# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
## 6 Results  
This section presents the main differences between the previously proposed methodologies (i.e., GA and MA) and the results obtained for the MA-VND-Chains presented in this paper. These results show that the MA-VND-Chains improves the overall performance of the TDOA architecture and reduces the Root Mean Squared Error (RMSE) of the sensor distribution even more than our previous MA [28]. Table 1 shows the parameters used for the positioning signal link and for the architecture sensors characteristics based on real LPS applications. Figure 2 in Section 3 shows the simulation scenario. The parameters that compose the scenario analyzed are detailed in Table 2.  
Improving the GA with the MA increases the complexity of the design but reduces some of its limitations. The GA is inspired by the parameters of biological evolution (i.e., mutation, crossover, and selection) to perform an intelligent search in the space of solutions of the NLP. These genetic operators are blind and do not provide any knowledge about the problem domain. The MA introduces knowledge of the problem domain to discover new individuals. The LS analyzes the areas discovered in the exploration phase of the GA in search for better individuals through an LS procedure. The balance achieved between diversification and intensification ensures that the MA is more robust to premature convergence. Thus, the RMSE of the TDOA architecture in the covered region by the LPS in the scenario of simulations is lower for the MA than for the GA as shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Magnitude** & **Measure Unit** \\ \hline \multirow{4}{*}{Link} & Frequency of emission & 5.465 & GHz \\  & Bandwidth & 100 & MHz \\  & Transmission power & 1 & W \\  & Mean noise power & \(-\)94 & dBm \\  & Line-of-Sight (LOS) path loss exponent & 2.1 1 & \\  & Non-LOS (NLOS) path loss exponent & 4.1 2 & \\ \hline \multirow{4}{*}{Sensor} & Receptor sensibility & \(-\)90 & dBm \\  & Clock frequency & 1 & GHz \\ \cline{1-1}  & Frequency-drift & U\(\left(-15,15\right]\) & ppm \\ \cline{1-1}  & Time-frequency product & 1 & \\ \hline \end{tabular} \({}^{12}\) Path-loss exponents used in the Log-Normal model.  
\end{table} 
Table 1: Values used for the configuration of the distributions [74; 75; 76]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Magnitude** \\ \hline \multirow{3}{*}{Points analyzed} & TLE & 1471 \\  & NLE & 24,000 \\ \cline{1-1}  & RMSE\({}_{nsf}\) & 300 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for the urban scenario configuration. The randomness in each iteration favoring the shaking and the acceleration in the descent phase of the MA-VND-Chains algorithm with respect to the MA, allows the faster convergence to the local maximum achieved in the LS and the analysis of more individuals. Table 3 shows the values assumed by these metaheuristic methods. The probability of the LS for improving an individual is increased by the intensification parameter which quantifies the need of an individual to be exploited. These measures are reflected in the results obtained by the MA-VND-Chains that reduces the RMSE of the sensor distribution, thus achieving more competitive results. Figure 5 represents the RMSE obtained at each point of the TLE scenario for the MA-VND-Chains distribution. The simulations have been executed with an Intel Core i7 2.4 GHz CPU and 16 GB of RAM and the algorithm has been coded in the MATLAB platform. Figure 6 shows the comparison between the RMSE obtained by the GA, which has not reached an efficient coverage on the scenario, and from the MA, which allows optimizing the sensor distributions by relocating them in adjacent positions to improve coverage in the disadvantaged areas. The analysis scenario presents mainly three areas with lower accuracy in the coverage zone of the LPS. The areas coincide with the bordering of the space at study.