scholarly journals DDGS: A Network Coding Scheme for Dynamic Adaptation to Heterogeneous Vehicular Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zongzheng Wang ◽  
Ping Dong ◽  
Tao Zheng ◽  
Hongke Zhang
Keyword(s):  
Network Coding ◽  
Vehicular Networks ◽  
Rapid Development ◽  
Heterogeneous Wireless Networks ◽  
Vehicular Network ◽  
Time Cost ◽  
Transportation Industry ◽  
Network Resources ◽  
Bandwidth Aggregation ◽  
Coding Scheme

The rapid development of the transportation industry has brought about the demand for massive data transmission. In order to make use of a large number of heterogeneous network resources in vehicular network, the research of applying network coding to multipath transmission has become a hot topic. Network coding can better solve the problems of packet reordering and low aggregation efficiency. The determination of coding scale is the key to network coding scheme. However, the existing research cannot adapt to the different characteristics of network resources in vehicular network, leading to larger decoding time cost and lower bandwidth aggregation efficiency. In this paper, we propose a network coding scheme called Delay Determined Group Size (DDGS), which could adaptively adjust the coding group according to the heterogeneous wireless networks state. The mathematical analysis and process design of the DDGS scheme are discussed in detail. Through a large number of simulations, we proved that the DDGS scheme is significantly superior to other coding group determination schemes in terms of decoding time cost and bandwidth aggregation efficiency.

scholarly journals Leveraging Aviation Risk Models to Combat Cybersecurity Threats in Vehicular Networks

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 390
Author(s):  
Jonathan Ebert ◽  
Ohad Newton ◽  
Jeffery O’Rear ◽  
Scott Riley ◽  
Jaehong Park ◽  
...  
Keyword(s):  
Risk Management ◽  
Vehicular Networks ◽  
Vehicular Network ◽  
Management Process ◽  
Transportation Industry ◽  
Risk Models ◽  
Vehicle To Vehicle ◽  
Risk Management Process ◽  
Vehicle To Infrastructure ◽  
Aviation Risk

The rapidly developing technology and lack of standards in the transportation industry for the proposed Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) networks, which all vehicles will operate under, drives concern about information validity and authenticity due to the risk of erroneous or malicious information being injected into a vehicular network (VN). In this paper, we apply a risk management process to a vehicular network that will identify hazards and possible controls that can lower their risk. After researching and reviewing various technologies along with several risk models, we have developed a basic framework for assessing and assigning risk through a phased method that leads to input for our developed model.

scholarly journals Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6542
Author(s):  
Ida Nurcahyani ◽  
Jeong Woo Lee
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Vehicular Networks ◽  
Learning Algorithms ◽  
Vehicular Network ◽  
Machine Learning Algorithms ◽  
Allocation Strategy ◽  
Network Resources ◽  
Network Resource ◽  
Resource Allocation Strategy

The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

Secure multi-source network coding scheme with lattice signature

2015 ◽  
Vol 20 (6) ◽  
pp. 476-482 ◽  
Author(s):  
Tao Shang ◽  
Tianli Peng ◽  
Jianwei Liu
Keyword(s):  
Network Coding ◽  
Coding Scheme

scholarly journals Distributed Data Processing in Vehicular Networks using Mobile Agents

1970 ◽  
Vol 2 ◽  
pp. 61-62
Author(s):  
Óscar Urra ◽  
Sergio Ilarri
Keyword(s):  
Wireless Communications ◽  
Mobile Agent ◽  
Open Problem ◽  
Mobile Agents ◽  
Short Range ◽  
Vehicular Networks ◽  
Vehicular Network ◽  
Agent Technology ◽  
Distributed Data ◽  
Distributed Data Processing

In a vehicular network, vehicles can exchange interesting information (e.g., about accidents, traffic status, etc.) using short-range wireless communications. Besides, the vehicles can be equipped with additional sensors that can directly obtain data from the environment. How to efficiently process and collect these data is an open problem. We argue that mobile agent technology could be helpful.

scholarly journals Treatment Design and Construction Strategy for Tunnel Mud and Water Inrush Disaster

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Yike Wei ◽  
Lingfeng Yu
Keyword(s):  
Rapid Development ◽  
Water Inrush ◽  
Construction Process ◽  
Transportation Industry ◽  
Treatment Efficiency ◽  
Geographical Environment ◽  
Geological Conditions ◽  
Treatment Measures ◽  
Construction Strategy ◽  
Treatment Design

With the rapid development of the transportation industry in China, the number and scale of tunnel construction are increasing. Tunneling through fault zones and other complex geological environments is becoming more and more common. In the construction of highway tunnels, due to the special geographical environment and complex geological conditions, mud and water inrush often occur in the tunnel. Water inrush disasters pose a major risk to the construction of highway tunnels and affect the normal construction of highway tunnels. This paper combines the engineering background of the tunnel mud and water inrush accidents, carries out evaluation on the accident treatment measures and the treatment efficiency, and summarizes the main concerns in the construction process and the technical guidelines for dealing with the tunnel mud and water inrush.

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