Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment
The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources h...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10403965/ |
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| author | Salah Zidi Bechir Alaya Tarek Moulahi Amal Al-Shargabi Salim El Khediri |
| author_facet | Salah Zidi Bechir Alaya Tarek Moulahi Amal Al-Shargabi Salim El Khediri |
| author_sort | Salah Zidi |
| collection | DOAJ |
| description | The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV. |
| format | Article |
| id | doaj-art-e25c634a5e3f47e1bffa2e08bd57b7d7 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-e25c634a5e3f47e1bffa2e08bd57b7d72025-01-24T00:02:36ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01513214510.1109/OJITS.2023.334748410403965Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network EnvironmentSalah Zidi0https://orcid.org/0000-0002-4330-6072Bechir Alaya1https://orcid.org/0009-0009-0882-7735Tarek Moulahi2https://orcid.org/0000-0002-5173-3656Amal Al-Shargabi3https://orcid.org/0000-0002-7312-9003Salim El Khediri4https://orcid.org/0000-0002-9765-1605Electrical Department, Gabes University, Gabes, TunisiaElectrical Department, Gabes University, Gabes, TunisiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaThe amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.https://ieeexplore.ieee.org/document/10403965/Vehicular networkfault predictionfault recoveryInternet of Vehicles (IoV)machine learninghierarchical temporal memory (HTM) |
| spellingShingle | Salah Zidi Bechir Alaya Tarek Moulahi Amal Al-Shargabi Salim El Khediri Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment IEEE Open Journal of Intelligent Transportation Systems Vehicular network fault prediction fault recovery Internet of Vehicles (IoV) machine learning hierarchical temporal memory (HTM) |
| title | Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment |
| title_full | Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment |
| title_fullStr | Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment |
| title_full_unstemmed | Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment |
| title_short | Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment |
| title_sort | fault prediction and recovery using machine learning techniques and the htm algorithm in vehicular network environment |
| topic | Vehicular network fault prediction fault recovery Internet of Vehicles (IoV) machine learning hierarchical temporal memory (HTM) |
| url | https://ieeexplore.ieee.org/document/10403965/ |
| work_keys_str_mv | AT salahzidi faultpredictionandrecoveryusingmachinelearningtechniquesandthehtmalgorithminvehicularnetworkenvironment AT bechiralaya faultpredictionandrecoveryusingmachinelearningtechniquesandthehtmalgorithminvehicularnetworkenvironment AT tarekmoulahi faultpredictionandrecoveryusingmachinelearningtechniquesandthehtmalgorithminvehicularnetworkenvironment AT amalalshargabi faultpredictionandrecoveryusingmachinelearningtechniquesandthehtmalgorithminvehicularnetworkenvironment AT salimelkhediri faultpredictionandrecoveryusingmachinelearningtechniquesandthehtmalgorithminvehicularnetworkenvironment |