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2019 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Learning-based VANET Communication and Security Techniques

verfasst von: Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen

Verlag: Springer International Publishing

Buchreihe : Wireless Networks

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs.

Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming.

The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book.

This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Vehicular Ad Hoc Networks (VANETs) provides the efficient dissemination of information among the vehicles and roadside infrastructure. However, due to the high mobility of onboard units (OBUs) and the large-scale network topology, VANETs are vulnerable to attacks. In this chapter, we first review the fundamentals of VANETs in Section 1.1. Next, the type of attacks in VANETs is presented in Section 1.2, including the scope of the attack, and the impact to VANETs. We review the VANETs security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning in Section 1.3. Finally, we conclude in Section 1.4.
Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
Chapter 2. Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals
Abstract
Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units and the large-scale network infrastructure with roadside units. In this chapter, we propose a physical-layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. We also propose a privacy-preserving proximity-based security system for location-based services (LBS) in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. Finally, we use a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model.
Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
Chapter 3. Learning While Offloading: Task Offloading in Vehicular Edge Computing Network
Abstract
In vehicular edge computing (VEC) systems, vehicles can contribute their computing resources to the network, and help other vehicles or pedestrians to process their computation tasks. However, the high mobility of vehicles leads to a dynamic and uncertain vehicular environment, where the network topologies, channel states and computing workloads vary fast across time. Therefore, it is challenging to design task offloading algorithms to optimize the delay performance of tasks. In this chapter, we consider the task offloading among vehicles, and design learning-based task offloading algorithms based on the multi-armed bandit (MAB) theory, which enable vehicles to learn the delay performance of their surrounding vehicles while offloading tasks. We start from the single offloading case where each task is offloaded to one vehicle to be processed, and propose an adaptive learning-based task offloading (ALTO) algorithm, by jointly considering the variations of surrounding vehicles and the input data size. To further improve the reliability of the computing services, we introduce the task replication technique, where the replicas of each task is offloaded to multiple vehicles and processed by them simultaneously, and propose a learning-based task replication algorithm (LTRA) based on combinatorial MAB. We prove that the proposed ALTO and LTRA algorithms have bounded learning regret, compared with the genie-aided optimal solution. And we also build a system level simulation platform to evaluate the proposed algorithms in the realistic vehicular environment.
Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
Chapter 4. Intelligent Network Access System for Vehicular Real-Time Service Provisioning
Abstract
With mobile operating systems becoming increasingly common in vehicles, it is undoubted that vehicular demands for real-time Internet access would get a surge in the soon future. The vehicular ad hoc network (VANET) offloading represents a promising solution to the overwhelming traffic problem engrossed to cellular networks. With a vehicular heterogeneous network formed by a cellular network and VANET, efficient network selection is crucial to ensuring vehicles’ quality of service (QoS), avoiding network congestions and other performance degradation. To address this issue, we develop an intelligent network access system using the control theory to provide seamless vehicular communication. Specifically, our system comprises two components. The first component recommends vehicles an appropriate network to access by employing an analytic framework which takes traffic status, user preferences, service applications and network conditions into account. In the second one, a distributed automatic access engine is developed by utilizing a learning method, which enables individual vehicles to make access decisions based on access recommender, local observation and historic information. Lastly, simulations show that our proposal can effectively select the optimum network to ensure the QoS of vehicles, and network resource is fully utilized without network congestions in the meantime.
Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
Chapter 5. UAV Relay in VANETs Against Smart Jamming with Reinforcement Learning
Abstract
Frequency hopping-based anti-jamming techniques are not always applicable in VANETs due to the high mobility of OBUs and the large-scale network topology. In this chapter, we use unmanned aerial vehicles (UAVs) to relay the message of an OBU and improve the communication performance of VANETs against smart jammers that observe the ongoing OBU and UAV communication status and even induce the UAV to use a specific relay strategy and then attack it accordingly. More specifically, the UAV relays the OBU message to another RSU with a better radio transmission condition if the serving RSU is heavily jammed or interfered.
Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
Chapter 6. Conclusion and Future Work
Abstract
In this book, we first propose a PHY-layer authentication scheme based on ambient radio signals and the RSSI of packets that usually ignored in VANETs. The problem of network selection in VANETs is considered, taking into account the rapid changes in signal strength brought about by high-speed movement of the vehicle. In addition, we propose a hotbooting PHC-based UAV relay strategy to resist smart jamming without the knowledge of the UAV channel model and the jamming model. A learning-based task offloading framework using the multi-armed bandit theory is developed, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources and minimizes the average offloading delay [1].
Liang Xiao, Weihua Zhuang, Sheng Zhou, Cailian Chen
Metadaten
Titel
Learning-based VANET Communication and Security Techniques
verfasst von
Liang Xiao
Weihua Zhuang
Sheng Zhou
Cailian Chen
Copyright-Jahr
2019
Electronic ISBN
978-3-030-01731-6
Print ISBN
978-3-030-01730-9
DOI
https://doi.org/10.1007/978-3-030-01731-6

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