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All Outputs (102)

TG-SPRED: Temporal graph for sensorial data PREDiction (2024)
Journal Article
Laidi, R., Djenouri, D., Djenouri, Y., & Lin, J. C. (in press). TG-SPRED: Temporal graph for sensorial data PREDiction. ACM Transactions on Sensor Networks, https://doi.org/10.1145/3649892

This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network’s operational lifespan. Our model, TG-SPRED (Temporal Graph Sensor Prediction), predicts readi... Read More about TG-SPRED: Temporal graph for sensorial data PREDiction.

DPFTT: Distributed particle filter for target tracking in the Internet of Things (2023)
Conference Proceeding
Boulkaboul, S., Djenouri, D., & Bagaa, M. (2023). DPFTT: Distributed particle filter for target tracking in the Internet of Things. In 2023 12th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN). https://doi.org/10.23919/PEMWN58813.2023.10304926

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (... Read More about DPFTT: Distributed particle filter for target tracking in the Internet of Things.

Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES (2023)
Conference Proceeding
Laidi, R., Djenouri, D., Bagaa, M., Khelladi, L., & Djenouri, Y. (2023). Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES. In 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). https://doi.org/10.1109/pimrc56721.2023.10293922

This work targets a comprehensive model enabling energy-constrained IoT (Internet of Things) sensor devices to be inactive for extended periods while estimating their readings of real-time events. Although events seem semantically uncoupled, they are... Read More about Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES.

Knowledge guided deep learning for general-purpose computer vision applications (2023)
Conference Proceeding
Djenouri, Y., Belbachir, A. N., Jhaveri, R. H., & Djenouri, D. (2023). Knowledge guided deep learning for general-purpose computer vision applications. In Computer Analysis of Images and Patterns (185-194). https://doi.org/10.1007/978-3-031-44237-7_18

This research targets general-purpose smart computer vision that eliminates reliance on domain-specific knowledge to reach adaptable generic models for flexible applications. It proposes a novel approach in which several deep learning models are trai... Read More about Knowledge guided deep learning for general-purpose computer vision applications.

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.

Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things? (2023)
Journal Article
Belhadi, A., Djenouri, D., Djenouri, Y., Belbachir, A. N., & Srivastava, G. (2023). Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things?. IEEE Transactions on Computational Social Systems, https://doi.org/10.1109/TCSS.2023.3332562

Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we sug... Read More about Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things?.

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.

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.

Deep learning vs. traditional solutions for group trajectory outliers (2020)
Journal Article
Belhadi, A., Djenouri, Y., Djenouri, D., Michalak, T., & Chun-Wei Lin, J. (2022). Deep learning vs. traditional solutions for group trajectory outliers. IEEE Transactions on Cybernetics, 52(6), 4508-4519. https://doi.org/10.1109/TCYB.2020.3029338

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, whi... Read More about Deep learning vs. traditional solutions for group trajectory outliers.

When the decomposition meets the constraint satisfaction problem (2020)
Journal Article
Djenouri, Y., Djenouri, D., Habbas, Z., Lin, J. C., Michalak, T. P., & Cano, A. (2020). When the decomposition meets the constraint satisfaction problem. IEEE Access, 8, 207034-207043. https://doi.org/10.1109/access.2020.3038228

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is th... Read More about When the decomposition meets the constraint satisfaction problem.

A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories (2020)
Journal Article
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Cano, A., & Lin, J. C. W. (2021). A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4496-4506. https://doi.org/10.1109/tits.2020.3022612

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase de... Read More about A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories.

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection (2020)
Journal Article
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C., & Fortino, G. (2021). Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection. Information Fusion, 65, 13-20. https://doi.org/10.1016/j.inffus.2020.08.003

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories... Read More about Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection.