Unequal Clustering Protocol in IoT Networks Based on Multiple Criteria Processing
Conference paper
Authors | Mir, F. and Meziane, F. |
---|---|
Type | Conference paper |
Abstract | The Internet of Things (IoT) is a rapidly expanding network characterized by a very significant number of heterogeneous devices with limited resources. Clustering is a very powerful technique used to reduce the communication load, conserve energy, aggregate data and optimize the performance of IoT systems. Designing an efficient clustering algorithm is a real challenge. Once the Cluster Heads (CHs) are elected, traditional clustering algorithms typically use a single clustering radius to group nodes to form clusters. However, this approach is potentially not optimized, as devices within a cluster may have different characteristics. In this paper, we focus on the DCOPA protocol proposed for clustering in IoT networks. Once the CHs are elected, the identical clustering radius is applied to these different elected CHs despite their differences in terms of residual energy and Distance to Base Station. The new approach, called Unequal-DCOPA (UDCOPA), makes it possible to define for each CH its clustering radius which will be sensitive to the local criteria of energy and DistBS for the CH that is being considered. The radius is modelized as a multicriteria system. Simulation results show that our new protocol UDCOPA outperforms DCOPA in energy management and lifetime parameters of nodes and network. UDCOPA increases lifetime by 34.55% when compared to DCOPA. |
Keywords | IoT; DCOPA Protocol; Unequal Clustering; Data Communication; Load Balancing; Energy Efficiency |
Year | 2023 |
Conference | 6th International Conference on Information Science and Systems (ICISS 2023) |
Publisher | ACM Press |
Web address (URL) | http://iciss.org/ |
Accepted author manuscript | File Access Level Controlled |
ISBN | 9798400708206 |
Output status | Published |
Publication dates | |
Online | 2023 |
Publication process dates | |
Accepted | 2023 |
Deposited | 25 Jul 2023 |
https://repository.derby.ac.uk/item/9zw75/unequal-clustering-protocol-in-iot-networks-based-on-multiple-criteria-processing
99
total views0
total downloads2
views this month0
downloads this month
Export as
Related outputs
Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification
Abdi, A., Melhem, W. and Meziane, F. 2024. Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification. Applied Sciences. 14 (23), pp. 1-21. https://doi.org/10.3390/app142311009Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs
Melhem, W., Abdi, A. and Meziane, F. 2024. Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs. 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0013066900003838Unequal-Radius Clustering in WSN-Based IoT Networks : Energy Optimization and Load Balancing in UDCOPA Protocol
Mir, F. and Meziane, F. 2024. Unequal-Radius Clustering in WSN-Based IoT Networks : Energy Optimization and Load Balancing in UDCOPA Protocol. The Journal of Supercomputing. pp. 1-32. https://doi.org/10.1007/s11227-024-06426-wQuantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol
Mir, F., Meziane, F., Bounceur, A. and Laouid, A. 2024. Quantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol. 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE Computer Society. https://doi.org/10.1109/PAIS62114.2024.10541142EDCOPA : Enhancing DCOPA Protocol by Exploring New Criteria for Improved Clustering
Mir, F., Bounceur, A. and Meziane, F. 2024. EDCOPA : Enhancing DCOPA Protocol by Exploring New Criteria for Improved Clustering. Proceedings of the 7th International Conference on Future Networks and Distributed Systems (ICFNDS '23). ACM Press. https://doi.org/10.1145/3644713.36448Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function
Hasan, A.M., Khalaf, M., Sabbar, B.M., Ibrahim, R.W., A. Jalab, H.A. and Meziane, F. 2024. Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function. Baghdad Science Journal. 21 (12), pp. 1-11. https://doi.org/10.21123/bsj.2024.9286LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model
Al Qerom, M., Otair, M., Meziane, F., AbdulRahman, S. and Alzubi, M. 2024. LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model. Journal of Robotics and Control. 5 (5), pp. 1311-3121. https://doi.org/10.18196/jrc.v5i5.21834A graph based named entity disambiguation using clique partitioning and semantic relatedness
