# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 3 NGeneApp: An Android-Based Real Time RFI Detector for Smartphones
### The Crowdsourcing Approach of the Server  
For saving bandwidth and further analysis in case of interference, the information sent to server is classified into two types of message. The first type of message, which is regularly sent to server, contains the following information:  
* The PVT results computed by NGeneApp receiver
* The PSD estimation and the total energy of error value
* The correlation distribution of the Chi-square GoF test  
The second type of message will be sent when the interference is detected consist of the following information:  
* 30 s of IF digitalized samples (raw data)
* The output of the tracking stage (i.e., correlators value, Doppler frequency, code rate)
* C/N\({}_{0}\) measurements  
By using the PVT results and the C/N\({}_{0}\) from the crowd-sourced, the possibility of detecting and localizing the source of the jammer was demonstrated in [17; 51]. In those studies, the information (i.e., C/N\({}_{0}\)) is extracted from the GNSS receiver embedded in smartphone. However, the C/N\({}_{0}\) value may be affected by other factors, such as multipath and partial blockage. Therefore, when the interference is detected, NGeneApp sends also the spectrum and raw data which enable the server to identify the source of jamming in a more accurate and flexible way [9]. An analysis of the advantages and drawbacks of the proposed crowdsourcing approach versus other similar works is reported in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Front-End** & **Sampling Frequency (MHz)** & **Intermediate Frequency (MHz)** & **Bandwidth (MHz)** \\ \hline SiGE v2 [50] & 16.3676 & 4.1304 & 2.5 \\ SiGE v3 [48] & 16.368 & 4.092 & 2.5 \\ NSL STEREO [49] & 16.0 & 3.905 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FEs supported by NGeneApp.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Other Works [17; 51]** & **Proposed Crowdsourcing** \\ \hline \multirow{4}{*}{Device} & Using embedded GNSS chipset in & \(\bigstar\) & Android smartphones \\  & Android smartphones & \(\bigstar\) & Front-end (SiGE v2/SiGE v3) \\  & & \(\bigstar\) & Patch antenna \\ \hline \multirow{4}{*}{Data provided} & GNSS information available from the & All available information can be gathered from the \\  & Android OS: & GNSS receiver: \\ \cline{1-1}  & \(\bullet\) & PVT results & \(\bigstar\) \\ \cline{1-1}  & \(\bullet\) & Satellite information & \(\bigstar\) \\ \cline{1-1}  & \(\bullet\) & C/N\({}_{0}\) & \(\bigstar\) \\ \cline{1-1}  & \(\bullet\) & Raw measurements such as & \\ \cline{1-1}  & pseudorange, Doppler frequency, code & \(\bigstar\) & Raw measurements such as pseudorange, Doppler \\ \cline{1-1}  & rate (if supported) & \(\bigstar\) & frequency, code rate \\ \hline \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{AGC values} \\  & & \(\bigstar\) & The output of the tracking stage (i.e., correlators value) \\ \cline{1-1}  & & \(\bigstar\) & PSD estimation \\ \cline{1-1}  & & \(\bigstar\) & Correlation distribution of the Chi-square GoF test) \\ \cline{1-1}  & & \(\bigstar\) & If digitalized samples \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between similar works and the proposed crowdsourcing approach.  
The proposed server can also characterize the interference by using the spectrum data. Moreover, the raw data sent simultaneous from multi-source is also valuable for further investigation. With this IF samples database, the interference event can be analyzed and replicated in post-processing investigation. Hence, we can evaluate the effect of interference on the receiver operation and assess the performance of receiver under harsh environment [54; 55].