IIoT Needs Both Edge and Fog Computing

Jun 13, 2018 | 3960 Views

Industrial Internet of Things needs scalability and flexibility so the future of IIoT needs both fog and edge computing because differentiating between these two technologies can be a challenge for enterprise and industrial companies. These two terminologies are often used interchangeably as they both involve pushing intelligence and processing capabilities. 

What is Fog Computing?
Fog computing was originally coined by Cisco it is just closer to edge computing.  Fog is a form of cloud computing that can manage large amount of data without pushing the data out to the cloud. It improves the capabilities of the edge by handling real-time IoT requests between the edge and the data center cloud. 

Fog networking supports the Internet of Things (IoT), in which most of the devices used by humans on a daily basis will be linked to each other. Like it includes phones, wearable health monitoring devices, connected vehicle and augmented reality (AR) using devices such as the Google Glass. Fog computing is used in the traffic light system and it can change its signal based on surveillance of coming traffic to avoid accidents and diminish congestion. Data is also sent to the cloud for long-term analytics. Another use case of fog computing is in rail safety, power backup from networks and cyber-security. 

Fog computing helps in creating new applications and services that cannot be easily maintained by the current host-based and cloud-based application platforms. New fog-based securities services will be able to address several challenges to secure the Internet of Things. 

What is Edge Computing?
Edge computing allows devices to process data locally and makes local decisions. This reduces the load on the network. It also reduces the round trip time for data to travel back and forth and is significant for making real time decisions in IoT products. Edge computing provides real-time data and analytics to optimize performance for the IIoT and the future of industrial automation. It covers a wide range of technologies including wireless sensor networks, mobile data acquisition, mobile signature analysis etc. 
For organizations whose infrastructure is more individualized and secular then deployment of fog computing is not necessary, but understanding of both the computing and implementation is necessary to a successful IIoT strategy. 
Deploying for IIoT:
After understanding the different applications and scalability for both edge and fog computing it makes easier to determine that which the best fit technology in particular situation. In industrial settings, edge networks are sufficient to service the data and analytics needs of multiple facilities in a system. But for industries preparing to scale greatly, fog may be a better option to implement for long-term growth and success. Appling edge computing is the next step towards IIoT that will create facilities for the future of automation. The need to optimize efficiency, productivity and quality are pushing manufacturers to move with intelligence to process data faster and respond to competitive pressure. Fog is somehow related to the edge, which means it can manage real-time IoT processing requests faster and reduce latency in sharing data between networks. Outside of industrial networks, fog computing can help diverse industries and sectors scale to connect thousands or millions of edge devices.
Hence, the future of IIoT needs the both fog and edge computing. As the capabilities of IIoT grow, more networks will be connected and scale up to service our everyday needs, creating an even greater network of smart homes, buildings, and cities.

Source: HOB