## 1 Introduction  
In 2007, the Visible Light Communication (VLC) standards CP-1221 (VLC system) and CP-1222 (Visible Light ID system) were established by the Japan Electronics and Information Technology Industries Association (JEITA). CP-1222 is a standard concerned with the field of visible light positioning (VLP) [1]. In addition to the standards that came from Japan, the IEEE Standards Association has published IEEE 802.15.7: Visible Light Communication: Modulation Schemes and Dimming Support [2], and IEEE 802.15.7-2018--IEEE Draft Standard for Local and Metropolitan Area Networks--Part 15.7: Short-Range Optical Wireless Communications [3]. Standardization in VLC may be the catalyst for technological innovation and product commercialization in the near future. VLC-based indoor positioning provides opportunities to develop highly reliable, robust, and inexpensive positioning technologies [4]. Philips, one of the largest electronics companies in the world, has achieved some initial success in this field. Their solution is to embed Light Emitting Diode (LED) luminaires with VLC technology, and utilize the store lighting to provide location data using the store's mobile app [5]. Additional VLP applications, such as mobile robots, assistive devices for patients with impaired vision or other handicaps are being studied [6; 7; 8]. The Global Positioning System (GPS) is the perfect choice for outdoor applications because of its coverage and cost [9]. For indoor positioning, there are several possible options, includingWiFi [10; 11], Zigbee-based internet of things (IoT) [12], Bluetooth [13], radio frequency identification (RFID) [14], and camera-based solutions [15]. Depending on many factors, each option achieves a different level of accuracy, but the applied algorithms can be considered a fundamental factor. In this article, we examine some of the indoor positioning studies utilizing machine learning (ML) algorithms, which play a key role in our proposed solution. First, indoor WiFi localization is always the preferred choice, because of the universality of WiFi signals and the availability of many wearable devices with WiFi signal receivers [10]. [PERSON] et al. [11] proposed a novel hybrid indoor WiFi localization that combined soft clustering with the random decision forest algorithm. [PERSON] et al. [12] used another ML approach that was applied to the ZigBee-based IoT network. These authors investigated a narrow-band indoor positioning system (IPS) fusing time and received signal strength via ensemble learning. Using a random forest regression model, their solution achieved a 36.1% improvement over the traditional method based on received signal strengths (RSS). For RFID technology, the application of ML to locate an object position in the indoor environment is also efficacious. In Reference [14], to overcome the limitation of the mutual dependence of positioning accuracy and the density of reference tags, an extreme ML algorithm was adopted. The results showed that their solution created a better performance than existing solutions. In addition to using device-based localization systems, there is also the option of using device-free localization (DFL) in a wireless sensor network field to locate a person. In this innovative approach, the application of ML to optimize positioning quality is an inevitable trend. The logistic regression classifier has been used to improve the localization accuracy of a fingerprint-based DFL in a changing environment [16], and an extreme ML algorithm with parameterized geometrical feature extraction for DFL is also suggested in Reference [17]. Several recent studies in the field of VLC-based IPS have applied traditional methods as well as improved ML algorithms for better positioning efficiency [18; 19; 20; 21]. [PERSON] et al. [18] proposed an indoor localization solution based on the fusion of multiple classifiers (grid-independent least squares and grid-dependent least squares).