ThinORAM: Towards Practical Oblivious Data Access in Fog Computing Environment

Huang, Yanyu; Li, Bo; Liu, Zheli; Li, Jin; Yiu, Siu Ming; Baker, Thar; Gupta, Brij B.

doi:10.1109/tsc.2019.2962110

Cited by 24 publications

(18 citation statements)

References 22 publications

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“…Consensus mechanism refers to the process of reaching a unified agreement on the state of the network in a decentralized manner, which helps to verify that information is added to the ledger, ensuring that only real transactions are recorded on the blockchain [22]. It means that a certain node modifies a certain data in the block during storage, but other nodes have no consensus on this block, and the modification operation is invalid.…”

Section: Consensus Mechanismmentioning

confidence: 99%

Mine Consortium Blockchain: The Application Research of Coal Mine Safety Production Based on Blockchain

Qiang

Wang

Song

et al. 2021

Security and Communication Networks

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To solve the problem that the safety data in the process of coal mine production are easy to be maliciously tampered with and deleted, a mine consortium blockchain data security monitoring system is proposed. The coal mine consortium blockchain includes supervision department, builds favourable centralized and decentralized production mode, and improves PBFT (Practical Byzantine Fault Tolerance) consensus mechanism to implement practical coal mine safety production. The evaluation shows that the architecture we proposed is more appropriate and efficient for the mine Internet of Things than the traditional blockchain architecture. The Hyperledger Fabric platform is used to build the mine consortium blockchain system to achieve the sensor data reliability, node consensus, safe operation automation management, and major equipment traceability.

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Section: Consensus Mechanismmentioning

confidence: 99%

Mine Consortium Blockchain: The Application Research of Coal Mine Safety Production Based on Blockchain

Qiang

Wang

Song

et al. 2021

Security and Communication Networks

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“…Which is generally referred to the Cloud of Things (CoT) integration 4 . Within this framework, we need techniques that would enable artificial intelligence (AI) to reduce data dimensions and complexity 7–10 . These techniques can be executed in fog level since it is close to IoT devices.…”

Section: Introductionmentioning

confidence: 99%

A fog computing data reduce level to enhance the cloud of things performance

Moulahi

Khediri

Khan

et al. 2021

Int J Communication

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Summary Devices in the Internet of Things (IoT) generate and gather a vast amount of data. Smart devices with constrained resources cannot execute large data‐based machine learning (ML) algorithms. Therefore, in this paper, we propose, analyze, and evaluate data reduction at the Fog level. As a matter of fact, the focus of this paper is twofold: The first objective is to process the reduced datasets by ML, and the second aim is to segregate the irrelevant data and preserve the quality of the ML models. The naïve Bayesian classifier is used for model analysis. For data (attributes) reduction, the state‐of‐the‐art approaches of CF Subset Evaluation, Info Gain Evaluation, Gain Ratio Attribute Evaluation, and the principal component analysis (PCA) approaches are employed. From the implementation point of view, the naïve Bayesian classifier is used to learn the class distribution and the correlation of the classes with the rest of the features. The naïve Bayesian model is generated using the full dataset without feature reduction. Feature selection algorithms then reduce features by 50%. This reduced set is also used to generate the naïve Bayesian model, which gives us a benchmark for comparing the results with the original unreduced dataset. From the results, we find that the performance of the reduced set is either increased or is almost similar to the full set of features. With this comprehensive experimental evaluation, we believe that data reduction can provide a blueprint for avoiding unnecessary data storage and processing. The performance of the ML models is not drastically affected by feature reduction and hence demonstrates the efficacy of the reduced ML models.

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“…Ferry nodes moving between different regions connect to different areas. At present, research studies on ferry nodes mainly include routing algorithms based on ferry nodes [3][4][5][6][7][8][9][10][11][12], ferry node motion path planning [13][14][15][16][17][18][19], and ferry node network signal coverage [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

Wang

Zhang

et al. 2021

Security and Communication Networks

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An opportunistic network is a special type of wireless mobile ad hoc network that does not require any infrastructure, does not have stable links between nodes, and relies on node encounters to complete data forwarding. The unbalanced energy consumption of ferry nodes in an opportunistic network leads to a sharp decline in network performance. Therefore, identifying the ferry node group plays an important role in improving the performance of the opportunistic network and extending its life. Existing research studies have been unable to accurately identify ferry node clusters in opportunistic networks. In order to solve this problem, the concepts of k-core and structural holes have been combined, and a new evaluation indicator, namely, ferry importance rank, has been proposed in this study for analyzing the dynamic importance of nodes in a network. Based on this, a ferry cluster identification model has been designed for accurately identifying the ferry node clusters. The results of the simulations conducted for verifying the performance of the proposed model show that the accuracy of the model to identify the ferry node clusters is 100%.

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ThinORAM: Towards Practical Oblivious Data Access in Fog Computing Environment

Cited by 24 publications

References 22 publications

Mine Consortium Blockchain: The Application Research of Coal Mine Safety Production Based on Blockchain

Mine Consortium Blockchain: The Application Research of Coal Mine Safety Production Based on Blockchain

A fog computing data reduce level to enhance the cloud of things performance

Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

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