## 3 State-of-the-Art in Warning Systems Based on Radio Frequency Technologies  
The study of people's behavior in indoor and outdoor areas was addressed from three different perspectives, namely, using sensors [32; 33; 34; 35; 36], using image processing [37], or using historical data and applying predictive models [38]. In this section, a review of the works carried out to estimate people's behavior using sensors and wireless technologies is presented. One of the first works to derive the number of people from the imprint left on radio frequency (RF) signals was conducted in [39]. The proposal was based on the use of received signal strength (RSS) and a link quality indicator (LQI) to obtain an estimation of crowd density. They assumed that there was a linear relationship between the number of people between two nodes (transmitter and receiver of RF signals) and both the RSS amplitude (decreasing) and the RSS variance (increasing). They also assumed that the LQI replicated this behavior. Experimentally, they demonstrated that their hypothesis was true. Indoor tests were conducted in two scenarios, namely a Zigbee wireless sensor network and a WiFi 802.11b network. In the latter, only RSS was measured. As the authors indicated in their work, the accuracy of the system should be improved. In [40], an improvement to [39] was introduced. The new method was based on using successive signal cancellation in an iterative manner to count people in indoor scenarios. The procedure was as follows: first, the testing scenario was empty, and they measured the ambient RSS in this condition. Then, a person walked randomly, and they measured the RSS, so by subtracting the obtained measurement from the ambient RSS, the authors quantified the impact of one person on the RSS values. However, they were aware that the relationship between the number of people and the RSS variation was not linear. They therefore proposed a consecutive iterative cancellation method, selecting at each iteration the strongest signal to be subtracted from the RSS until no further presence was detected (i.e., sequential counting). The authors obtained good results, although limited to a very low number of people. The reason is that their proposal cannot yet deal with a severe multipath effect. Aiming to improve the accuracy of previous proposals and following the same device-free trend, two methods were proposed in [41] to estimate the number of people in an indoor environment. The two proposals were based on linear regression and vector regression using the RSS of WiFi signals as the physical descriptor. For the evaluation, they used dedicated wireless devices (Raspberry Pi). The suggested method was as follows: the wireless devices calculated the RSS of the signal received from the WiFi Access Point (AP). These values were then collected by a computer, which estimated the number of people in the room. For the estimation, linear regression or vector regression--non-linear regression based on support vector machine (SVM)--was employed. The coefficients of the regression formula were previously calculated with historical real data obtained from the experiments. One of the main findings of this work is the corroboration that there was no linear relationship between RSS variations and the number of people in the room. In [42], the effect of moving people on the received RSS was studied from two different perspectives: the blocking of the line of sight (LoS) and the multipath effect. Once characterized, they combined both to obtain the probability distribution of the received signal amplitude as a function of the number of people in the scene. Then, the Kullback-Leibler divergence was applied to match the experimental measurements with the previously assessed analytical expression. The parameter that minimized the divergence between both was interpreted as the resulting people estimation. One possible limitation of this work is that they first modeled the pedestrian movement from the experiments.