The availability of a reliable and accurate indoor positioning system (IPS) for emergency responders during on-duty missions is regarded as an essential tool to improve situational awareness of both the emergency responders and the incident commander. This tool would facilitate the mission planning, coordination, and accomplishment, as well as decrease the number of on-duty deaths. Due to the absence of global positioning system signal in indoor environments, many other signals and sensors have been proposed for indoor usage. However, the challenging scenarios faced by emergency responders imply explicit restrictions and requirements on the design of an IPS, making the use of some technologies, techniques, and methods inadequate on these scenarios. This survey identifies the specific requirements of an IPS for emergency responders and provides a tutorial coverage of the localization techniques and methods, highlighting the pros and cons of their use. Then, the existing IPSs specifically developed for emergency scenarios are reviewed and compared with a focus on the design choices, requirements, and additional features. By doing so, an overview of current IPS schemes as well as their performance is given. Finally, we discuss the main issues of the existing IPSs and some future directions.

Wireless Sensor Networks are an emerging technology which has been recently adopted in many applications. Due to its wireless nature, the analysis of the radio propagation models plays an important role for performance evaluation in both theoretical and practical aspects. In this regards, path loss exponent is one of the most important parameter which has been considered widely in wireless communications analysis. There are several theoretical evaluations of path loss exponent for wireless sensor networks available in the literature. However there is a lack of experimental evaluation of both path loss exponent and the effect of shadowing. In this paper, three environments (free space, in building and industrial), where wireless sensor nodes are widely deployed, have been chosen in order to evaluate the experimental analysis. Path loss and path loss exponent are measured by means of Received Signal Strength Indicator (RSSI) and based on them, the standard deviation of shadowing effect is also calculated. All the measured parameters are compared with the theoretical analysis available in the literatures.

Ultra-wideband (UWB) is a popular technology for indoor positioning systems (IPSs) due to its robust signaling in harsh environments, through-wall propagation, and high-resolution ranging. However, the propagation of wireless signals in indoor environments is affected by nonline-of-sight (NLOS) conditions, which can lead to positively biased distance estimates. Several research works were carried out in this area, but the NLOS effect caused by the human body shadowing on the ranging performance is not properly addressed in the literature. In this paper, a commercial UWB transceiver is used to assess the impact of the human body shadowing on the ranging accuracy. A set of real-time features is combined with different machine learning algorithms for NLOS identification and mitigation. With a subset of four features, a 0.97 F1-score was obtained for NLOS identification. For NLOS mitigation, with a subset of three features, the residual error follows a Gaussian distribution, has a mean error close to zero, and an STD of 0.67 m.