# Factorial Design Analysis for Localization Algorithms
## 2 Related Work
### Evaluation Scenarios Reported
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Localization** & **Type** & **Description** & **Performance Metrics** & **Number of Hops** & **Simulation Environment** \\ \hline \multirow{2}{*}{CL} & \multirow{2}{*}{range-free} & This method calculates the position of the NOI as the centroid of the RNs. & MSE, CDF & One-Hop & Indoors \\ \hline \multirow{2}{*}{WCL} & \multirow{2}{*}{range-free} & Based on the CL algorithm considering a vector of weights, where these weights depend on the distance separating the NOI and the RNs. & MSE, CDF & One-Hop & Indoors \\ \hline \multirow{2}{*}{RWL} & \multirow{2}{*}{range-free} & Based on the WCL algorithm, where the vector of weights depends on a linear relation of the RSS & MSE, CDF & One-Hop & Indoors \\  & & between the NOI and the RNs. & & & Outdoors \\ \hline \multirow{2}{*}{REWL} & \multirow{2}{*}{range-free} & Based on the WCL algorithm, where the vector of weights depends on an exponential relation of the RSS between the NOI and the RNs. & MSE, CDF & One-Hop & Indoors \\ \hline \multirow{2}{*}{DV-Hop} & \multirow{2}{*}{range-free} & This method calculates the average distance of a hop on the network, to estimate the distance separating the NOI from the RNs. & MSE, CDF & Multi-Hop & Outdoors \\ \hline \multirow{2}{*}{IDV-Hop} & \multirow{2}{*}{range-free} & This technique takes the average of all the average distances per hop, to diminish the variance of the average distance per hop. & MSE, CDF & Multi-Hop & Outdoors \\ \hline \multirow{2}{*}{WDV-Hop} & \multirow{2}{*}{range-free} & This technique considers the average distance per hop calculated by the IDV-Hop algorithm adding a compensation factor. & MSE, CDF & Multi-Hop & Outdoors \\ \hline \multirow{2}{*}{PSO} & \multirow{2}{*}{range-free} & This localization algorithm calculates the position of the NOI as the overall optimum using an iterative process that minimizes the cost function. & MSE, CDF & Multi-Hop & Outdoors \\ \hline \multirow{2}{*}{Hyperbolic} & \multirow{2}{*}{range-based} & This method converts the problem of non-linear localization into a linear problem using a least-squares estimator. & MSE, CDF & One-Hop & Indoors \\ \hline \multirow{2}{*}{Weighted Hyperbolic} & \multirow{2}{*}{range-based} & This method converts the problem of non-linear localization into a linear problem using a weighted least-squares estimator. & MSE, CDF & One-Hop & Indoors \\ \hline \multirow{2}{*}{Circular} & \multirow{2}{*}{range-based} & This method calculates the position of the NOI by iteratively using the descending gradient method & MSE, CDF & One-Hop & Indoors \\  & & until the cost function is minimized. & & Multi-Hop & Outdoors \\ \hline \multirow{2}{*}{WLS multilateration} & \multirow{2}{*}{range-based} & This method converts the problem of non-linear localization into a linear problem using a weighted minimum-squares optimization algorithm. & MSE, CDF & One-Hop & Indoors \\ \hline \multirow{2}{*}{Vertex-Projection} & \multirow{2}{*}{range-based} & Localization technique of low computational complexity based on a pyramid-shaped structure. & RMSE & One-Hop & Outdoors \\ \hline \multirow{2}{*}{Vertex-Projection} & \multirow{2}{*}{range-based} & Considers a correction factor in the distance separating the NOI from the RNs, which diminishes the localization error. & RMSE & One-Hop & Outdoors \\ \hline \multirow{2}{*}{Maximum-likelihood} & \multirow{2}{*}{range-based} & This method uses the maximum-likelihood principle to estimate the distance separating the NOI from the RNs as a function of the number of hops. & RMSE & One-Hop & Outdoors \\ \hline \multirow{2}{*}{LSDV-Hop} & \multirow{2}{*}{range-based} & This method improves the precision of the NOI localization by extracting a minimum-squares transformation vector between the real and estimated position of the randomly selected RNs. & ALE & Multi-Hop & Outdoors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of the localization techniques. been used to evaluate localization technique performance.