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All Outputs (5)

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.

Trajectory outlier detection: New problems and solutions for smart cities (2021)
Journal Article
Djenouri, Y., Djenouri, D., & Chun-Wei Lin, J. (2021). Trajectory outlier detection: New problems and solutions for smart cities. ACM Transactions on Knowledge Discovery from Data, 15(2), Article 20. https://doi.org/10.1145/3425867

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for th... Read More about Trajectory outlier detection: New problems and solutions for smart cities.