## 2 Literature Review  
Indoor real-time locating systems (RTLS) have been gaining relevance due to the widespread advances of devices and technologies and the necessity of location-based services. The interest of the mobile industry to accelerate the adoption of indoor position solutions turned into the foundation of the InLocation Alliance (ILA (InLocation Alliance): \(\text{inlocationalliance.org}\), accessed on 19 December 2021). The goal of this alliance is to facilitate a rapid market adoption so that new business streams are opened up with context-aware applications in indoor environments. The ILA chose Wi-Fi and Bluetooth as their preferred technologies. Both proposed technologies require specialized apps on the mobile devices in order to produce satisfactory results [16]. A thorough and contemporary survey of the Indoor Positioning Systems (IPS) for IoT is presented in [17]; it presents indoor positioning concepts and a list of already used criteria that define IPS for IoT. [PERSON] et al. [16] provide a classification of Indoor Positioning Systems(IPS), basing the classification on a set of papers comparing different IPS approaches. This is a list of identified technologies: Infrared mobile reader, Infrared (IR), laser (passive), ultrasound passive, audible sound, magnetic, RFID mobile tag, RFID mobile reader, Wi-Fi, Bluetooth, ZigBee, UWB, tomographic technology (water resonance), camera infrastructure, cameras (portable), floor tiles, air pressure, inertial, ambient light, artificial light, indoor AGPS, cellular technology, TV, and FM. All the technologies that need any intervention from the user are out of the scope of this experiment, so all technologies based on wearables, which demand the installation of software on mobile devices or the users to act in a certain way, are out of the scope of the paper. All technologies based on audible and visible changes in the environment (such as the usage of fluorescent lighting) pose a distraction. Additionally, the use of video cameras and microphones presents a huge privacy concern and were thus eliminated from this study. Most IR systems require line-of-sight (LOS) clearance from the emitter to the sensor; in the context of IR IPS systems, the requirement of LOS clearance is a great disadvantage, as it suffers from no-detection areas, and the system performance is also affected by sunlight [18]. A metaheuristic for anomaly detection in IoT is proposed in [19], which is an extension of the work presented in [20]. The method is based on an activity footprints-based method to detect anomalies in IoT, but with small changes it can be used to track indoor activity. The technology that \"survived\" the criteria posed by the presented study was \"intelligent tiles\", usually using pressure sensors. There has been some research in the area of employing pressure sensors to track the users' indoor behaviors, ranging from person tracking and indoor localization to fall prediction. The Smart Floor project at Georgia Institute of Technology [21] and ORL Active Floor at The Olivetti and Oracle Research Laboratory [21] provide location and identification without encumbering the users, but their highest levels of precision will not be reached until the user steps on the exact centers of the floor tiles, which for a reliable measurement would require conscious attention. [PERSON] et al. [22] present a smart-sensored floor setting that draws energy to power the motion sensors from the integrated generators that are powered by normal floor activity such as walking or sport activity. [PERSON] et al. [23] present a cost-effective intelligent floor setting using pressure-sensing sensors that functionally competes with higher-cost systems. [PERSON] and [PERSON] [24] report on the development of a distributed sensing floor using an optical fiber sensor. However, all presented intelligent floor systems fail to properly address the privacy and data-sensitivity issues.