A V2V Emergent Message Dissemination Scheme for 6G-Oriented Vehicular Networks

Chen, Chen; Chenyu, Wang; Li, Cong; Ming, Xiao; Qingqi, Pei

doi:10.23919/cje.2022.00.337

Cited by 20 publications

(4 citation statements)

References 40 publications

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“…For stable clustering in VANETs, an insightful, novel, naive Bayesian likelihood-based method of estimation for traffic distribution was proposed (ANTSC) [30] based on a traditional artificial intelligence methodology and a situation where performance in a variety of network scenarios was compared in terms of traffic flow [31] and traffic management [32] using clustering; the proposed method was more effective. In [33], a method to disseminate data based on the packet delivery ratio was proposed for a 6th-generation vehicular network for the selection of information forwarder vehicles responsible for reducing the delay in transmission.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

“…It encompasses the transmission delay (TD), propagation delay (PD), queuing delay (QD), and processing delay (PrD). For defined factors, including the population and subgroups' size, the average delay (τ) is shown in Equation (33).…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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“…In recent years, along with the development of 5G/6G industries [10,11], digital twin technology has also been gradually developed in various fields in China. In the field of production manufacturing, concepts such as the Digital Twin Shopfloor (DTS) have been proposed by Tao Fei et al, who studied five key technical issues related to twins and designed the composition and operation mechanism of the DTS, providing theoretical support for the application of digital twin technology in production manufacturing.…”

Section: Related Workmentioning

confidence: 99%

Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations

He,

Zhang,

et al. 2024

Electronics

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Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages.

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“…The Sixth Generation is expected to revolutionize network coverage and user mobility by optimizing infrastructures in the air, in space, on the ground, and at sea. It aims to seamlessly integrate terrestrial and non-terrestrial networks, thus offering comprehensive and unrestricted coverage [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method

Wang,

Li,

Zhang

et al. 2024

Remote Sensing

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The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications.

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A V2V Emergent Message Dissemination Scheme for 6G-Oriented Vehicular Networks

Cited by 20 publications

References 40 publications

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations

A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method

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