Blockchain-Based Distributed Software-Defined Vehicular Networks via Deep Q-Learning

Qiu, Chao; Yu, F. Richard; Xu, Fangmin; Hu, Yao; Zhao, Chenglin

doi:10.1145/3272036.3272040

Cited by 31 publications

(25 citation statements)

References 9 publications

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“…With ML and BT, financial institutions and other associated organizations across the globe can trace the data changes to make quick and fast business decisions regardless of irregular activities or suspicious transactions. Moreover, the performance of the blockchain-based Software-Defined Vehicular Network (SDVN) has been improved using deep Q-learning methodology [53], [54]. This approach used a permissioned blockchain with Byzantine fault tolerance as a consensus protocol.…”

Section: B Real-timementioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion. INDEX TERMS Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

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Section: B Real-timementioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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“…A few works are available on these integrated techniques. For example, in [343], the authors used a permissioned blockchain approach to reach a consensus in distributed SDN-based VANET. To overcome the existing drawbacks of the permissioned blockchain, they used deep Q-learning.…”

Section: F Integration With Blockchain Technologymentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

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“…Kataoka et al [16] presented a method called Trust List, which could be used to establish the trust among various IoT and SDN devices, and prevent attacks & abuses by using both public and private blockchains. Qiu et al [36] designed an approach of using a permissioned blockchain to help verify data flows and reach consensus in a distributed software defined vehicular network (SDVN). • The application of SDN to blockchains.…”

Section: Future Trend and Conclusionmentioning

confidence: 99%

“…We figure out that the trend of combining SDN and blockchain seems positive and will continue. Thanks to many benefits like management flexibility, scalability and data flow verification, blockchainbased SDN can have a broad application in various domains like intrusion detection [21], [37], healthcare industry [30], [35], vehicular [36], etc. Meanwhile, we advocate that more future efforts could be made on exploring how to improve the security and performance of blockchain-based SDN.…”

Section: Future Trend and Conclusionmentioning

confidence: 99%

Towards Blockchain-Based Software-Defined Networking: Security Challenges and Solutions

Meng

Liu

et al. 2020

IEICE Trans. Inf. & Syst.

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Software-Defined Networking (SDN) enables flexible deployment and innovation of new networking applications by decoupling and abstracting the control and data planes. It has radically changed the concept and way of building and managing networked systems, and reduced the barriers to entry for new players in the service markets. It is considered to be a promising solution providing the scale and versatility necessary for IoT. However, SDN may also face many challenges, i.e., the centralized control plane would be a single point of failure. With the advent of blockchain technology, blockchain-based SDN has become an emerging architecture for securing a distributed network environment. Motivated by this, in this work, we summarize the generic framework of blockchainbased SDN, discuss security challenges and relevant solutions, and provide insights on the future development in this field.

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Blockchain-Based Distributed Software-Defined Vehicular Networks via Deep Q-Learning

Cited by 31 publications

References 9 publications

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Towards Blockchain-Based Software-Defined Networking: Security Challenges and Solutions

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