Resources

• OpenFog Architecture Overview (published February 2016)
• OpenFog Reference Architecture Technical Paper (published February 2017)
• OpenFog Security Approaches and Requirements (October 2017)
• OpenFog Glossary of Fog Computing Terms (January 2018)

• Fog and IoT: An Overview of Research Opportunities  by Dr. Mung Chiang, Princeton University; and Dr. Tao Zhang, Cisco System
• From Cloud to Fog and The Internet of Things by Michael Enescu, Co-founder at Energy Adaptive Networks
• Fog Computing and Its Role in the Internet of Things by FLAVIO BONOMI, ET AL.
• Fog Networks by Mung Chiang, OpenFog Consortium Board Member and Arthur LeGrande Doty Professor of Electrical Engineering at Princeton University
• Fog Networking: An Overview on Research Opportunities by Mung Chiang, OpenFog Consortium Board Member and Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University
• Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are; Author: Cisco

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.

Use Case: Subsurface Imaging for Oil & Gas Exploration
Vertical: Energy

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.

Use Case: Smart Buildings
Vertical: Smart Spaces

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.

Use Case: Autonomous Driving
Vertical: Smart Transportation

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.

Use Case: Patient Monitoring
Vertical: Smart Healthcare

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.

Use Case: Process Manufacturing – Beverage Industry
Vertical: Smart Factories

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.

Use Case: Live Video Broadcasting – Sporting Event
Vertical: Entertainment

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.

Please fill out the form below for access to all of these use cases.

Traffic Congestion 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.  Download the use case.

High-Scale Package Drone Delivery

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.

Real-time Subsurface Imaging

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.

Traffic Congestion

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

Video Surveillance

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.