# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 1 Introduction
presents a unified framework using factor graphs to optimize indoor localization performance by integrating various data sources [13]. Additionally, [PERSON] et al. have explored the performance improvements achieved through cooperative fingerprint-based localization, highlighting the importance of cooperation in enhancing accuracy [14]. Our study builds upon these foundational works while introducing novel methods to further advance the field. As they pertain to smart retail complexes like hyperstores, the findings may be investigated further. Imagine having a lengthy shopping list in your hand and entering a shopping center with thousands of things to choose from, and you have to spend hours finishing your list. The presence of an internal location system based on the Internet of things in this instance may show the position of each of the items you desire on your smartphone. Prioritizing the purchase of any goods will undoubtedly result in significant time savings. As a result, based on its current and historical data, this store's smart technology can design a time-based shopping path for you and lead you. Internal positioning systems are also essential for asset monitoring and inventory management applications. Visual-inertial navigation systems have offered dependable methods for indoor and outdoor settings [15], and these systems may substantially improve the efficiency of system responders. VINSs, however, often depend on appropriate illumination and streams of information, while the usage of cameras may create privacy issues [16] that limit their use. Internal positioning systems based on conventional wireless communication technologies have also been investigated as a supplementary option. Environmental obstacles significantly affect radio signals in interior settings, reducing the system's accuracy [17]. The position of a GPS device is always restricted. When the weather is poor, GPS signals cannot be received inside big buildings or in fully enclosed areas such as roads and city tunnels. Unfortunately, the absence of a satellite signal may have a variety of severe consequences in each of these situations. Imagine trying to locate people or objects among structures destroyed during an earthquake or natural disaster, or in a large hospital, showing patients the correct path to their desired location based on GPS. While GPS can easily locate people or objects in open spaces, it cannot send a signal and displays a large vacuum in indoor environments [8]. Because wireless local area networks and Bluetooth Low Energy are so common nowadays, the emphasis of this article is on how to use the Radio Signal Strength Indicator that is accessible through wireless local area networks and Bluetooth Low Energy. The location of transmitters is addressed first in this article, followed by point finding using the fingerprint technique. A thorough examination of the algorithms used in comparable articles also aids in the development of a unique technique for internal filing by combining various algorithms and applying different filters to the final output. Because receiving satellite signals in big interior settings is constantly restricted, and people expressly need a different system in these locations, the most essential objective of this research is to identify things in these surroundings. Another aim of this study is to improve the process accuracy in locating and presenting the position of items by taking ambient noise into account. In this article, we ask the following questions:  
How can we locate indoors without the need for GPS using IoT? How can we maximize productivity by locating with a minimum number of transmitters? How can we neutralize the effect of environmental noise with existing algorithms and improve the accuracy and speed of receiver spatial calculations? To answer the above questions, we consider and test the following hypotheses:  
1.