IoT Edge Processing

By Gary Audin July 09, 2020

Information processing can reside anywhere in a network. Traditionally it has been in the data center and now the cloud. The Internet of Things (IoT) is forcing a reconsideration of centralized processing. Get ready for a new class of micro data centers that are operated with little or no human involvement. When processing is performed at the network edge near the location of information production, it is called fog computing, “IoT at the Network Edge.”

Is Fog Computing Right for Your IoT?

The Internet of Things (IoT) will involve millions to billions of endpoints in fixed and mobile conditions. The processing of IoT data output can be accomplished in a data center or in the cloud. The response time (latency) of data centers and clouds will not be fast enough for some of the applications such as in vehicles. It is expected that there will a lot of processing in vehicles (cars, buses, trucks, trains, construction equipment…) performed locally that does not depend on the cloud. This processing will be very fast analysis and decision-making measured in milliseconds. There will be interactions with the cloud for processing which can be historical or information such as maps, traffic conditions, advertising…….

There will be situations where there are hundreds of sensors that need to connect to the cloud. In this situation, will be a controller which will connect to these endpoints to the cloud. The endpoints will be sensors and possibly actuators. It is more cost effective to have the controller communicate to the cloud rather than many endpoints. Since there has to be a controller and processing is so cheap, it is likely that the controller will act as an analytic processor for the IoT endpoints. Once the analytic work is done, it can be communicated to the cloud for further processing and actions.

How close is the Data?

Edge processing (micro data center), also called fog computing, is the candidate for many IoT installations. The edge processor collects the raw data, analyzes it, and provides what information is necessary to the cloud. It is reasonable that all the raw data to be transmitted to the cloud. If the raw data transmitted has to be sent to the cloud, it can be done in a file transfer at a later time.

Decisions by the edge processor can be made rapidly and determine whether any measurements require real-time decision making and response. The cloud will be there to analyze what the edge processor does plus to provide historical information but not for extremely rapidly changing conditions.

Real Time or Near Real Time

IoT endpoints communicating to the cloud will produce a lot of data. Some of the data will require actions faster than the cloud can provide. Some of the IoT endpoints will be working in very rapid real-time, milliseconds. When the response time is in seconds, the cloud can be the source of actions and responses. You need to classify the responses into real time (milliseconds) then it has to be done at the edge. If seconds of response times are acceptable, it can to be done by the cloud in near real time.

Processing in the Data Center or Cloud

IoT endpoints will depend on network access and continuity. It can be dangerous and inefficient or even unacceptable for the IoT endpoints to fail because of lost access to the cloud. The IoT endpoints will be dependent upon the edge processor to provide business continuity. I would not want to be driving a car that requires cloud access to deal with my safety. What if the wireless network is just not accessible? Then my safety is in jeopardy.

Edge processors will be very important in providing business continuity when access to the cloud fails. This means a number of decisions have to be made locally for the endpoints. The data may also have to be collected and sent when the cloud access is restored.

Processing at the Edge

The cost of processing has decreased considerably. A laptop at a remote location could act as the edge processor. The real difference will be in the software. There is already software for specific industries and endpoints. Not every business organizations the same. There are APIs available so that customers who own edge processors can develop and install software specific to their business needs. This means that the cloud will also become the central depository for all software and updates.

Security is a concern for IoT edge processors. They will need to be highly secure to ensure that the IoT data is accurate and timely and operate correctly. Security of IoT endpoints may not be good, but security of the edge processor can make up for security limitations.

Micro Data Center Characteristics

The edge processor (micro data center) will probably operate unattended, with little or no human interaction. It will:

  • Have few if any mechanical parts to produce long term reliability
  • Operate in a wide range of conditions (temperature, humidity, vibration…)
  • Have a long operational life consistent with the IoT endpoint life
  • Contain significant battery backup.
  • Be able to safely store data with a loss of power
  • Require low power to operate

Since this is a new device to manage, expanded network, system and application management tools have to be implemented.

Should it be Both Ways, Hybrid?

This is another situation where hybrid processing will be very likely. The processing of data and actions be taken in sub second time periods will be assigned to the edge processor. For those functions that can be performed in seconds, the cloud or data center can perform the work. In the end all the data collected from IoT endpoints will probably be stored elsewhere. The best place for that is the cloud or data center not in the edge processor. Historical information, time consuming analysis, and modified processing analytics of the IoT data should reside in the cloud or data center.

Edited by Ken Briodagh
Related Articles

Will All Enterprise Networks Be LTE/5G by 2030? Some Experts Think So

By: Matthew Vulpis    6/14/2021

Unlike previous generations of network technology that paved the way for innovations like smartphones and wireless broadband, 5G's tremendous improvem…

Read More

Advancing the Orchestration of Distributed Edge Applications, ZEDEDA Integrates with Microsoft Azure IoT

By: Arti Loftus    2/24/2021

ZEDEDA's recently introduced orchestration solution for the distributed edge provides a unique, native integration with Azure IoT, giving developers a…

Read More

Raising the Bar on Edge Computing, ZEDEDA Introduces Industry's First Open Orchestration Solution for the Distributed Edge

By: Arti Loftus    1/29/2021

We are officially in the Infrastructure-as-a-Service (IaaS) world, with the value of evolving ecosystems growing. Proprietary orchestration solutions …

Read More

NXP Expands Scalable Machine Learning Portfolio and Capabilities

By: Ken Briodagh    11/4/2020

NXP makes a strategic investment with Au-Zone Technologies to expand eIQ Machine Learning development environment

Read More

Sequitur Labs Joins NVIDIA Partner Network to Protect the Edge

By: Ken Briodagh    10/28/2020

EmSPARK Security Suite Supports NVIDIA Jetson Edge AI Platform to Address Data and Device Security Needs in the IoT

Read More