How Fog Computing Helps Businesses Fully Encash IoT Benefits

What is Fog Computing?
Fog Computing helps extend the cloud by bringing it closer to the things (the edge devices) that actually produce IoT data in the first place and also act on it. Fog nodes, which sit between the cloud and smart device (thing), can be deployed anywhere with a network connection close to the smart device where actual data generation or end outcome takes place. Example of this can be factory floor, on top of a power pole, alongside a railway track, in a vehicle, or on an oil rig. A Fog nodes can be any device with computing ability, storage capacity and and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras.

Why Fog Computing Now?
IoT devices generate data constantly and in gigantic volumes. Often analysis must be very rapid. Some IoT use cases are as follows
  • Automatic locking of doors in case of security emergency
  • Applying autmatic brakes on a vehicles or trains in emergencies
  • Zooming a video camera for detecting anomalous activities 
  • Automatically opening a valve in response to a pressure reading
  • Sending an alert to a technician to make a preventive repair. 
In the time it takes user data to travel from the edge to the cloud for analysis, specially during network latencies or breakdowns, catastrophic events may result. Handling the volume, variety, and velocity of IoT data in such cases requires a new computing model. Fog Computing offers a solution with following benefits.
  • Minimize latency: As we saw in the above examples, even milliseconds matter when you are trying to prevent an emergency. Analyzing data close to the device that collected the data can help minimize this latency and make the difference between averting disaster and a cascading system failure. While big data analytics on historical data needs the computing and storage resources of the cloud, extremely time-sensitive decisions should be made closer to the things producing and acting on the data. 
  • Reducing network bandwidth consumption: The edge devices in IoT generate gigantic volumes of data. For example, offshore oil rigs generate 500 GB of data weekly, commercial jets generate 10 TB for every 30 minutes of flight. It is not practical to transport vast amounts of data from thousands or hundreds of thousands of edge devices to the cloud. Also, many of the critical analyses may not even require cloud-scale processing and storage.
  • Address privacy and security concerns: IoT data needs to be protected both in transit and at rest. Many times industry regulations and privacy concerns also prohibit offsite storage of certain types of data. Fog Computing Model protects your fog nodes using the same policy, controls, and procedures you use in other parts of your IT environment. This way you can analyze sensitive data locally instead of sending it to the cloud for analysis. Your IT team can monitor and control the devices that collect, analyze, and store data.
However, it does not mean cloud layer is not required. It has its own role in running deeper analysis and creating insightful and actionable real time analytics and control application rules for Fog nodes to function in real time. To set everything in perspective], here is a summary of what role each computing layer plays in overall IoT ecosystem.

[Note: The post is based on information and notes from the Cisco whitepaper: Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are]