Fog computing middleware for distributed cooperative data analytics

2019 ◽  
pp. 117-137
Author(s):  
Maria Valero ◽  
Jose Clemente ◽  
WenZhan Song
Keyword(s):  
Data Analytics ◽  
Fog Computing

Fog Computing Realization for Big Data Analytics

2019 ◽  
pp. 259-290 ◽  
Author(s):  
Farhad Mehdipour ◽  
Bahman Javadi ◽  
Aniket Mahanti ◽  
Guillermo Ramirez-Prado
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics

scholarly journals Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

2018 ◽  
Vol 5 (2) ◽  
pp. 677-686 ◽  
Author(s):  
Jianhua He ◽  
Jian Wei ◽  
Kai Chen ◽  
Zuoyin Tang ◽  
Yi Zhou ◽  
...  
Keyword(s):  
Data Analytics ◽  
Large Scale ◽  
Smart Cities ◽  
Fog Computing

scholarly journals Fog Computing: An Overview of Big IoT Data Analytics

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Rizwan Anawar ◽  
Shangguang Wang ◽  
Muhammad Azam Zia ◽  
Ahmer Khan Jadoon ◽  
Umair Akram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Key Characteristics ◽  
Challenges And Opportunities ◽  
Cloud Framework ◽  
Iot Devices ◽  
Health Care Monitoring ◽  
Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

Expounding the Edge/Fog Computing Infrastructures for Data Science

2018 ◽  
pp. 1-32 ◽  
Author(s):  
Pethuru Raj ◽  
Pushpa J.
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Analytical Methods ◽  
Data Analytics ◽  
Data Science ◽  
Storage Systems ◽  
Fog Computing ◽  
Next Generation ◽  
Distributed Source ◽  
Real Time Applications

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.

IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

scholarly journals Resource Aware Placement of Data Analytics Platform in Fog Computing

2016 ◽  
Vol 97 ◽  
pp. 153-156 ◽  
Author(s):  
Mohit Taneja ◽  
Alan Davy
Keyword(s):  
Data Analytics ◽  
Fog Computing ◽  
Resource Aware

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.

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