A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications
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Abstract (arXiv)
In this paper, we propose a swarming approach and optimize the one-hop delay for interplatoon communications through adjusting the minimum contention window size of each backbone vehicle in two steps. In the first step, we first set a small enough average one-hop delay as the initial optimization goal and then propose a swarming approach to find a minimum average one-hop delay for inter-platoon communications through adjusting the minimum contention window of each backbone vehicle iteratively. In the second step, we first set the minimum average one-hop delay found in the first step as the initial optimization goal and then adopt the swarming approach again to get the one-hop delay of each backbone vehicle balance to the minimum average one-hop delay. The optimal minimum contention window sizes that get the one-hop delay of each backbone vehicle balance to the minimum average one-hop delay are obtained after the second step. The simulation results indicate that the one-hop delay is optimized and the other performance metrics including end-to-end delay, one-hop throughput and transmission probability are presented by using the optimal minimum contention window sizes.
Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jul 2018.
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Citing papers per year: 2024: 12; 2025: 3; 2026: 1.
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- 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
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- 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation18 Jul 2024
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- 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
- 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
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- 2024Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehiclescited by 0
- 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenariocited by 0
- 2025PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2Xcited by 0
- 2026Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoVcited by 0
- 2026Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV6 May 2026
- 2025A Survey on Semantic Communications in Internet of Vehicles3 Mar 2025
- 2025PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X22 Jan 2025
- 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV20 Nov 2024
- 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning7 Nov 2024
- 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario30 Oct 2024
- 2024Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles20 Sep 2024
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- 2024Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation16 Jul 2024
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- 2024Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network10 Jul 2024