Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications

2020 ◽  
Vol 16 (9) ◽  
pp. 6050-6058 ◽  
Author(s):  
Mithun Mukherjee ◽  
Suman Kumar ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Rakesh Matam ◽  
...  
Keyword(s):  
Fog Computing ◽  
Industrial Applications ◽  
Data Offloading ◽  
Parallel Task

Guest Editorial Fog Computing for Industrial Applications

2018 ◽  
Vol 14 (10) ◽  
pp. 4481-4486 ◽  
Author(s):  
Lei Shu ◽  
Gerhard P Hancke ◽  
Der-Jiunn Deng ◽  
Chunsheng Zhu ◽  
Mithun Mukherjee
Keyword(s):  
Guest Editorial ◽  
Fog Computing ◽  
Industrial Applications

scholarly journals Data Offloading for Effective Rescue Operations During Fire Disaster Management

2019 ◽  
Vol 9 (1) ◽  
pp. 5319-5321
Keyword(s):  
Disaster Management ◽  
Data Analytics ◽  
Preventive Measures ◽  
Fog Computing ◽  
Data Offloading ◽  
Loss Of Life ◽  
The Past ◽  
Fire Disaster ◽  
Moving Vehicles ◽  
Post Disaster

Disasters such as fire, earthquake and tsunami, etc. often result in precious loss of life as well as pose great economic challenge to developing countries like India. Of late, fires in moving vehicles such as trains and buses have become very common in India leading to loss of life and property. As we cannot predict or control the disaster, we can at least make efforts in minimising loss and some effective rescue operations post disaster. It is very important to perform post disaster analysis of the event to have better rescue operations and also to analyse the reason behind the occurrence of such disaster if possible. So that if it is man-made disaster preventive measures could be prescribed in future. Many Wireless Sensor Networks were proposed in the past for disaster management. But, fire leaves the network disconnected and important data is left unused in the damaged network. So, we propose Internet of Things (IoT) which is a promising technology that can be used to solve some of the problems mentioned above. To date, the application of IoT in post-disaster management is still an unexplored problem. In this paper, we propose data offloading mechanism for effective rescue operations post disaster from damaged network. The network is partially damaged in case of disaster and using fog computing we retrieve data and transfer to cloud for better data analytics. We propose a data flow framework built for effective post-disaster management.

A Lightweight Autoscaling Mechanism for Fog Computing in Industrial Applications

2018 ◽  
Vol 14 (10) ◽  
pp. 4529-4537 ◽  
Author(s):  
Fan-Hsun Tseng ◽  
Ming-Shiun Tsai ◽  
Chia-Wei Tseng ◽  
Yao-Tsung Yang ◽  
Chien-Chang Liu ◽  
...  
Keyword(s):  
Fog Computing ◽  
Industrial Applications

scholarly journals Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs

2019 ◽  
Author(s):  
Andrea Tassi ◽  
Ioannis Mavromatis ◽  
Robert Piechocki ◽  
Andrew Nix ◽  
Christian Compton ◽  
...  
Keyword(s):  
Fog Computing ◽  
Data Offloading ◽  
Computing Infrastructure

scholarly journals OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

2022 ◽  
Vol 2022 ◽  
pp. 1-15
Author(s):  
Sávio Melo ◽  
Felipe Oliveira ◽  
Cícero Silva ◽  
Paulo Lopes ◽  
Gibeon Aquino
Keyword(s):  
Smart Cities ◽  
Fog Computing ◽  
Data Offloading ◽  
Resource Limitations ◽  
Significant Step ◽  
Open Issue ◽  
Compression Data ◽  
Iot Devices ◽  
Definition Of ◽  
And Storage

IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.

Classification Aspects of the Data Offloading Process Applied to Fog Computing

2021 ◽  
pp. 340-353
Author(s):  
Sávio Melo ◽  
Cícero Silva ◽  
Gibeon Aquino
Keyword(s):  
Fog Computing ◽  
Data Offloading

scholarly journals On the Combination of Multi-Cloud and Network Coding for Cost-Efficient Storage in Industrial Applications

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1673 ◽  
Author(s):  
Goiuri Peralta ◽  
Pablo Garrido ◽  
Josu Bilbao ◽  
Ramón Agüero ◽  
Pedro Crespo
Keyword(s):  
Network Coding ◽  
Fog Computing ◽  
Industrial Applications ◽  
Strong Impact ◽  
Cloud Infrastructure ◽  
Efficient System ◽  
Storage Cost ◽  
Redundant Data ◽  
Cost Efficient ◽  
Multi Cloud

The adoption of both Cyber–Physical Systems (CPSs) and the Internet-of-Things (IoT) has enabled the evolution towards the so-called Industry 4.0. These technologies, together with cloud computing and artificial intelligence, foster new business opportunities. Besides, several industrial applications need immediate decision making and fog computing is emerging as a promising solution to address such requirement. In order to achieve a cost-efficient system, we propose taking advantage from spot instances, a new service offered by cloud providers, which provide resources at lower prices. The main downside of these instances is that they do not ensure service continuity and they might suffer from interruptions. An architecture that combines fog and multi-cloud deployments along with Network Coding (NC) techniques, guarantees the needed fault-tolerance for the cloud environment, and also reduces the required amount of redundant data to provide reliable services. In this paper we analyze how NC can actually help to reduce the storage cost and improve the resource efficiency for industrial applications, based on a multi-cloud infrastructure. The cost analysis has been carried out using both real AWS EC2 spot instance prices and, to complement them, prices obtained from a model based on a finite Markov chain, derived from real measurements. We have analyzed the overall system cost, depending on different parameters, showing that configurations that seek to minimize the storage yield a higher cost reduction, due to the strong impact of storage cost.

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