## 3 Perception of the User in the Environment: Methodologies and Technologies for Localization
### Methodologies for Indoor Localization  
Indoor localization, meaning the process to retrieve a user or device localization in an indoor setting or environment, has been boosted in recent years by smartphones and wireless devices. Indoor device localization has been used, starting from a few decades in industrial settings and robotics, however, only less than a decade the possibility to track a user enabled a wide range of applications and services. Also, the Internet of Things (IoT) approach based on the connection of end-to-end billion devices, starting from long-range technologies, has been extended to short-range localization even if in cooperation with different communication interfaces [1]. In general, indoor localization requires a finer granularity compared to outdoor localization meaning a more significant information on a smaller area; this is the reason why it encompasses different localization techniques, technologies and systems. Three different localization approaches can be recognized. In the Device Based Localization (DBL), the user position is retrieved based on the reference nodes ; it is applied where assistance in navigating around space is required by the user. Differently, in the Monitor Based Localization (MBL), the reference nodes passively obtain the user's position; the user is often tracked to provide services. Finally, proximity detection aims to estimate a distance between the user and a Point of Interest (PoI). Proximity detection is already used in a shopping mall, as an example, where the user can be informed on promotions based on its position. It is also of interest since in a museum or in the context of the fruition of cultural goods, a series of additional multimedia information can be enabled for the user when approaching an object [1]. As concerns the indoor localization techniques, one of the most straightforward and widely adopted approach is the received signal strength (RSS). It exploits the power of the signal at the receiver to estimate the distance between the receiver and the transmitter. The DBL requires more reference points; then trigonometry is applied to calculate the distance. In the MBL case, the strength of the signal at a reference point is used for calculating the position of the user. RSS proximity services can use a single reference node to build a geofence to estimate the proximity. The RSS is simple and cheap, but it can exhibit poor accuracy due to signal attenuation and noise; it can be improved by implementing more complex algorithms [15; 16]. The Channel State Information (CSI) techniques composed of the Channel Impulse Response (CIR) and of the Channel Frequency response (CFR) are conceptually similar to RSS but show a finer granularity being based on the detection of both amplitude and phase of the signal. Many cheap network interface controllers (NIC) IEEE 802.11 NICs cards can provide more information resulting in stable measurements and higher localization accuracy [15]. In Scene Analysis techniques, measurements are taken offline (using RSS or CSI), then they are compared to the online ones. The comparison is performed by probabilistic methods, artificial neural networks, k-nearest neighbor or support vector machines [17]. The Angle of Arrival (AoA) method adopts antennas array and calculates the time difference at the individual antenna. In general, this method is quite accurate but in comparison with RSS, it requires more complex hardware and precise calibration [18]. The Time of Flight (ToF) or Time of Arrival (ToA) uses the propagation time of the signal to compute the distance between the receiver and the transmitter; it is suitable for both MBL and DBL scenarios. On the other hand, a strict synchronization is required, and estimation accuracy depends on the sampling rate and signal bandwidth. It should be underlined that in case of unavailability of a sight path, direct line localization error remains [19].