scholarly journals Measurement and Characterization of Electromagnetic Noise in Edge Computing Networks for the Industrial Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3104 ◽  
Author(s):  
Huiting Li ◽  
Liu Liu ◽  
Yiqian Li ◽  
Ze Yuan ◽  
Kun Zhang
Keyword(s):  
Internet Of Things ◽  
Wireless Network ◽  
Markov Models ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Statistical Parameters ◽  
Electromagnetic Noise ◽  
Technological Basis ◽  
Industrial Internet ◽  
Measurement Results

Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz–3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.

scholarly journals Editorial: Special Section on Pervasive Edge Computing for Industrial Internet of Things

2021 ◽  
Vol 17 (7) ◽  
pp. 5010-5011
Author(s):  
Zhaolong Ning ◽  
Edith Ngai ◽  
Ricky Y. K. Kwok ◽  
Mohammad S. Obaidat
Keyword(s):  
Internet Of Things ◽  
Special Section ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet

scholarly journals Wireless Powered Mobile Edge Computing for Industrial Internet of Things Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101539-101549 ◽  
Author(s):  
Hao Wu ◽  
Hui Tian ◽  
Gaofeng Nie ◽  
Pengtao Zhao
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet

scholarly journals A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

2021 ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

Abstract Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

scholarly journals Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

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