scholarly journals Reused Shadow Recovery Scheme for Flash-based Edge Gateway Servers

2021 ◽  
Vol 15 ◽  
pp. 1695-1701
Author(s):  
Siwoo Byun
Keyword(s):  
Flash Memory ◽  
Data Center ◽  
Edge Computing ◽  
Solid State Drives ◽  
Computing Technology ◽  
Local Data ◽  
Data Services ◽  
Center Edge ◽  
Recovery Scheme ◽  
Central Data

Edge computing refers to decentralized computing technology to reduce cloud computing's overload or security problems that redirect local data to a central data center. Edge computing is emerging as a technology that complements cloud computing in an IoT environment where huge amounts of data are generated in real time. Recently, solid state drives using flash memory have recently been recognized as a suitable storage for massive IoT data services. In this study, we propose a new data recovery scheme based on shadow paging using flash memory for effective and safe data services in IoT edge gateways. The proposed scheme recycles invalidated old data blocks that are discarded when new data is stored. Thus, The proposed scheme minimizes the burden of additional storage space required to traditional shadow paging schemes, and reduces I/O performance degradation. Simulation results show that the space gain of the proposed scheme reaches even to 29%.

scholarly journals Research on Low-Power Main Control Chip Architecture Based on Edge Computing Technology

2021 ◽  
Vol 1802 (3) ◽  
pp. 032031
Author(s):  
Guoyu Cui ◽  
Wei Ye ◽  
Zhanbin Hou ◽  
Tong Li ◽  
Ruolin Liu
Keyword(s):  
Low Power ◽  
Edge Computing ◽  
Computing Technology

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

Fault diagnosis of motor based on low cost edge computing technology

2020 ◽  
Author(s):  
Yi-Horng Lai ◽  
Ye-Cheng Zhang ◽  
Liang Fang ◽  
Chiao-Sheng Wang ◽  
Jau-Woei Perng
Keyword(s):  
Fault Diagnosis ◽  
Low Cost ◽  
Edge Computing ◽  
Computing Technology

scholarly journals A Multifault Diagnosis Method of Gear Box Running on Edge Equipment

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiao-ping Zhao ◽  
Yong-hong Zhang ◽  
Fan Shao
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Data Center ◽  
Edge Computing ◽  
Industrial Equipment ◽  
Point Model ◽  
Computing Device ◽  
External Data ◽  
Diagnosis Method ◽  
Analyze Data

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

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