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Fog-based use cases are essential to the work of the OpenFog Consortium. They provide invaluable insight into the spectrum of vertical markets and applications served by fog computing and networking, and they also drive requirements for next iterations of the OpenFog architecture and testbeds. Each of the use cases posted here highlights one or more representative fog attributes of SCALE (security, cognition, agility, latency and efficiency).
The architectural use cases provide a technical foundation to plan and design fog implementations as well as extract high-level requirements tailored to specific vertical industries. We appreciate comments on these use cases, which can be sent to email@example.com. Thank you for your interest in fog computing and in our work.
These vertical market use cases, created by OpenFog members and contributors, showcase how fog works in industry. They were designed to provide system architects with a resource that has enough detail to plan and design a fog implementation and extract high-level requirements. Please fill out the form below for access to all of these use case studies.
By leveraging key principles of fog computing that enable processing to take place in close proximity to the robots, robots simultaneous localization and mapping (SLAM) is enabled by high-performance real-time edge processing, optimized analytics, and heterogeneous applications. The SLAM use case speeds up the time to process vast amounts of data required in life-or-death situations such as firefighting or rescue operations. Tasks can be completed in real-time despite the limited onboard processing capabilities of robots SLAM. It also adds efficient, cost-effective technical processes to applications such as surveillance and scientific exploration.
Subsurface imaging and monitoring in real time is crucial for understanding subsurface structures and dynamics that may pose risks or opportunities for oil, gas and geothermal exploration and production. This use case describes an architecture for integrating IoT sensor networks with fog computing and geophysical imaging technology. Fog’s scalability enables real-time computation in remote field locations, including support for complex compute algorithms.
Today’s smart buildings are leveraging the IoT for improved business outcomes, such as better energy efficiency, improved occupant experience, and lower operational costs. They require distributed fog architectures because they typically contain thousands of sensors measuring various building operating parameters such as: temperature, humidity, occupancy, energy usage, keycard readers, parking space occupancy, fire, smoke, flood, security, elevators, and air quality. This use case demonstrates how fog nodes at the room level, floor level, building level and cloud level can be hierarchically architected for efficient real-time processing, enabling dozens of new applications.
Autonomous Driving, which involves hundreds and hundreds of simultaneous data processes and connections, can’t be accomplished without fog. Fog establishes trustworthiness of communications between low-level sensors while enabling high-bandwidth real-time processing. This use case validates how fog architectures for autonomous cars enable significantly greater scalability than any other architecture. Fog interoperability enables on-board equipment to communicate at a variety of hierarchies while providing standard interfaces that will provide a foundation for the fog ecosystem.
With its real-time communications and analytics requirements for data from thousands of low-level sensors, today’s hospital patient monitoring requires the scalability and agility of fog. Fog’s distributed architecture and hierarchical structure are necessary for moment-to-moment healthcare operations. This use case describes an architecture based on a virtual compute environment residing on a series of fog nodes that supports the flexible deployment of applications and streamlines the integration of healthcare systems.
Consumer lifestyles and diversified preferences mean that, in order to meet market demand, food and beverage producers must be able to cope with small-quantity, large-variety products, along with product lifecycles with large fluctuations in demand periods and quantities. Systems need to be securely linked along the supply chain. Fog computing helps process brewing by enabling digital twins of process in order to replicate key functions, enabling fog nodes to scale up or down to meet demand, and ensuring privacy of data.
Today’s sporting events need to broadcast live video from all corners of the arena or race course with zero latency. Fans demand to view real-time action on their mobile devices over a race course that spreads miles over terrain. Hierarchical fog nodes shorten video latency and decrease backhaul bandwidth. The OpenFog architecture delivers the agility to manage video services and video algorithms. These algorithms distribute the video process services in different layers from camera to cloud, according to their different performance requirements.
Commercial drones operate in many environments, from aerial to subterranean. Fog enables near realtime adjustments and collaboration in response to anomalies, operational changes or threats. Fog computing enables drones, as self-aware individual fog nodes, to interoperate and cooperate as a dynamic community. Fog allows for interoperability to make it practical for drones from many different vendors to share the skies safely without adding layers of proprietary communications, interfaces and networks. The fog infrastructure enables architects to efficiently distribute services across compute, storage, networking, security, and other functions. This detailed architectural use case discusses the technical architecture necessary for drone operations.
There are still significant roadblocks to wide-scale acceptance of Autonomous Driving (AD) vehicles. AD is a mission-critical application. Precise operations during every millisecond of drive time can have life-and-death consequences. But just consider the amount of information that has to be collected, shared, analyzed, and then acted upon in order for every vehicle on the road to execute the right action at precisely the right moment. Fog computing enables the critical functions for AD vehicles to collaborate, cooperate and utilize the underlying infrastructure to coordinate their operations within smart highways and roads and smart cities. Read the executive summary.
Monitors are used in operating and emergency rooms and in many types of units, such as intensive care, cardiac care, and others. Over the years, the healthcare industry has continued to see advances in monitoring and a wider range of applications (such as monitoring infusion pumps for IV drug delivery). While monitoring technology has continued to improve, systems are still relatively siloed. Hospital error is the third-leading cause of death each year in the U.S. Many deaths are due to the lack of integration between medical devices monitoring a patient. Hospitals recognize the tremendous benefits associated with sharing information between systems. By networking monitoring systems to more medical devices and healthcare subsystems, these siloed systems can share data and enable more rapid and safer response to changes in a patient’s condition. Read the executive summary.
