# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 1 Introduction  
The Internet of things is a promising technology that has already been shown to be useful in sectors such as health, environmental monitoring, transportation systems, and in other commercial applications. The internet, things, and the semantic viewpoint are the three major components of IoT, all of which have significantly enhanced the communication network. The Internet of things is a network of sensors, software, and hardware components that uses shared storage and the internet to gather, store, and manage data. Smart towns that want to utilize more public space may leverage the Internet of things in order to do so. Hospitals, retail malls, libraries, transit systems, and other public institutions may all benefit from using the Internet of things to proactively manage and improve services [1]. Indoor localization can be realized through various IoT technologies, including WiFi, RFID, UWB, and BLE. Each of these technologies offers unique benefits and faces certain limitations. BLE has emerged as a preferred choice for indoor positioning due to its low energy consumption, relatively low deployment cost, and compatibility with most modern devices, including smartphones. This advantage makes BLE highly suitable for real-time localization applications, unlike UWB, which, although more accurate, requires specialized hardware and higher costs [2, 3]. Furthermore, BLE provides a favorable trade-off between range and accuracy, outperforming WiFi in environments with significant interference. Comparative studies such as MLTL, IDWPSOInLoc, and EDEEPRFID-IPS have demonstrated BLE's superior performance in terms of scalability and ease of deployment, making it an efficient choice for various IoT applications [4, 5, 6]. To address the challenges of indoor localization, our research introduces two novel contributions: the Positive Weighted Centroid Localization (PWCL) algorithm and theHYBRID-MAPPED method. These approaches are designed to enhance localization accuracy by prioritizing stronger signals and integrating multiple filtering techniques, respectively. This study aims to provide robust solutions to problems surrounding real-world applications by distinguishing our methods from existing studies. We acknowledge that the original contribution of our work could be further emphasized. To address this, we have revised our approach to clearly highlight the development of the PWCL algorithm and the HYBRID-MAPPED method, which provide significant advancements over existing localization techniques. In this study, we aim to enhance indoor localization accuracy by leveraging IoT devices, specifically using Bluetooth Low Energy (BLE) combined with advanced fingerprinting methods. The novel contribution of our work lies in the development of the Positive Weighted Centroid Localization (PWCL) algorithm, which prioritizes stronger signals to improve localization accuracy. Additionally, we propose the HYBRID-MAPPED method that integrates multiple filtering techniques, including outlier detection and mapping filters, to further reduce localization errors in challenging indoor environments. These methods distinguish our work from existing studies in this domain. Various technologies have been investigated for indoor positioning, including Bluetooth Low Energy (BLE) [1; 7; 8; 9; 10]. In addition to BLE-based methods, the SLAM (Simultaneous Localization and Mapping) approach has been widely used in robotics for simultaneous mapping and localization, and it is highly relevant to indoor localization [11]. Furthermore, while GPS/GNSS technologies are often limited in indoor environments, their integration in hybrid localization systems provides valuable context for the development of more comprehensive solutions [12]. Recent advancements in indoor localization have introduced unified frameworks that integrate multiple localization techniques to improve accuracy and reliability. For example, the work of [PERSON] et al.