Abstract
Many vehicular network applications such network administration, routing, and data transmission protocols require location information. If a precise prediction of the vehicle’s future move can be made, resources can be allocated optimally while vehicle travels. Will result in improving VANETs performance. For that purpose, Kalman filter is proposed for correcting and predicting vehicle’s position. The research used both real vehicle movement traces and model-driven traces. Kalman filter and neural network-based techniques are quantitatively compared. Across all scenarios proposed, model exhibits superiority than other correction and prediction schemes.
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Yaduwanshi, R., Kumar, S. (2023). Location Accuracy and Prediction in VANETs Using Kalman Filter. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_49
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DOI: https://doi.org/10.1007/978-981-19-4676-9_49
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