Subsurface imaging and monitoring in real time is crucial for understanding subsurface structures and dynamics that may pose risks or opportunities for oil/gas exploration and production, civil infrastructure, etc., while minimizing environmental impact. This use case examines a new approach to real-time subsurface imaging through a mesh network of fog nodes. Download the use case.
Today’s smart buildings are beginning to leverage the Industrial Internet for improved business outcomes, such as better energy efficiency, improved occupant experience, and lower operational costs. The fog computing architecture gives smart building technology suppliers the flexibility to collaborate with their customers to create more targeted, outcome-based solutions. By moving computation, networking and storage to locations within the building, it removes the constraint of operating entirely at the edge with no long-term learning abilities, or entirely in the cloud with Inadequate real-time responses. This will help address the high volume of untapped opportunities in the market. Download the executive summary.
Fog computing provides the foundation for the IoT-based smart factory. A fog-based infrastructure is capable of collecting the wealth of sensor data distributed among brewery equipment and aging vessels. In a fog environment, even the techniques used by experienced beer crafters can be digitally recorded and captured as data, to be repeated exactly in subsequent batches. This short executive summary focuses on fog computing in a precise manufacturing environment: a craft brewery. Download the executive summary.
The concept of using drones—also known as flying robots and unmanned aerial vehicles (UAVs)—is gaining a lot of attention. Drone fleets can reduce costs, congestion and environmental impact to a degree that no one would have imagined possible. Yet there are also challenges to deployment: Network bandwidth and availability, drone hub management, regulatory considerations and more. This use case examines the advantages, requirements and restrictions of drone package delivery in a fog computing environment. Download the use case.
Traffic congestion is such a severe and growing problem that it has the potential to paralyze major cities, choking off growth and prosperity. Some cities are taking what commuters may consider drastic and expensive measures, like expanding toll roads or restricting the number of license plates issued.
The open architecture of fog computing gives municipalities a new weapon in the fight against traffic congestion. Fog has the flexibility to leverage traffic-related big data, which enables municipalities to take measures to alleviate congestion by connecting and analyzing previously unconnected infrastructure devices, roadside sensors, and on-board vehicles devices, in order to redirect traffic based on real-time data. Review the OpenFog Use Case – Traffic Congestion
Surveillance and security cameras are being deployed worldwide in record numbers in order to ensure the security and safety of materials, people, and places. These surveillance devices are generating massive amounts of data, with a single camera generating in excess of one terabyte of data per day. Systems of surveillance devices generate data that must be analyzed in real time in order to ensure public safety. Traditional cloud-based models to analyze the data are no longer adequate due to latency challenges, network availability and the cost to transmit data to the cloud and back. This use case explores the use of fog computing in surveillance scenarios. Download the use case.
At today’s sporting events, fans are demanding high-quality, real-time video on their smartphones and tablets. Events can take place in a stadium, across multiple venues (like the Olympics) or outdoors. Even outdoor events vary—from a fixed track to races that start in one location and end many kilometers – even days – away. Throughout these areas, broadcasters use High-Definition (HD) cameras to live stream coverage. These cameras generate massive amounts of video data. Fog computing keeps the video processing local, eliminating latency and congestion issues and eliminating the need to provision expensive backhaul links. With fog, processed and filtered video can be sent to the cloud for long-term storage. Download the executive summary.
Fog computing & networking is an emerging and exciting field for researchers around the world. These pages highlight demonstrations and research papers from the universities who are leading the way in fog advancements.
We are honored to include the research from fogresearch.org on these pages.
To contribute to these pages, please contact us at firstname.lastname@example.org.
In March 2017, the OpenFog Consortium commissioned 451 Research to rigorously investigate the market size and impact of fog computing, including the current spend in fog and the five-year outlook across the globe. 451 Research also looked at cloud vs. on premise spend, trends over time, and the industries where fog will have the biggest impact.
As pioneers in fog computing, OpenFog members are keenly aware that fog computing is a large market – after all, fog is necessary for IoT, 5G, and embedded AI. With the 451 Research report, we now have an independent, thoroughly researched data set to validate our work.
As we anticipated, the research revealed an industry with enormous upside that already has established market traction: Fog computing will be an $18 billion market worldwide by the year 2022. The report also investigates:
We invite you to read the 15-page report and see the highlights on the infographic. We also invite you to join in our work. OpenFog members are building an open, interoperable architecture for fog computing that will become the foundation for creating standards and will be the body that determines compliance and interoperability to the fog standards. For more information on our work, please view the OpenFog Reference Architecture and our latest use cases.
Interested in learning more? Please contact us for a conversation.
This report is copyrighted by 451 Research and is distributed with permission by OpenFog. Reproduction and distribution of this publication, in whole or in part, in any form without prior written permission is forbidden.
Please fill out this form to download the full report. Thank you for your interest in the market opportunity for fog computing.
OpenFog members include some of the leading universities from around the world who are doing pioneering research in the area of fog computing and networking. This repository contains links to selected work by members. For more information about this work, please contact the researchers directly or contact OpenFog at email@example.com.
Out of the Fog: Viewpoints from OpenFog Members (5-7 minute videos):
Fog World Congress 2017
Introductory Overviews on Fog Computing