A certain amount of computing is incorporated in every IoT Thing. Computing is after all required to translate information available in electrical signal format into data that can be transmitted over a connection to the Internet/Mobile/Business applications associated with a specific IoT implementation. Computing is also required for a Thing to receive data and commands from the same or other applications. Although edge computing is used in every Thing, the amount of processing power available and how it is used is unique for each IoT design dependent on available computing capabilities at the the time the Thing is developed and manufactured. Moore’s law continuously moves price/performance/power requirement curve so that a 3-year-old design is different from today’s design that has more processing at lower price and requiring less power. This is all part of the vibrant field of Embedded computing supported by hundreds of chip, board and solution manufacturers around the world. While edge computing is technically a sub-set of embedded computing it uses the same underlying components and software with the only difference that Edge is also used to connect to the internet and run IoT processing oriented functions, software and applications instead of just ‘embedded control’ ones.
Edge Computing in IoT
What are some of the functions performed by embedded or edge computing in IoT? Let us separate this between Basic Edge Computing functions implemented in all IoT devices –
and more Advanced Edge Computing implemented on IoT devices with more processing power like embedded boards with full OSs (Operating Systems) –
Whether an IoT device supports only basic functions or mode advanced is based on the hardware/software design. Regardless, this processing power can add a lot of capabilities and greatly enhance the value of an IoT implementation if utilized to the fullest.
The Case & Value of Edge Computing in IoT
The common belief that all data is valuable and should be collected and stored for later analysis by trained ‘Data Scientists’ is a fallacy accompanied by overflowing databases with years of unusable information. With little pre-processing, these data lakes (yes, they have a name) can be better described as data swamps where any attempt to coerce information is hindered by the lack of forethought about what information is of interest for the business or users.
Real IoT solutions are about the collection of information that is valuable for the business and Edge Computing is the main vehicle for turning raw IoT data into meaningful information.
Three examples illustrate the point.
As shown in these examples, edge computing can refocus an IoT solution from data collection to information delivery, providing value to all intended users from its inception. Accomplishing this requires a different approach when developing the IoT solution; putting business needs in the driver’s seat.
Implementing the Right IoT Edge Computing
What are some of the functions performed by embedded or edge computing in IoT? It can be helpful to divide them between Basic Edge Computing functions implemented in all IoT devices and Advanced Edge Computing. The former includes tasks such as providing electrical and protocol connection to sources of IoT data on the device (sensors, switches) and translating this data to a data model. It could also include managing IoT connectivity or receiving IoT data sent from IoT applications.
In contrast, Advanced Edge is implemented on devices with more processing power like embedded boards with full embedded OSs (Operating Systems) or IoT specific OSs or Software agents. Advanced Edge can handle processing and transformation of IoT data, filtering of irrelevant or repetitive information, Lambda Functions, Statistical Analysis and even things like machine learning.
So, how do you identify and implement the right edge computing capabilities when developing your IoT solution? These 11 questions can serve as a guide:
IoT Edge Computing provides a way to develop superior IoT solutions delivering derived information to users through applications instead of just transmitting available IoT data hoping it can be mined for information at a later time. In its simplest use, turning data into events dramatically reduces the amount of data sent. In its most advanced form, it delivers ML and AI at the Edge delivering IoT information that can be synthesized and used by the rest of the IoT solution.
Triotos is focused on providing a rapid IoT on-boarding solution complete with applications in months instead of years for a fraction of the cost of doing it yourself. By providing a complete integrated IoT back-end solution using the AWS IoT cloud, our customers can focus on their IoT Business (Applications of IoT Things) and develop the value in record time. As part of this we advise on how to incorporate the right and best Edge Processing solutions and provide simulation tools to speed up development and implementation.