# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 1 Introduction  
In recent years, Internet-of-Things (IoT) technology, an extension of the Internet that envisions connecting all devices to the Internet for communications, is developing rapidly and is expected to radically transform education, healthcare, smart home, manufacturing, commerce, and transportation, etc. It is essential for transforming the world into a smart world, wherein localization of devices or terminals is an indispensable aspect [1; 2; 3]. Although the global positioning system (GPS) satisfies the wide requirements of outdoor scenes, it performs poorly and has very limited usage in indoor scenes [4]. There exist numerous requirements and challenges in the development of indoor localization. According to the signal source, indoor-localization technologies can be divided into external and natural signal sources. External signal sources mainly include Wi-Fi [5], Bluetooth [6], ultra-wide band (UWB) [7], visible light [8], ZigBee [9], computer vision [10], and radio-frequency identification (RFID) [11]. By contrast, indoor localization technology based on natural signal sources primarily relies on the sensors of terminal devices to achieve localization, including inertial measurement units (IMU) [12] and geomagnetics [13], etc. The list of these technologies can be extended as technology develops. For example, Long Range (LoRa), originally developed for long-range communication with a high link budget, can also be employed for indoor localization [14]. Among these, UWB-based indoor-localization technology offers the advantages of high accuracy and simple localization methods; however, it relies on additional deployment devices and incurs a high cost [15]. A localization system based on vision utilizes high-precision computer vision technology, but it can only spread within the line of sight and requires high hardware cost and complex computation [10]. The main principle of RFID localization [16] is to perform non-contact communication transmission using the spatial coupling characteristics of the radio frequency. Passive RFID equipment is cheap but has a small transmission range, whereas active signals have wide coveragebut high hardware costs. However, its localization accuracy is inadequate. The IMU localization system uses an accelerometer, gyroscope, magnetometer, and other sensors of the terminal equipment to perform navigation calculations; however, the localization accuracy is limited by hardware devices and inevitably produces cumulative errors [17], which require continuous calibration with external information. For the LoRa, the received signal strength (RSS) distance method, an RSS-based logarithmic path loss model, could be adopted for indoor localization. RSS values are used to calculate the location of an object according to the principle of trilateration [14]. Further, Wi-Fi, ZigBee, and Bluetooth are wireless-sensor-network technologies based on IEEE 802 standards, featuring low power consumption and low cost [18]. The ranging principle is mainly based on geometric constraints and signal-strength feature matching. Zigbee-based localization measures the distance between the unknown and reference points in advance, and the signal has a low transmission rate and a short transmission distance. Moreover, Bluetooth and Wi-Fi are supported in most terminal devices, but the range of Bluetooth signal communication is limited, and the localization accuracy is inadequate, with a large time delay. In contrast, Wi-Fi signal transmission rate is fast, its localization range is wide, and equipment deployment is easy. In the field of indoor localization, Wi-Fi fingerprint-based localization is a current mainstream method [15]. However, it is limited by the volatility of Wi-Fi signal, which makes offline data not reliable enough, and it is difficult to achieve stable high-accuracy localization.