Outputs (5)

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.

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?.