## 6 Field Experiments
### Experimental Results  
Over 50 different field trials were conducted in both LOS and NLOS conditions to verify the efficiency of the proposed algorithm. The results of various trials are summarized in Table 2. We obtained an average of a 78% success rate (determined based on the resulting final position and the theoretical optimum position). An average of a 3.5-dBm improvement in RSS is obtained for a displacement of 1 m by the relay node. In the field experiments, the temporal performance is grounded based on the time taken by the algorithm instead of the number of iterations as used in the simulation trials. The obstacle avoidance system also had some impact on the performance of the algorithm, as it constrained the free control on the motion executed by the algorithm and, in some cases, redirected the robot in a completely opposite direction (inhibiting the directions provided by the algorithm). Therefore, only the results from the trials where the robot was not obstructed or blocked by obstacles are used in the performance analysis. A possible solution to overcome this issue is to use a sophisticated localization system, such as a simultaneous localization and mapping (SLAM) algorithm that provides the layout of the physical objects in the environment, using which the influence on the desired direction of RSO algorithm can be determined, and corrective actions can be taken. However, this is out of scope of this paper and is indeed a possible avenue for further research. It is important to note that we consider the theoretical optimum as the reference value (for calculating the success rate) in the field experiments, which need not be the actual global optimum due to the physical constraints mentioned earlier. Hence, the percentage of cases that have a zero success score does not necessarily imply that the algorithm failed; however, it could imply that the actual global optimum could be at the position where the relay node finally reached, for instance, because of NLOS influence. \begin{table}
\begin{tabular}{l l l} \hline  & **Mean Value** & **Standard Deviation** \\ \hline RSS improvement (dBm) & 16.7 & 6.6 \\ Success rate \% & 78 & 6.7 \\ Time taken (s) & 10.6 & 3.9 \\ Distance traveled (m) & 4.7 & 4.4 \\ \hline \end{tabular}
\end{table} 
Table 2: Summarized results of the field trials. Figure 16 presents two instances of paths taken by the relay node for two similar trials experimented at different times. This is to show that a given radio environment (field) does not change significantly with time because the path taken is similar. **Figure 16.** Path taken by the relay node in two different trials when the initial positions are almost the same. Server and client node are shown as blue dots. Red and green dots indicate initial and final relay node position, respectively. The theoretical optimum position is marked as *. In Figure 17, two examples of the RSS balancing (and improvement) behavior are shown. The error bars are used to present the RSS values from various sensors on the relay node. It can be observed from this figure that the RSS is balanced quickly within the first few iterations, and then, the algorithm maintains the balance in the RSS between server and client nodes. Figure 18 plots the change in the goodput values during an experiment trial where the end-to-end link goodput (throughput) is consistently improved, even though the success score was zero (the relay node was trapped in a obstructed area). This confirms that the algorithm is able to achieve its objective of maximizing the network capacity, though sometimes, it cannot reach the global optimum position. **Figure 17.** Change in the RSS values of server and client nodes at the relay node during the execution of the algorithm. As a fundamental advantage of our proposed multi-sensor method is the redundancy (discussed in the Section 4.5.2), this method can be useful in environments with ionizing radiations that can affect the electronics in the nodes.