# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Year** & \multicolumn{2}{c}{**Techniques**} & **Tool** & \multicolumn{2}{c}{**Advantages**} & **Limitations** \\ \hline \multirow{4}{*}{[11]} & \multirow{4}{*}{2021} & \(\bullet\) & Threshold-based and Machine & \multirow{4}{*}{NA} & \(\bullet\) & Self-updating capabilities & \(\bullet\) & Classifying cracks, bumps, and \\  & & Learning approaches & & \(\bullet\) & Hybrid method & \(\bullet\) & potholes not in the scope of \\  & & \(\bullet\) & MATLAB & & & & this study \\  & & \(\bullet\) & ArcGIS & & & & \\ \hline \multirow{4}{*}{[12]} & \multirow{4}{*}{2018} & \(\bullet\) & Machine learning algorithms & \(\bullet\) & Real-Time Solution & \(\bullet\) & Limited to Android \\  & & \(\bullet\) & Web Technology & Asfault & \(\bullet\) & Good results & \(\bullet\) & Only accelerometer sensors used \\  & & & & \(\bullet\) & Publicly available & & & \\ \hline \multirow{4}{*}{[13]} & \multirow{4}{*}{2017} & \(\bullet\) & Fuzzy system & \multirow{4}{*}{Pardim} & Smart & \(\bullet\) & Crowdsourcing adopted & \(\bullet\) & Limited to Android \\  & & Huffman coding & & Patrolling & \(\bullet\) & Open source database & \(\bullet\) & Only accelerometer sensors used \\ \hline \multirow{4}{*}{[14]} & \multirow{4}{*}{2017} & \(\bullet\) & LPC analysis & \multirow{4}{*}{Smart} & \(\bullet\) & Calculations partially made & \(\bullet\) & Tool and experimental \\  & & \(\bullet\) & Levinson-Durbin recursion & & Smart & on smartphones & & framework borrowed from \\  & & \(\bullet\) & Mathematical Models & RoadSense & \(\bullet\) & Crowdsourcing adopted & & existing works \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{2021} & \multirow{4}{*}{2021} & \(\bullet\) & S-mile platform & \multirow{4}{*}{Road} & \(\bullet\) & Crowdsourcing adopted & \(\bullet\) & Tool and experimental \\  & & & Multi-class classifiers & Condition & \(\bullet\) & Maturity of the tool used & & \(\bullet\) & framework borrowed from \\  & & & Tool & & & & \(\bullet\) & Limited to Android \\ \hline \multirow{4}{*}{[17]} & \multirow{4}{*}{2021} & \(\bullet\) & Power spectral density analysis & \multirow{4}{*}{NA} & \(\bullet\) & Lower sampling frequency & \(\bullet\) & Limited to Android \\  & & K-means unsupervised & & \(\bullet\) & Lower rate of & \(\bullet\) & Driving at a constant speed \\  & & machine learning algorithm & & battery consumption & \(\bullet\) & Fixing the smartphone at \\ \hline \multirow{4}{*}{[18]} & \multirow{4}{*}{2017} & \(\bullet\) & C4.5 Decision tree & \multirow{4}{*}{RoadSense} & \multirow{4}{*}{Use of both gyroscope} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Limited to Android} \\  & & \(\bullet\) & Weka data mining tool & & & & \\ \cline{1-1}  & & Android API & & & accelerometer sensors & & \\ \cline{1-1}  & & \(\bullet\) & SQLite database & & & & \\ \hline \multirow{4}{*}{[19]} & \multirow{4}{*}{2017} & \(\bullet\) & Gaussian model based mining & \multirow{4}{*}{NA} & \(\bullet\) & Abnormal event detection & \(\bullet\) & Only accelerometer sensors used \\  & & algorithm & & & Estimation of severity of & \(\bullet\) & No details about the mobile \\ \cline{1-1}  & & \(\bullet\) & Severity estimation algorithm & & & abnormal events & & application provided \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. After listing and examining all previous applications, our approach in this work presents many advantages. In fact, the developed application was based on a cross-platform technology (React Native: [[https://reactnative.dev](https://reactnative.dev)]([https://reactnative.dev](https://reactnative.dev)), accessed on 14 November 2022) enabling it to work on all devices (Android and iOS). The architecture of the application allows for dealing efficiently with streaming data and online error management. In addition, the adopted architecture takes into account the collaborative aspect of the application as well as the online scanning and user alerting.