Data security storage model for fog computing in large-scale IoT application

2018 ◽  
Author(s):  
Shuqing He ◽  
Bo Cheng ◽  
Haifeng Wang ◽  
Xuelian Xiao ◽  
Yunpeng Cao ◽  
...  
Keyword(s):  
Data Security ◽  
Large Scale ◽  
Fog Computing ◽  
Storage Model

scholarly journals Enhancing the Health Care Data Security through Blockchain

2019 ◽  
Vol 8 (6) ◽  
pp. 549-554
Keyword(s):  
Data Security ◽  
Time Complexity ◽  
Large Scale ◽  
Fog Computing ◽  
Streaming Data ◽  
Internet Technology ◽  
Consensus Algorithm ◽  
Insurance Company ◽  
Large Scale Data ◽  
Scale Data

As the Internet technology is improving, large amount of data is generated. To deal with this huge amount of data many applications try to store the data on cloud networks. So before storing the data, security of data should be taken into consideration. Many classical existing approaches have already provided ACID properties with transaction management for consistent data provision. Some multi cloud environment systems also support to provide a consistent streaming data to end users even in high network. The blockchain is an important technique that provide the security for transactional dataset. Bitcoin is the most popular example to illustrate the strategy of blockchain execution. In this research we propose healthcare data security using blockchain and fog computing. The reason behind to use fog computing during the execution is to process the large-scale data which is generated from various sources. This work is categorized into various sections such as: insurance Company, thenthe patient registration as well as hospital registration, for each patient having a unique identification number as well as hospital also. When a particular patient communicates with Hospital as well as makes any future transaction with desired Hospital, it will store all records into the blockchain. The data has been stored according to the classical blockchain miner. SHA-256 has been used for hash generation and mining algorithm applied for validating the current hash according to given policy. The consensus algorithm used to validate the proof of work as well as to validate the current blockchain into peer to peer network. The fog computing is important to reduce the time complexity when system generate the large-scale data. According to various experimental analysis the system will provide drastic security to Private data as well as provide minimum time complexity.

Dynamic Experimental Investigation on the Self-Vibration Characteristics of Liquid Storage Tanks Under Seismic Excitations

2013 ◽  
Author(s):  
Zhou Fang ◽  
Zhiping Chen ◽  
Guodong Jia ◽  
Hui Wang ◽  
Xiang Li
Keyword(s):  
Large Scale ◽  
Vibration Characteristics ◽  
The Self ◽  
Earthquake Simulation ◽  
Seismic Excitations ◽  
Storage Model ◽  
Design Standard ◽  
Finite Element Software ◽  
Liquid Storage ◽  
Cylindrical Steel

A large-scale earthquake simulation experiment about the unanchored cylindrical steel liquid storage model tanks has been completed. The self-vibration characteristics of the model tanks with liquid inside were investigated based on the experimental data of the acceleration dynamic response. The seismic table test, the analysis methods are designed and conducted, and experimental results of the model tanks were carefully measured. Furthermore, ANSYS finite element software was used to simulate and calculate the low order natural frequency and fundamental frequency of the model tank systems according to the national design standard. The reasons for the existence of consistency and differences among the results obtained from experiments, numerical simulation and national design standard were discussed.

Characterizing the Capability of Vehicular Fog Computing in Large-scale Urban Environment

2018 ◽  
Vol 23 (4) ◽  
pp. 1050-1067 ◽  
Author(s):  
Xiaoyan Kui ◽  
Yue Sun ◽  
Shigeng Zhang ◽  
Yong Li
Keyword(s):  
Urban Environment ◽  
Large Scale ◽  
Fog Computing

scholarly journals Towards Distributed Data Management in Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Vasileios Moysiadis ◽  
Panagiotis Sarigiannidis ◽  
Ioannis Moscholios
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Cloud Services ◽  
Smart Devices ◽  
Distributed Data ◽  
Storage Allocation ◽  
Huge Amount ◽  
Distributed Data Storage

In the emerging area of the Internet of Things (IoT), the exponential growth of the number of smart devices leads to a growing need for efficient data storage mechanisms. Cloud Computing was an efficient solution so far to store and manipulate such huge amount of data. However, in the next years it is expected that Cloud Computing will be unable to handle the huge amount of the IoT devices efficiently due to bandwidth limitations. An arising technology which promises to overwhelm many drawbacks in large-scale networks in IoT is Fog Computing. Fog Computing provides high-quality Cloud services in the physical proximity of mobile users. Computational power and storage capacity could be offered from the Fog, with low latency and high bandwidth. This survey discusses the main features of Fog Computing, introduces representative simulators and tools, highlights the benefits of Fog Computing in line with the applications of large-scale IoT networks, and identifies various aspects of issues we may encounter when designing and implementing social IoT systems in the context of the Fog Computing paradigm. The rationale behind this work lies in the data storage discussion which is performed by taking into account the importance of storage capabilities in modern Fog Computing systems. In addition, we provide a comprehensive comparison among previously developed distributed data storage systems which consist of a promising solution for data storage allocation in Fog Computing.

scholarly journals Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Qi Zhao ◽  
Shuchang Lyu ◽  
Boxue Zhang ◽  
Wenquan Feng
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Large Scale ◽  
Fog Computing ◽  
Feature Extractor ◽  
Benchmark Datasets ◽  
Classification Tasks

Convolutional neural networks (CNNs) are becoming more and more popular today. CNNs now have become a popular feature extractor applying to image processing, big data processing, fog computing, etc. CNNs usually consist of several basic units like convolutional unit, pooling unit, activation unit, and so on. In CNNs, conventional pooling methods refer to 2×2 max-pooling and average-pooling, which are applied after the convolutional or ReLU layers. In this paper, we propose a Multiactivation Pooling (MAP) Method to make the CNNs more accurate on classification tasks without increasing depth and trainable parameters. We add more convolutional layers before one pooling layer and expand the pooling region to 4×4, 8×8, 16×16, and even larger. When doing large-scale subsampling, we pick top-k activation, sum up them, and constrain them by a hyperparameter σ. We pick VGG, ALL-CNN, and DenseNets as our baseline models and evaluate our proposed MAP method on benchmark datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. The classification results are competitive.

Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing

2018 ◽  
Vol 36 (3) ◽  
pp. 574-586 ◽  
Author(s):  
Xinchen Lyu ◽  
Chenshan Ren ◽  
Wei Ni ◽  
Hui Tian ◽  
Ren Ping Liu
Keyword(s):  
Large Scale ◽  
Distributed Optimization ◽  
Fog Computing
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