Article Dans Une Revue
Applied Sciences
Année : 2023
Résumé
Intelligent transportation systems (ITS) enhance safety, comfort, transport efficiency, and environmental conservation by allowing vehicles to communicate wirelessly with other vehicles and road infrastructure. Cooperative awareness messages (CAMs) contain information about vehicles status, which can reveal road anomalies. Knowing the location, time, and frequency of these anomalies is valuable to road users and road authorities, and timely detection is critical for emergency response teams, resulting in improved efficiency in rescue operations. An enhanced locally selective combination in parallel outlier ensembles (ELSCP) technique is proposed for data stream anomaly detection. A data-driven approach is considered with the objective of detecting anomalies on the fly from CAMs using unsupervised detection approaches. Based on the experiments carried out, we note that ELSCP outperforms other techniques, with 3.64 % and 9.83 % better performance than the second-best technique, LSCP, on AUC-ROC and AUCPR, respectively. Based on our findings, ELSCP can effectively detect anomalies in CAMs.
Origine | Fichiers éditeurs autorisés sur une archive ouverte |
---|---|
Licence |
Cyril DE RUNZ : Connectez-vous pour contacter le contributeur
https://hal.science/hal-04133844
Soumis le : mardi 20 juin 2023-11:34:37
Dernière modification le : lundi 1 juillet 2024-15:34:05
Archivage à long terme le : jeudi 21 septembre 2023-18:41:21
Citer
Juliet Chebet Moso, Stéphane Cormier, Cyril de Runz, Hacène Fouchal, John Mwangi Wandeto. Streaming-Based Anomaly Detection in ITS Messages. Applied Sciences, 2023, 13 (12), pp.7313. ⟨10.3390/app13127313⟩. ⟨hal-04133844⟩
Collections
42
Consultations
73
Téléchargements