A Non-Uniform and Optimized Clustering Mechanism to Improve Network Lifetime in 5g-Based Indoor Localization

High-accuracy localizationis considered critical for resource management algorithms in modern applications such Internet of Things (IoT), smart factories, and autonomous vehicles. Indoor positioning presents a challenging process as the complexity of attaining higher accuracy is difficult within the indoor environments.This paper proposes a novel Non-Uniform and Optimised Clustering Mechanism (NUOCM) for indoor localization to manage complex datasets acquired from large-scale indoor radio systems. Using NUOCM-based communication, the research aims to improve the network lifetime and serves a reasonable number of User Equipments (UEs) or devices in Multiple Input Multiple Output (MIMO) systems. NUOCM generates clusters through a novel method that uses an ensemble of classifiers from non-uniform cluster layers in 5G-based indoor localization.The UEs or devices are divided into sub-datasets based on values of Reference Signal Received Power (RSRP). Base classifiers including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF)are trained on these non-uniform clusters across several layers to improve spatial diversity. Finally, the optimal number of layers and clusters are defined by a Firefly Algorithm (FA), which maximises for localization accuracy, spatial diversity. The experiments are evaluate the efficacy of the proposed NUOCM and the results show an increased localization accuracy and network lifetime than the traditional clustering techniques in MIMO systems.