# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 1 Introduction  
With the start of the Fourth Industrial Revolution around the world, Internet of Things (IoT), artificial intelligence (AI), and big data are attracting attention as major technologies. Most people these days own a smartphone, which is an IoT device. In addition, a large volume of data can be stored and used through big data technology. These two technologies of IoT and big data can be combined with AI to increase efficiency in the navigation field. It is very important for navigation technology to estimate the initial location of the user to perform accurate route guidance. If the user's initial location cannot be accurately positioned, the user is guided to an inefficient path. The global positioning system (GPS) technology currently used in outdoor environments has reliable positioning accuracy [1]. However, GPS has a limitation in performing indoor positioning due to a signal loss problem caused by obstacles and walls existing in indoor environments [2, 3, 4]. Hence, various positioning technologies are developed for indoor office environments [5]. Such indoor positioning technology is generally based on two types of communication technology and positioning algorithm. Mobile communication technologies are wireless-fidelity (Wi-Fi) [6], ultra-wide band (UWB) [7], and Bluetooth [8]. Fingerprinting, triangulation, and particle swarm optimization (PSO) techniques can be used as location positioning techniques [9]. These existing wireless sensor positioning algorithms can be divided into range-based algorithms and range-free algorithms. The former are applied more widely for convenience in engineering practice. Typical range-based technologies include received signal strength indicator (RSSI), angle of arrival (AoA) [10], time of arrival (ToA) [11], time difference of arrival (TDoA) [12], etc. However, in an indoor environment, the signal path is short and due to the complexity of the radio wave environment, it is difficult to achieve an accurate measurement distance with ToA, TDoA, and AoA, causing a problem in obtaining an accurate location. In contrast, RSSI can obtain relatively high positioning accuracy at a low cost by combining it with a fingerprinting scheme, which are generally applied for user positioning in an indoor environment [13]. Therefore, in this work, a study was conducted to improve the performance of the PSO algorithm to increase the indoor positioning accuracy based on Wi-Fi in the indoor office environment suggested by the Third Generation Partnership Project (3 GPP) [14]. First, the database is built based on RSSI by performing the fingerprinting scheme in the offline step. As the indoor environment expands, these fingerprinting databases can be stored through big data. Thereafter, sample points (SPs) adjacent to the user can be derived through a fuzzy matching algorithm in the online step, and a region can be limited based on the derived SPs. Distributed particles within a limited region are then subjected to the PSO. The PSO locates the user by performing optimizations based on intelligent particles. There are two advantages to limiting the initial search region of the PSO. First, the probability of reaching the global optimum is improved in the PSO optimization process. Second, the processing time for target accuracy is reduced. Based on these two advantages, the proposed scheme can achieve fast processing time and high accuracy when positioning the user's location. The main contributions of this paper are described as follows:  
1. We propose an improved PSO method for positioning the user's location, based on a new method for limiting the initial search region of the PSO. 2. It uses a single algorithm (fingerprinting, fuzzy matching) to find the region where the user is most likely to exist and limits that region to the initial search region of the PSO. It then locates the user by deploying intelligent particles within a limited region. 3.