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Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.

With the advancement of sensor technologies, Wireless Sensor Networks (WSN) are envisioning a rich variety of promising services in many fields. WSN is formed by the deployment of sensor nodes in the regions of interest using a deterministic or random deployment strategy. The random deployment strategy is more suitable in large monitoring areas and harsh environments. But, in this type of deployment, coverage holes and disconnected networks can exist. Important events may get unnoticed reducing the reliability of the networks. Since the locations of nodes are unknown in a randomly deployed WSN, it is difficult to locate the holes. For this, we are proposing a localization and deployment model. The localization algorithm uses Arithmetic Optimization Algorithm (AOA) and the results of this algorithm are further used to develop a deployment model to achieve a completely connected network. This algorithm is tested in various fields. The algorithm is able to localize nodes accurately and identify the coverage holes with an error rate of less than 0.27% when the Average Localization Error (ALE) is within 5m.

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