**4. Conclusion**

(Wi-Fi is a trademark of the Wi-Fi Alliance), is a wireless network of devices that uses high frequency radio signal (2.4 GHz in ISM band) to transmit and receive data within a limited area. As the connection between nodes of the network maintains continuity, the communication is preserved even if one device is moving around in the limited area (50–100 m) [37]. This means that for these reasons, the WLAN technology could be used to estimate the location of a mobile device within this network. The positioning accuracy required to offer satisfactory LBSs is in the order of 1 m and a great effort is needed in R&D. The expansion of this field of research is expected to continue for years, beside numerous commercial applications, due to the fact that it is a low cost solution providing proper connectivity and high speed links. In fact, nowadays, the WLAN infrastructure is widespread in many indoor environments and it

Usually, an indoor environment is often complex, characterized by nonline-of-sight (NLOS) of target objects; in these situations, WLAN positioning technologies could be very helpful because they do not require the line of sight. Unfortunately, compared to IRBL procedure, WLAN positioning is affected by a large estimation error, proportional to the number, and position of nodes in the network. Others challenging issues are the power consumption and the signal attenuation. Pros and cons of WLAN positioning are true in function of the techniques of positioning used. The most popular WLAN positioning method is based on the received signal straight indicator (RSSI) because it is easy to extract from any connected device in a Wi-Fi network [17]. The RSSI method is based on the received signal power and on the relation between the signal attenuation and distance of the nodes. Knowing the strength of the emitted signal, the strength of the received signal, is possible to calculate its attenuation and consequently the distance between the emitter and the receiver. With these techniques, it is possible to combine different strategies for positioning, like propagation modeling, fingerprinting, cell of origin, and multilateration [30]. To obtain a most precise localization, it is necessary to combine the technique of fingerprinting [37] that consists an *a priori* analysis to map the observed signal strength of fixed routers in every place of the indoor environment. With this data it is possible to generate a database (i.e., a radio map). The limitation of this method is the necessity of *a priori* information, an effort that means an increased workload and a well-spread router network. The propagation model differs from the fingerprinting model because it tries to determinate the RSSI map analytically instead of empirically. Of course, the major issues are related with the right description and modeling of the environmental effects (moving objects, signal attenuation, multipath) [5].

Another way to locate a device in a Wi-Fi positioning system is the cell of origin (CoO) method, with which the receiver position is made to coincide with the coordinate of the access point (AP) generating the highest RSSI value. Due to the spatial distribution of the APs in an indoor environment, this type of techniques is able to reach location with errors around 10–20 m [14]. Finally, multilateration methods, like time of arrival, time difference of arrival, angle of arrival, and so forth, are less common for WLAN positioning due to computational complex-

A literature review on WLAN systems for indoor positioning has been published by He et al. in 2016 [18]. There are many previous research studies on indoor Wi-Fi localization that pursue different goals and use different methods and technologies also in the function of the field of interest of the research groups. In particular, besides the numerous interesting

ity of these kinds of measurements in mobile devices [26].

is already standardize for commercial smartphone communication.

178 Smartphones from an Applied Research Perspective

In this chapter, the authors have tried to describe the positioning performances and methodologies in outdoor and indoor scenarios considering smartphone technology. In particular, the state-of-the-art of smartphone technology regarding the precisions and accuracies that can be achieved with these instruments for positioning and navigation purposes has been analyzed, in both scenarios.

Even if in outdoor scenarios the obtainable accuracy is less than 5 m under open-sky conditions considering only the GNSS sensors, in indoor environment is possible to have accuracies of 10–50 cm if some sensors, such as INS, cameras, or Wi-Fi technology, are considered. Interesting results are obtained fusing IRB technique with MEMS technology: considering an interval of 2 s between images, the mean planimetric error is about 61 cm at 67% and 1.49 m at 95% of reliability. A possible interesting alternative for indoor positioning could be represented by the fusion of range camera and INS instruments.

This is not an exhaustive overview, also because the technology is evolving more quickly than the minds that are producing them: so this would be a starting point for future works regarding these instruments for positioning and mapping application.