Belalta, R., Belazzoug, M. and Meziane, F. 2024. A graph based named entity disambiguation using clique partitioning and semantic relatedness. Data and Knowledge Engineering. pp. 1-27. https://doi.org/10.1016/j.datak.2024.102308Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization
Asogbon, M.G., Samuel, O., Meziane, F., Li, G. and Li, Y. 2024. Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization. 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012373400003657Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis
Sami, N., Makkar, A., Meziane, F. and Conway, M. 2024. Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis. International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer. https://doi.org/10.1007/978-3-031-53085-2_17Novel Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks
Mir, F. and Meziane, F. 2024. Novel Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks. Expert Systems with Applications. 247, pp. 1-23. https://doi.org/10.1016/j.eswa.2024.123212Molecular subtypes classification of breast cancer in DCE-MRI using deep features
Hasan, A.M., Al-Waely, N.K.N., Aljobouri, H.K., Jalab, H.A., Ibrahim, R.W. and Meziane, F. 2024. Molecular subtypes classification of breast cancer in DCE-MRI using deep features. Expert Systems with Applications. 236, pp. 1-8. https://doi.org/10.1016/j.eswa.2023.121371Diagnosis of Breast Cancer Based on Hybrid Features Extraction in Dynamic Contrast Enhanced Magnetic Resonance Imaging
Hasan, A.M., Aljobouri, H.K., Al-Waely, K.N.A., Ibrahim, W.I., Jalab, H.A. and Meziane, F. 2023. Diagnosis of Breast Cancer Based on Hybrid Features Extraction in Dynamic Contrast Enhanced Magnetic Resonance Imaging. Neural Computing and Applications. pp. 1-14. https://doi.org/10.1007/s00521-023-08909-yClassification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion
Hasan, A.M., Al-Waely, N.K.N., Ajobouri, H.K., Ibrahim, R.W., Jalab, H.A. and Meziane, F. 2023. Classification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion. Biomedical Signal Processing and Control. 84, pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105002The Impact of Arabic Diacritization on Word Embeddings
Abbache, M., Abbache, A., Xu, J.W., Meziane, F. and Wen, X.B. 2023. The Impact of Arabic Diacritization on Word Embeddings. ACM Transactions on Asian and Low-Resource Language Information Processing . pp. 1-32. https://doi.org/10.1145/3592603![](/~544/ssr/default-thumbnail.png)
A review of the generation of requirements specification in natural language using objects UML models and domain ontology
Abdalazeima, Alaa and Meziane, Farid 2021. A review of the generation of requirements specification in natural language using objects UML models and domain ontology. Procedia Computer Science. 189, pp. 328-334. https://doi.org/10.1016/j.procs.2021.05.102Mitigation of Popularity Bias in Recommendation Systems
Karboua, S., Harrag, F., Meziane, F. and Boutadjine, A. 2022. Mitigation of Popularity Bias in Recommendation Systems. Tunisian-Algerian Joint Conference on Applied Computing. Constantine, Algeria 14 - 15 Dec 2022Describing Pulmonary Nodules Using 3D Clustering
Al-Funjan, A., Farid Meziane and Aspin, R. 2022. Describing Pulmonary Nodules Using 3D Clustering. Advanced Engineering Research. 22 (3), pp. 261-271. https://doi.org/10.23947/2687-1653-2022-22-3-261-271Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach
Swee, C.P., Labadin, J. and Meziane, F. 2022. Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach. Journal of Computing and Social Informatics. 1 (2), pp. 1-16. https://doi.org/10.33736/jcsi.4761.2022DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications
Mir, F. and Meziane, F. 2022. DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications. Cluster Computing. 26, p. 1077–1098. https://doi.org/10.1007/s10586-022-03741-w![](/~544/ssr/default-thumbnail.png)
Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
Almomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid 2021. Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing. 36 (1), p. 50. https://doi.org/10.1016/j.cosrev.2020.100305![](/~544/ssr/default-thumbnail.png)
MRI brain classification using the quantum entropy LBP and deep-learning-based features
Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R. and Obaiys, Suzan J. 2020. MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy. 22 (9), p. 1033. https://doi.org/10.3390/e22091033![](/~544/ssr/default-thumbnail.png)