Recent Advances in Edge Computing Paradigms

2021 ◽  
Vol 4 (1) ◽  
pp. 37-51
Author(s):  
Sana Sodanapalli ◽  
Hewan Shrestha ◽  
Chandramohan Dhasarathan ◽  
Puviyarasi T. ◽  
Sam Goundar
Keyword(s):  
Network Architecture ◽  
Fog Computing ◽  
Minimum Cost ◽  
Workplace Safety ◽  
Edge Computing ◽  
New Approach ◽  
Data Source ◽  
Edge And Fog Computing ◽  
New Research ◽  
And Storage

Edge computing is an exciting new approach to network architecture that helps organizations break beyond the limitations imposed by traditional cloud-based networks. It has emerged as a viable and important architecture that supports distributed computing to deploy compute and storage resources closer to the data source. Edge and fog computing addresses three principles of network limitations of bandwidth, latency, congestion, and reliability. The research community sees edge computing at manufacturing, farming, network optimization, workplace safety, improved healthcare, transportation, etc. The promise of this technology will be realized through addressing new research challenges in the IoT paradigm and the design of highly-efficient communication technology with minimum cost and effort.

scholarly journals Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

2018 ◽  
Vol 10 (11) ◽  
pp. 3832 ◽  
Author(s):  
Francisco-Javier Ferrández-Pastor ◽  
Higinio Mora ◽  
Antonio Jimeno-Morenilla ◽  
Bruno Volckaert
Keyword(s):  
Fog Computing ◽  
Edge Computing ◽  
Climate Control ◽  
Embedded Devices ◽  
Computing Systems ◽  
Smart Building ◽  
Computing Paradigm ◽  
Novel Approach ◽  
Edge And Fog Computing ◽  
Building Designs

Advances in embedded systems, based on System-on-a-Chip (SoC) architectures, have enabled the development of many commercial devices that are powerful enough to run operating systems and complex algorithms. These devices integrate a set of different sensors with connectivity, computing capacities and cost reduction. In this context, the Internet of Things (IoT) potential increases and introduces other development possibilities: “Things” can now increase computation near the source of the data; consequently, different IoT services can be deployed on local systems. This paradigm is known as “edge computing” and it integrates IoT technologies and cloud computing systems. Edge computing reduces the communications’ bandwidth needed between sensors and the central data centre. Management of sensors, actuators, embedded devices and other resources that may not be continuously connected to a network (such as smartphones) are required for this method. This trend is very attractive for smart building designs, where different subsystems (energy, climate control, security, comfort, user services, maintenance, and operating costs) must be integrated to develop intelligent facilities. In this work, a method to design smart services based on the edge computing paradigm is analysed and proposed. This novel approach overcomes some drawbacks of existing designs related to interoperability and scalability of services. An experimental architecture based on embedded devices is described. Energy management, security system, climate control and information services are the subsystems on which new smart facilities are implemented.

scholarly journals Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 96 ◽  
Author(s):  
Yongpeng Shi ◽  
Yujie Xia ◽  
Ya Gao
Keyword(s):  
Approximation Algorithm ◽  
Mobile Devices ◽  
Computational Efficiency ◽  
Network Architecture ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Optimal Solutions ◽  
Resource Constrained ◽  
And Storage

As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

scholarly journals Edge and Fog Computing in Healthcare – A Review

2019 ◽  
Vol 20 (2) ◽  
pp. 191-206 ◽  
Author(s):  
Sujata Dash ◽  
Sitanath Biswas ◽  
Debajit Banerjee ◽  
Atta UR Rahman
Keyword(s):  
Healthcare System ◽  
Fog Computing ◽  
Edge Computing ◽  
Wireless Devices ◽  
Privacy Concerns ◽  
Health Care Informatics ◽  
Trade Offs ◽  
Architectural Framework ◽  
Edge And Fog Computing ◽  
Care Informatics

The architectural framework of Fog and edge computing reveals that the network components which lie between the cloud and devices computes application oriented operations. In this paper, an in-depth review of fog and mist computing in the area of health care informatics is analyzed, classified, and discussed various applications cited in the literature. For that purpose, applications are classified into different categories and a list of application-oriented tasks that can be handled by fog and edge computing are enlisted. It is further added that on which layer of the network system such fog and edge computing tasks can be computed and trade-offs with respect to requirements relevant to healthcare are provided. The review undertaken in this paper focuses on three important areas: firstly, the enormous amount of computing tasks of healthcare system can take mileage of these two computing principles; secondly, the limitation of wireless devices can be overcome by having higher network tiers which can execute tasks and aggregate the data; and thirdly, privacy concerns and dependability prevent computation tasks to completely move to the cloud. Another area which has been considered in the study is how Edge and Fog computing can make the security algorithms more efficient. The findings of the study provide evidence of the need for a logical and consistent approach towards fog and mist computing in healthcare system.

scholarly journals An Overview of Fog Computing and Edge Computing Security and Privacy Issues

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8226
Author(s):  
Ahmed M. Alwakeel
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Edge Computing ◽  
Security And Privacy ◽  
Local Network ◽  
Security Issues ◽  
Computing Paradigm ◽  
Edge And Fog Computing ◽  
Cloud Technologies ◽  
Privacy Issues

With the advancement of different technologies such as 5G networks and IoT the use of different cloud computing technologies became essential. Cloud computing allowed intensive data processing and warehousing solution. Two different new cloud technologies that inherit some of the traditional cloud computing paradigm are fog computing and edge computing that is aims to simplify some of the complexity of cloud computing and leverage the computing capabilities within the local network in order to preform computation tasks rather than carrying it to the cloud. This makes this technology fits with the properties of IoT systems. However, using such technology introduces several new security and privacy challenges that could be huge obstacle against implementing these technologies. In this paper, we survey some of the main security and privacy challenges that faces fog and edge computing illustrating how these security issues could affect the work and implementation of edge and fog computing. Moreover, we present several countermeasures to mitigate the effect of these security issues.

NIOSH total worker health program: Exploring new research horizons for workplace safety and health

2013 ◽  
Author(s):  
Heidi Hudson ◽  
Kellie Pierson ◽  
Chia-Chia Chang ◽  
Steve Sauter ◽  
Jeanie Nigam ◽  
...  
Keyword(s):  
Health Program ◽  
Workplace Safety ◽  
Worker Health ◽  
Safety And Health ◽  
New Research ◽  
Total Worker Health

Edge and Fog Computing Enabled AI for IoT-An Overview

2019 ◽  
Author(s):  
Zhuo Zou ◽  
Yi Jin ◽  
Paavo Nevalainen ◽  
Yuxiang Huan ◽  
Jukka Heikkonen ◽  
...  
Keyword(s):  
Fog Computing ◽  
Edge And Fog Computing

scholarly journals A novel approach for IoT tasks offloading in edge-cloud environments

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Service Time ◽  
Edge Computing ◽  
Cloud System ◽  
Novel Approach ◽  
Delay Sensitivity ◽  
Cloud Environments ◽  
Application Characteristics ◽  
Iot Devices ◽  
Computational Resources ◽  
And Storage

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

Implementation of MQTT protocol based network architecture for smart factory

2021 ◽  
pp. 095440542110144
Author(s):  
Chia-Shin Yeh ◽  
Shang-Liang Chen ◽  
I-Ching Li
Keyword(s):  
Machine Tool ◽  
Gesture Recognition ◽  
Manufacturing Process ◽  
Network Architecture ◽  
Information Management System ◽  
Edge Computing ◽  
Smart Factory ◽  
Smart Manufacturing ◽  
Industrial Iot ◽  
Computing Module

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

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