## 6 Experiments  
### Partial Experiments  
#### 6.1.1 Training-Based RSSI Localization  
Accuracy  
The experiments were carried out in the _CONET Integrated Testbed_[31], see Figure 6 (left). The testbed (_[[http://conet.us.es_](http://conet.us.es_)]([http://conet.us.es_](http://conet.us.es_))) is a remote open tool to assess and compare multi-robot and WSN methods. The testbed deployment includes a configurable static WSN with nodes hanging from the ceiling. Figure 6 (right) shows a typical setting. The testbed also includes five _Pioneer AT_ robots. Robots carrying mobile nodes are convenient tools in localization experiments. They have greater motion accuracy than humans and the same experiment can be performed hundreds of times with high repeatability. The testbed is equipped with components that provide the ground truth location of the robots. Each robot is physically connected to one WSN node--mobile node--through a serial line. A simple protocol allows robot-WSN bidirectional interchange of data, requests and commands. The testbed is installed in a 500 m\({}^{2}\) room in the School of Engineering of Seville. A set of 25 localization experiments was performed. In the training stage, each static node obtained its RSSI-range model. In the application stage the robots followed a pre-assigned trajectory while their onboard mobile node broadcasted _LocRequest_ packets. In response each mobile node received _LocResp_  
Figure 6: (**Left**) Picture of the _CONET_ testbed; (**Right**) Scheme of a typical WSN deployment in the testbed room. packets from surrounding static nodes and computed its current location. Each mobile node transmitted its computed location to its robot. The robot logged in a synchronized way the estimated location together with the ground truth location of the robot. Figure 7 shows the RSSI-range models obtained in the training stage by nodes 12 (solid line) and 24 (dashed). Differences between their radio-chip units, antenna orientations and their surroundings, among others, resulted in different RSSI-range models. Figure 8 shows the location in axes \(X\) (top) and \(Y\) (bottom) obtained in one experiment. The estimated location is represented in solid line and the ground truth is in dashed line. The mean localization error was 124.43 cm and its standard deviation was 68.09 cm. The mean errors in axes \(X\) and \(Y\) were 75.55 cm and 49.56 cm, respectively. These results depend on the setting. We repeated the experiments with settings (rhombus grids, random deployment) and the results were satisfactory as well. This experiment was also used to evaluate the impact of _RANSAC_ outlier rejection. In the training stage _RANSAC_ was substituted by the simple regression in Equations (2) and (3). In this case, the mean localization error was to 141.56 cm, 13% higher. As expected, the rejection of outliers improves the localization accuracy. The same experiment was repeated using LS instead of WCL. In this case, the mean localization error was 167.44 cm--34% higher than WCL--and the standard deviation was 103.84 cm. Although LS is mathematically optimal, its high sensitivity to noise in RSSI measurements degrades its performance in real situations. Robustness to Static Nodes Displacement  
In our approach, static nodes are deployed by firefighters. There can be errors between the planned locations and where the firefighter actually put the nodes. Misplacement errors affect the training and the application stages. This test analyzes the impact of misplacement of static nodes. We simulated the localization method using four anchor nodes. The \(X\) and \(Y\) coordinates of each anchor node were distorted with zero mean Gaussian noise with standard deviations in the range from 0 to 500 cm. In total 500 samples for each standard deviation were taken. Notice that with \(\sigma\) = 500 cm, the 99% of the noise samples are in the range [-14.5, 14.5] m. We believe that these misplacement errors are lower than those usually found in the real problem.