Node Localization in Range-Free 3D-WSNs Using New DV-Hop Algorithm Based Machine Learning Techniques

Abstract

In many Wireless Sensor Network (WSN) applications, location is critical. Another intriguing aspect of the acquired data is the ability to obtain exact information about sensors' locations. In order to localize multi-hop WSNs, based connectivity algorithms use their benefits, such as simplicity and acceptable accuracy, to do so. However, the localization accuracy may be limited due to the two- or three-dimensional environment (2D or 3D). Range-Free 3D-WSNs can benefit from an analytic model for hop-size quantization and an Extreme Learning Machine (ELM) method for localization to reduce localization errors. The additional third dimension greatly affects the accuracy of localization. Since many applications require 3D localization, it is important to develop efficient self-localization algorithms for 3D WSNs. In this paper, for a uniform distribution of sensor nodes, a new probabilistic quantization of hop size in 3D WSNs is proposed. Moreover, the extreme learning machine (ELM) which represents a new approach to WSN localization is exploited combining a conventional method (probabilistic approach) with a non-conventional method (Machine Learning). For a variety of conditions, our algorithms have been tested through simulation in isotropic settings. The performance of the localization model was assessed using the average localization error (LE). When compared to previous 3D-DV-Hop heuristics, the suggested localization algorithm's performance in terms of accuracy is clearly demonstrated by simulation data. With the help of the predicted hop quantization for hop-size estimation and the ELM was used for position estimation, our localization method for 3D-WSNs lowers the average localization error of nodes and has a greater location accuracy compared to its rivals.