TFL-IHOA: Three-Layer Federated Learning-Based Intelligent Hybrid Optimization Algorithm for Internet of Vehicle

The Internet of Vehicles (IOV) allows vehicles to communicate with each other in the Internet of Things (IOT). As vehicle nodes are considered to be always in motion, their topology frequently changes. The dynamic topology changes that result in these changes have caused IOV to face major issues, in...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics 2024-08, Vol.70 (3), p.5818-5828
Main Authors: Kumar Agrawal, Navin, Khan, Rijwan, Rani, Preeti, Kumar Srivastava, Ajeet, Sharma, Rohit, Yadav, Kusum, Alkhayyat, Ahmed, Aledaily, Arwa N.
Format: Article
Language:English
Subjects:
6G mobile communication
Accuracy
Algorithms
Ant colony optimization
Cloud computing
Clustering
Federated learning
Heuristic algorithms
hybrid optimization
Internet of Things
Internet of Vehicle
Internet of Vehicles
IoT
Machine learning
Network topologies
Optimization
Performance evaluation
Routing
Shortest-path problems
Surveys
Topology optimization
Vehicle dynamics
Vehicles
Vehicular ad hoc networks
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Summary:The Internet of Vehicles (IOV) allows vehicles to communicate with each other in the Internet of Things (IOT). As vehicle nodes are considered to be always in motion, their topology frequently changes. The dynamic topology changes that result in these changes have caused IOV to face major issues, including scalability, shortest-path routing, and dynamic topology changes. Clustering can be used to solve such problems. Clustering is based on an optimization approach based on transmission range, node density, speed, and direction. This paper presents a method for calculating and evaluating an optimal cluster head (CH) using ant colony and Firefly optimization algorithms. Massively interconnected networks with heterogeneous data generated at the edge of networks require distributed machine-learning techniques that can take advantage of this data. A three-layer federated learning model is proposed in this study to take advantage of the distributed end-edge-cloud architecture typical of a 5G/6G environment to increase learning efficiency and accuracy while protecting data privacy and reducing communications overhead. Our experimental and evaluation results demonstrate our proposed method's outstanding performance in improving convergence speed and learning accuracy for 5G/6G-supported IoV applications. The proposed TFL-IHOA model enhanced the number of clusters in the grid by 3-5%, reduced the computation time by 1.5-2%, and had 12-20% less packet loss than the existing algorithms.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2023.3344129