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Outputs (96)

A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting (2023)
Journal Article
Djenouri, Y., Belhadi, A., Djenouri, D., Srivastava, G., & Lin, J. C. (2023). A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting. IEEE Transactions on Intelligent Transportation Systems, 24(11), 13218-13227. https://doi.org/10.1109/TITS.2023.3243542

Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV m... Read More about A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting.

Deep learning for estimating sleeping sensor’s values in sustainable IoT applications (2022)
Conference Proceeding
Djenouri, D., Laidi, R., & Djenouri, Y. (2022). Deep learning for estimating sleeping sensor’s values in sustainable IoT applications. In 2022 International Balkan Conference on Communications and Networking (BalkanCom) (147-151). https://doi.org/10.1109/BalkanCom55633.2022.9900817

The aim of this work is to develop a deep learning model that uses spatial correlation to enable turning turn off a subset of sensors while predicting their readings. This considerably saves the energy that would be consumed by those sensors both for... Read More about Deep learning for estimating sleeping sensor’s values in sustainable IoT applications.

Vehicle detection using improved region convolution neural network for accident prevention in smart roads (2022)
Journal Article
Djenouri, Y., Belhadi, A., Srivastava, G., Djenouri, D., & Line, J. C. (2022). Vehicle detection using improved region convolution neural network for accident prevention in smart roads. Pattern Recognition Letters, 158, 42-47. https://doi.org/10.1016/j.patrec.2022.04.012

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. T... Read More about Vehicle detection using improved region convolution neural network for accident prevention in smart roads.

Hybrid RESNET and regional convolution neural network for accident estimation (2022)
Journal Article
Djenouri, Y., Srivastava, G., Djenouri, D., Belhadi, A., & Jerry, C. L. (2022). Hybrid RESNET and regional convolution neural network for accident estimation. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25335-25344. https://doi.org/10.1109/TITS.2022.3165156

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT ex... Read More about Hybrid RESNET and regional convolution neural network for accident estimation.

LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST (2022)
Conference Proceeding
Mustapha, K., Djenouri, D., Jianguo, D., & Djenouri, Y. (2022). LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (694-699). https://doi.org/10.1109/MSN53354.2021.00107

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MA... Read More about LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST.

Intelligent deep fusion network for anomaly identification in maritime transportation systems (2022)
Journal Article
Djenouri, Y., Belhadi, A., Djenouri, D., Srivastava, G., & Lin, J. C. (2023). Intelligent deep fusion network for anomaly identification in maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems, 24(2), 2392-2400. https://doi.org/10.1109/TITS.2022.3151490

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime dat... Read More about Intelligent deep fusion network for anomaly identification in maritime transportation systems.

Emergent deep learning for anomaly detection in internet of everything (2021)
Journal Article
Djenouri, Y., Djenouri, D., Belhadi, A., Srivastava, G., & Lin, J. C. W. (2023). Emergent deep learning for anomaly detection in internet of everything. IEEE Internet of Things, 10(4), 3206-3214. https://doi.org/10.1109/JIOT.2021.3134932

This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environment... Read More about Emergent deep learning for anomaly detection in internet of everything.

On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications (2021)
Journal Article
Laidi, R., Djenouri, D., & Balasingham, I. (2022). On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications. IEEE Transactions on Systems Man and Cybernetics: Systems, 52(8), 5140-5151. https://doi.org/10.1109/TSMC.2021.3116141

Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consu... Read More about On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications.

Towards energy efficient clustering in wireless sensor networks: A comprehensive review (2021)
Journal Article
Merabtine, N., Djenouri, D., & Zegour, D. E. (2021). Towards energy efficient clustering in wireless sensor networks: A comprehensive review. IEEE Access, 9, 92688-92705. https://doi.org/10.1109/access.2021.3092509

Clustering is one of the fundamental approaches used to optimize energy consumption in wireless sensor networks. Clustering protocols proposed in the literature can be classified according to different criteria related to their features such as the c... Read More about Towards energy efficient clustering in wireless sensor networks: A comprehensive review.

Towards optimized one-step clustering approach in wireless sensor networks (2021)
Journal Article
Merabtine, N., Djenouri, D., Zegour, D., Bounnssairi, A., & Rahmani, K. (2021). Towards optimized one-step clustering approach in wireless sensor networks. Wireless Personal Communications, 120, 1501–1523. https://doi.org/10.1007/s11277-021-08521-0

This paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as cluster-h... Read More about Towards optimized one-step clustering approach in wireless sensor networks.