Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond

Dai, Yueyue; Du, Xuehui; Maharjan, Sabita; Chen, Zhuang; Qian, He; Zhang, Yan

doi:10.1109/mnet.2019.1800376

Cited by 320 publications

(161 citation statements)

References 11 publications

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“…As a result, MEC can improve the response, privacy-preservation and context-awareness in contrast to cloud computing. Therefore, offloading BDA tasks to MEC servers can potentially solve the privacy-leakage and long latency issue of cloud computing with blockchain [126]. Regarding data analytics on anonymous blockchain data, there are some recent advances: 1) complex network-based community detection [127] to identify multiple addresses associated with an identical user, 2) feature extraction of transaction patterns of Bitcoin blockchain data to identify payment relationships [128], 3) analysis of user accounts and operation codes on Ethereum to detect Ponzi fraud behavior [129].…”

Section: E Difficulty In Bda In Bcotmentioning

confidence: 99%

Blockchain for Internet of Things: A Survey

Dai

Zheng

Zhang

2019

IEEE Internet Things J.

Self Cite

976

397

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Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decisionmaking. However, intrinsic features of IoT result in a number of challenges such as decentralization, poor interoperability, privacy and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as Blockchain of Things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

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Section: E Difficulty In Bda In Bcotmentioning

confidence: 99%

Blockchain for Internet of Things: A Survey

Dai

Zheng

Zhang

2019

IEEE Internet Things J.

Self Cite

976

397

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“…In order to overcome these privacy predicaments, Blockchain is an evident solution. Wide range of Blockchain based developments are proposed in [56], [57] for edge computing scenarios. In MEC point of view, Blockchain could be adoptable for securing authentication of UEs, service migration channels, mobile offloading channels, and for maintaining service status parameters hindered from the adversaries.…”

Section: E Blockchainmentioning

confidence: 99%

Realizing Multi-Access Edge Computing Feasibility: Security Perspective

Ranaweera

Jurcut

Liyanage

2019

2019 IEEE Conference on Standards for Communications and Networking (CSCN)

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Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI has introduced the paradigm of Multi-Access Edge Computing (MEC) for creating efficient data processing architecture extending the cloud computing capabilities in the Radio Access Network (RAN). Despite the gained enhancements to the mobile network, MEC is subjected to security challenges raised from the heterogeneity of IoT services, intricacies in integrating virtualization technologies, and maintaining the performance guarantees of the mobile networks (i.e. 5G). In this paper, we are identifying the probable threat vectors in a typical MEC deployment scenario that comply with the ETSI standards. We analyse the identified threat vectors and propose solutions to mitigate them.

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“…So from a management perspective of the decentralized crowd-intelligence platform, human beings and machines are just indiscriminate workers represented by SPV nodes. All kinds of computing devices, especially AI devices like in-home routers, monitoring camera and mobile phones at edge network can work as machine workers [35]. With the blockchain smart contract, these machine workers can use the earnings to pay for their continued existence, e.g., hardware resources, access to more useful information and software improvements in their whole life cycle without human intervention.…”

Section: Hybrid Human-machine Workersmentioning

confidence: 99%

A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

Wang

Bhargava

et al. 2019

IEEE Trans. Ind. Inf.

104

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Crowd-intelligence tries to gather, process, infer and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things (IIoT). A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First we use a rewardpenalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers of the mobile edge computing network, which together function as a trustless hybrid humanmachine crowd-intelligence platform. As edge servers are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved edge servers and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method respectively.Index Terms-Mobile edge computing, blockchain smart contract, crowd-intelligence ecosystem, trustless, hybrid humanmachine, reward and penalty, strong Nash equilibrium.

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Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond

Cited by 320 publications

References 11 publications

Blockchain for Internet of Things: A Survey

Blockchain for Internet of Things: A Survey

Realizing Multi-Access Edge Computing Feasibility: Security Perspective

A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

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