# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 5 Experimental Verification and Results
### Framework Performance Analysis
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Column Index** & **Feature Meaning** & **Standard Deviation** \\ \hline
0–520 & Strength values for WAP001–WAP520. & Avg STD: 23.96 \\ \hline
521 & Longitude & – \\ \hline
522 & Latitude & – \\ \hline
523 & Floor heights within the building. & – \\ \hline
524 & ID used to identify the building. Measurements & – \\  & were taken in three different buildings. & – \\ \hline
525 & Internal ID numbers used to identify spaces (offices, corridors, classrooms) & – \\ \hline
526 & Location relative to space (1—inside, 2—outside & – \\  & in front of the door) & – \\ \hline
527 & User ID & – \\ \hline
528 & Android device identifier (phone ID) & – \\ \hline
529 & UNIX time at the time of capture & – \\ \hline \hline \end{tabular} _Note: “-” means the characteristics are categorical, discrete, or label-type variables for which statistical dispersion measures like standard deviation are not meaningful or interpretable in this context._  
\end{table} 
Table 2: Meaning and statistical characteristics of dataset fields. cues like barometric pressure or multi-floor signal variations. The mathematical formation is as follows:  
\[\text{FER}=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}\Big{(}\hat{f}_{i}\
eq f_{i}\Big{)} \tag{7}\]  
where \(f_{i}\) and \(\hat{f}_{i}\) denote the true and predicted floor labels, respectively. The mean signal ranging error represents the average Euclidean distance between estimated coordinates (2D or 3D) and ground-truth locations across all test samples, providing a holistic view of spatial accuracy. The mathematical formation is as follows:  
\[\text{MSRE}=\frac{1}{N}\sum_{i=1}^{N}\lVert\hat{\mathbf{p}}_{i}-\mathbf{p}_{i }\rVert_{2} \tag{8}\]  
where \(\mathbf{p}_{i}\) is the ground-truth position and \(\hat{\mathbf{p}}_{i}\) is the estimated position of the \(i\)-th sample. Lastly, the probability of a coordinate error within 10 m evaluates the system's reliability by calculating the percentage of samples with errors \(\leq\) 10 m. The mathematical formation is as follows:  
\[P_{10}=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}(\lVert\hat{\mathbf{p}}_{i}-\mathbf{ p}_{i}\rVert_{2}\leq 10) \tag{9}\]  
This metric indicates the proportion of samples for which the positioning error does not exceed 10 m. This indicator offers an intuitive and application-relevant perspective--rather than reporting only a mean signal ranging error, it directly reflects the likelihood that a user's estimated position falls within a practically usable zone, as required by typical venue navigation and location-based services. The choice of a 10 m threshold aligns with common benchmarks in public indoor positioning evaluations, such as the Microsoft Indoor Localization Competition [34], EVARILOS benchmarking framework [35], and ISO/IEC 18305 standard [36]. Together with the building-scale, floor-level, and absolute-error statistics discussed above, this metric provides a comprehensive and interpretable characterization of system performance across both spatial and application-specific dimensions.