Blockchain-Based Resource Allocation Model in Fog Computing

Wang, Haoyu; Wang, Lina; Zhou, Zhichao; Tao, Xueqiang; Pau, Giovanni; Arena, Fabio

doi:10.3390/app9245538

Cited by 50 publications

(24 citation statements)

References 40 publications

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“…Using the common-state function ΛðtÞ, we can combine the benefits of edge users with the QoE level state to construct a Hamiltonian operator H i ðtÞ. Based on the principle of the Bellman dynamic programming, the constructed Hamiltonian operator can be described as follows [26,27]:…”

Section: Game Analysismentioning

confidence: 99%

Dynamic Game‐Based Computation Offloading and Resource Allocation in LEO‐Multiaccess Edge Computing

Wang

2021

Wireless Communications and Mobile Computing

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The offloading of computing tasks in edge computing has always been a research hotspot and difficulty in recent years. As an effective way to run various applications on mobile devices with limited resources, it has been extensively studied by scholars from all walks of life. However, the traditional ground-based network-based edge computing network architecture cannot meet the needs of edge users with limited geographic areas. Therefore, this paper proposes an LEO (low earth orbit) satellite-based multiaccess edge computing network architecture and establishes a differential game model for this architecture. To obtain the Nash equilibrium solution of the open loop and the Nash equilibrium solution of the feedback for the task offloading amount, the relationship between the user’s income and the QoE level under the optimal task offloading amount is finally analyzed and discussed.

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Section: Game Analysismentioning

confidence: 99%

Dynamic Game‐Based Computation Offloading and Resource Allocation in LEO‐Multiaccess Edge Computing

Wang

2021

Wireless Communications and Mobile Computing

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“…Recently, with the development of large scale datasets, the improvement of computing power, and reliable and efficiency computing resources allocation [35], applications based on deep learning have entered a period of rapid development. Trackers based on the deep Siamese network have also drawn great attention and shown significant improvements regarding tracking performance.…”

Section: Zhang Et Al Proposed a Ensemble Tracker Based On Multi-expert Entropy Minimizationmentioning

confidence: 99%

Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking

Chen

Wang

2021

Symmetry

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Recently, Discriminative Correlation Filters (DCF) have shown excellent performance in visual object tracking. The correlation for a computing response map can be conducted efficiently in Fourier domain by Discrete Fourier Transform (DFT) of inputs, where the DFT of an image has symmetry on the Fourier domain. To enhance the robustness and discriminative ability of the filters, many efforts have been devoted to optimizing the learning process. Regularization methods, such as spatial regularization or temporal regularization, used in existing DCF trackers aim to enhance the capacity of the filters. Most existing methods still fail to deal with severe appearance variations—in particular, the large scale and aspect ratio changes. In this paper, we propose a novel framework that employs adaptive spatial regularization and temporal regularization to learn reliable filters in both spatial and temporal domains for tracking. To alleviate the influence of the background and distractors to the non-rigid target objects, two sub-models are combined, and multiple features are utilized for learning of robust correlation filters. In addition, most DCF trackers that applied 1-dimensional scale space search method suffered from appearance changes, such as non-rigid deformation. We proposed a 2-dimensional scale space search method to find appropriate scales to adapt to large scale and aspect ratio changes. We perform comprehensive experiments on four benchmarks: OTB-100, VOT-2016, VOT-2018, and LaSOT. The experimental results illustrate the effectiveness of our tracker, which achieved a competitive tracking performance. On OTB-100, our tracker achieved a gain of 0.8% in success, compared to the best existing DCF trackers. On VOT2018, our tracker outperformed the top DCF trackers with a gain of 1.1% in Expected Average Overlap (EAO). On LaSOT, we obtained a gain of 5.2% in success, compared to the best DCF trackers.

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“…Authors of [35] proposed a model in which BC is deployed in IoT-Fog-Cloud environment, to hold information about the contributed resources by fog nodes. The resulting BC in this system provides reliable and credible evaluation index for fog nodes.…”

Section: A Internet Of Things Applications (Iot)mentioning

confidence: 99%

A Survey on Blockchain-Fog Integration Approaches

Baniata

Kertész

2020

IEEE Access

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Fog computing (FC) is the extension of Cloud Computing (CC), from the core of the internet architecture to the edge of the network, with the aim to perform processes closer to end-users. This extension is proven to enhance security, and to reduce latency and energy consumption. Blockchain (BC), on the other hand, is the base technology behind crypto-currencies, yet is implemented in wide range of different applications. The security and reliability, along with the distributed trust management criteria proposed in BC, excited the research community to integrate it with FC, in a step towards reaching a distributed and trusted, Data, Payment, Reputation, and Identity management systems. In this survey we present the upto-date state-of-the-art of FC-BC integration with a detailed literature review and classification. We discuss and categorize the related papers according to the year of publication, domain, used algorithms, BC roles, and the placement of the BC in the FC architecture. Our research presents detailed observations, analysis, and open challenges for the BC-FC integration. We believe such conclusions may clarify the vision of the BC-FC integration, and calibrate the compass towards open issues and future research directions.

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Blockchain-Based Resource Allocation Model in Fog Computing

Cited by 50 publications

References 40 publications

Dynamic Game‐Based Computation Offloading and Resource Allocation in LEO‐Multiaccess Edge Computing

Dynamic Game‐Based Computation Offloading and Resource Allocation in LEO‐Multiaccess Edge Computing

Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking

A Survey on Blockchain-Fog Integration Approaches

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