Social Phenomena and Fog Computing Networks: A Novel Perspective for Future Networks

Tu, Shanshan; Waqas, Muhammad; Rehman, Sadaqat Ur; Mir, Talha; Halim, Zahid; Ahmad, Iftekhar

doi:10.1109/tcss.2021.3082022

Cited by 29 publications

(9 citation statements)

References 70 publications

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“…state matrix and task feature vector, the decision-making optimization engine decides on computing offloading and deploys the physical instance through the edge network agent [32].…”

Section: Plos Onementioning

confidence: 99%

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Xing

Ullah

et al. 2023

PLoS ONE

Self Cite

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Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.

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“…state matrix and task feature vector, the decision-making optimization engine decides on computing offloading and deploys the physical instance through the edge network agent [32].…”

Section: Plos Onementioning

confidence: 99%

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Xing

Ullah

et al. 2023

PLoS ONE

Self Cite

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“…They use the blockchain system and verifiable cryptographic signatures to ensure the fairness of the process, and dishonest participants would be punished with the money. In addition, they also achieve a fair three-party contract signing for fog computing [22] [23]. L Guo et al implement a multi-party fair exchange protocol based on the Bitcoin system [24].…”

Section: Related Workmentioning

confidence: 99%

Multi-party Non-interactive Atomic Fair Data Exchange based on Blockchain

Zhan

Zhao

et al. 2022

Preprint

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Widely adopted blockchain-based fair data exchange protocol have the following problems in multi-party scenario: 1) in multi-buyer v.s. one seller scenario, the negotiation for data availability verification between the buyer and seller reduces transaction throughput greatly. Besides, malicious buyers can launch DoS attacks to prevent the seller from serving others by requiring lots of data availability proofs. 2) in multi-seller v.s. one buyer scenario where the buyer pays for the merged data of all sellers, current protocols treat this as multiple independent transactions, resulting in excessive on-chain costs. Moreover, current protocols neglect that data ownership establishment on-chain may be tampered since the registration info is in plaintext and submitted through the Internet. This paper proposes multi-party non-interactive atomic fair data exchange protocol based on blockchain to solve the above problems, providing data confidentiality, transaction atomic fairness, data intellectual property right protection, and high efficiency. Specifically, we propose transparent zero knowledge proof-based data verification guaranteeing the data confidentiality and transaction fairness. With the proof computed once and used everywhere, transaction throughput is improved greatly and DoS attacks initiated by malicious buyers is prevented. Moreover, the agent representing multi-seller is introduced to reduce on-chain costs. Furthermore, two-stage on-chain ownership registration is proposed to prevent eavesdroppers from impersonating the owner. Finally, we implement a POC (Proof of Concept) of our protocol as the BADE (Blockchain-based multi-party non-interactive Atomic fair Data Exchange). Experiments show that, our throughput within 12 hours is 50 times that of existing solution\cite{ref10}. And in multi-seller scenario, the on-chain gas costs of our protocol is reduced by 19.9\%-30.5\% in different seller/buyer ratios. The fairness of data exchange is also ensured by extra gas costs of dishonest party.

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“…The fog devices can communicate with one another, and each device is connected to the cloud [6]. The main difference is that the cloud is a centralized cloud network, whereas fog is a decentralized distributed system [7]. In comparison to cloud computing, fog computing provides enhanced mobility, better location awareness, heterogeneity, scalability, low latency, and geographic distribution, enabling a diverse range of IoT systems and applications [8].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Yang¹,

Tu²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a twolayered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.

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Social Phenomena and Fog Computing Networks: A Novel Perspective for Future Networks

Cited by 29 publications

References 70 publications

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Multi-party Non-interactive Atomic Fair Data Exchange based on Blockchain

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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