A swarm algorithm for collaborative traffic in vehicular networks
- URL: http://arxiv.org/abs/2501.10007v1
- Date: Fri, 17 Jan 2025 07:42:11 GMT
- Title: A swarm algorithm for collaborative traffic in vehicular networks
- Authors: Jamal Toutouh, Enrique Alba,
- Abstract summary: We propose a swarm intelligence based distributed congestion control strategy to maintain the channel usage level under the threshold of network malfunction.
An exhaustive experimentation shows that the proposed strategy improves the throughput of the network, the channel usage, and the stability of the communications.
- Score: 2.6273514225715435
- License:
- Abstract: Vehicular ad hoc networks (VANETs) allow vehicles to exchange warning messages with each other. These specific kinds of networks help reduce hazardous traffic situations and improve safety, which are two of the main objectives in developing Intelligent Transportation Systems (ITS). For this, the performance of VANETs should guarantee the delivery of messages in a required time. An obstacle to this is that the data traffic generated may cause network congestion. Data congestion control is used to enhance network capabilities, increasing the reliability of the VANET by decreasing packet losses and communication delays. In this study, we propose a swarm intelligence based distributed congestion control strategy to maintain the channel usage level under the threshold of network malfunction, while keeping the quality-of-service of the VANET high. An exhaustive experimentation shows that the proposed strategy improves the throughput of the network, the channel usage, and the stability of the communications in comparison with other competing congestion control strategies.
Related papers
- AI Flow at the Network Edge [58.31090055138711]
AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers.
This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
arXiv Detail & Related papers (2024-11-19T12:51:17Z) - Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks.
Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
arXiv Detail & Related papers (2024-11-01T00:37:00Z) - LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks [2.943640991628177]
Vehicle-to-everything (V2X) networks support a variety of safety, entertainment, and commercial applications.
This paper introduces a framework for proactive congestion management for IoV networks.
arXiv Detail & Related papers (2024-10-12T21:21:42Z) - Joint Optimization of Traffic Signal Control and Vehicle Routing in
Signalized Road Networks using Multi-Agent Deep Reinforcement Learning [19.024527400852968]
We propose a joint optimization approach for traffic signal control and vehicle routing in signalized road networks.
The objective is to enhance network performance by simultaneously controlling signal timings and route choices using Multi-Agent Deep Reinforcement Learning (MADRL)
Our work is the first to utilize MADRL in determining the optimal joint policy for signal control and vehicle routing.
arXiv Detail & Related papers (2023-10-16T22:10:47Z) - An Intelligent SDWN Routing Algorithm Based on Network Situational
Awareness and Deep Reinforcement Learning [4.085916808788356]
This article introduces an intelligent routing algorithm (DRL-PPONSA) based on deep reinforcement learning with network situational awareness.
Experimental results show that DRL-PPONSA outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance.
arXiv Detail & Related papers (2023-05-12T14:18:09Z) - Reinforcement Learning for Joint V2I Network Selection and Autonomous
Driving Policies [14.518558523319518]
Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs)
It is critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions.
We develop a reinforcement learning framework to characterize efficient network selection and autonomous driving policies.
arXiv Detail & Related papers (2022-08-03T04:33:02Z) - Federated Semi-Supervised Classification of Multimedia Flows for 3D
Networks [0.16799377888527683]
Traffic classification is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management.
3D networks offer multiple routes that can guarantee different levels of anomaly detection.
In this paper, a cooperative feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised manner.
arXiv Detail & Related papers (2022-05-01T20:18:07Z) - AI-aided Traffic Control Scheme for M2M Communications in the Internet
of Vehicles [61.21359293642559]
The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies.
We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
arXiv Detail & Related papers (2022-03-05T10:54:05Z) - Federated Learning over Wireless IoT Networks with Optimized
Communication and Resources [98.18365881575805]
Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention.
It is of interest to investigate fast responding and accurate FL schemes over wireless systems.
We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
arXiv Detail & Related papers (2021-10-22T13:25:57Z) - Decentralized Learning for Channel Allocation in IoT Networks over
Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game [134.88020946767404]
We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network.
Our study maps this problem into a contextual multi-player, multi-armed bandit game, and proposes a purely decentralized, three-stage policy learning algorithm through trial-and-error.
arXiv Detail & Related papers (2020-03-30T10:05:35Z) - Wireless Power Control via Counterfactual Optimization of Graph Neural
Networks [124.89036526192268]
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium.
To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture.
We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
arXiv Detail & Related papers (2020-02-17T07:54:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.