and it tends to get stuck at a local ideal value [2]. Most of the existing systems' node coverage optimization and localization algorithms fail to accurately estimate the location of the nodes. Moreover, they fail to optimize node coverage, which will result in partitioning the network into disjointed segments. Therefore, providing efficient node coverage with accurate node location estimation in the network is needed to establish the connectivity among the nodes in the network [3]. The node coverage optimization and localization identification of the nodes are addressed by using the various algorithms in soft computing and Bio-inspired models. Particle Swarm Optimization (PSO) is a straightforward bio-inspired algorithm that finds the optimal solution; this algorithm is different from the other, following optimization techniques because it is not dependent on any gradient or any differential form of objectives, it needs only objective function. In addition, less hyper parameters are in use [4]. Various PSO and evolutionary algorithms have been applied to the WSN to provide an efficient solution to network node optimization, node localization, node clustering and node segmentation. The PSO algorithms work on the node with the node coverage problem to enhance the network lifetime and the coverage rate among the nodes in the network [5]. In WSN, PSO algorithms have been used to estimate the localizations of the nodes based on the angle measurement between the sensor nodes and the anchor node. Metrics such as the packet delivery ratio between the nodes are used to determine the angle measurement of the nodes in sensor nodes, time of arrival of the packets and range of the received signal strength [6]. Moreover, the PSO algorithms have been applied to WSNs to enhance the quality of service and provide energy-efficient data dissemination in a wireless sensor network. The bio-inspired algorithms have an efficient proposal for predicting the unknown nodes in the localization area and enhancing the various optimum node estimation parameters [7]. For the node coverage optimization challenges, the Bat Algorithm has been used to simulate the echolocation behavior of a bat. The Bat Algorithm has been employed, along with the various evolutionary algorithms, to provide the optimal solution to the node coverage and node localization problems in WSN. The node localization is estimated with the received signal strength; hence, the data are assumed to be from the proper location [8]. The unknown nodes near the sensor nodes are estimated with the analysis of the unknown sensor nodes, which are closer to the neighbor nodes and are taken into account in the known locations. While estimating the location of the unknown node using the node coverage parameters, it is essential to monitor the computational cost, and the received signal should not accumulate the data inferred in the optimal allocation [9]. The optimal constraint is analyzed with the hop node communication, and the distance is measured with hop-1 parameters multiplied by the total distance estimation. The PSO, Ant Colony Optimization algorithm (ACO) and The Artificial Bee Colony (ABC) algorithm are the estimation algorithms which identify the node localization using the received signal strength for the unknown node address.