The Edge of IoT

The global growth of the Internet of Things (IoT) continues unabated.  While the projections vary from source to source, the current thinking is that by 2022 there will be 29 Billion devices connected.  The original forecasts for 2025 were expected to hit around 50 Billion devices, but recent updates from the same sources now forecast about 75 Billion connected devices worldwide.  The growth of IoT is somewhat astonishing.

One of the catalysts for IoT is expected to be the proliferation of edge computing.  Edge computing pushes the intelligence closer to the edge of the network fabric.  While IoT does not need edge computing as mandatory, it does greatly enhance the capabilities and performance of IoT so it is anticipated to be one of the more powerful influences for the adoption of IoT applications in the future.

With compute, storage, and analytics collocated at the edge of the network, the speed of data processing is dramatically accelerated.  With IoT mesh networks now overtaking the classic IoT star topologies, we are seeing much more peer-to-peer connectivity.  With shorter routing paths between end nodes, relay nodes, and take-out nodes, the latency is in the sub 10 ms range when edge computing is a part of the solution.  The data rates for IoT are climbing too.  As is the overall robustness of these IoT networks.  All of these enhancements spell success and the opportunity to deploy new applications that might not of been possible previously without edge computing.

These IoT innovations are now permitting IoT solutions to be better fit for the applications and to show significant improvement in performance and capability.

While the centralized cloud servers are still used, the edge computing has the ability to shift the paradigm with more data residing on the network fabric then in the centralized cloud servers.  The ISO / IEC version of edge computing is now being referred to as ‘cloudlets’, which are baby clouds on the network fabric that are in close proximity or at the edge nearer the end sensors.  These cloudlets can act as stand alone computing and storage locations independent of the core clouds.  Or, they can collaborate upstream between the cloudlets and the clouds.  As well, they can function laterally around the edge with cloudlet to cloudlet communications.  With the older star topologies, most or all of the data had to be transported upstream to the cloud for repository.  However, by 2025 it is projected that over 50% of all IoT data will reside at the edge on these cloudlets.  If data does need to be transported to the centralized cloud, then it will likely be in a lessor form, as derived data, and not as raw data that we see today.  The outcome of this shift in where data resides means less traffic burden on the network itself.  Resulting in less congestion and improved latency performance.

Security of the data will be essential since it will now be federated on the nodes, in the cloudlets, and also on the clouds.  With your data housed in so many locations it is critical that the security be federated and mapped to the IoT topology too.  Security will be in the form of the Zero Trust model and not the older centralized design.

Artificial Intelligence (AI) will play a major role in these federated networks for traffic management, security, and time-to-live attributes of the data.  Routing data traffic will need AI to provide the visibility to the network resources due to the new levels of complexity that these next generation networks possess.  Peer-to-peer routing is a vital attribute of future IoT networks, so AI will allow the network to operate at a grader scale, by providing a view of this huge topology, it will allow this big data flows to be controlled and directed as required for it all to work properly.

With all of these improvements, the edge will bring the data to life and make possible new applications that were previously not feasible.  IoT will get a major boast from the arrival of 5G cellular technology too.  The 5G model is also federated and has many of the same characteristics as IoT.  So, it will be possible to share the IoT edge resources with the 5G edge resources.  IoT can be extended off of the end points of the 5G CAT-M1 and NB-IoT offerings.  Placing an IoT mesh network at the end of these new star model 4G / 5G cellular connection types will allow for a dramatic improvement.  While IoT and 5G can live apart from each other, they are both greatly improved and made more capable with each other in collaboration.

Microservices is another game changing technology to make this edge computing come to life.  The days of the large monolithic application sitting on a server farm in a private or public cloud are nearing an end.  With a modular building block approach to assembling these next generation applications, they can be federated over the network, at the edge, and at the centralized cloud too.  Different modules can exist at different places in the topology.

While blockchain has not achieved the dramatic dreams once envisioned, it still has a major role to play with both IoT and 5G.  Blockchain may have evolved too fast with too little benefit, but it will likely still see its heyday soon as an essential element of the federated architectures used for IoT and 5G.  It will protect transactional data and help with the privacy needs for most users.

Applications for smart cities, automation of factory floors, healthcare, mining, oil, and gas production, autonomous vehicles, smart grids, mobile entertainment, and wearable technologies, to name but a few, will all be further developed as a result of network federation and edge computing.  Most applications will not need centralized data to exist and they will be greatly improved if the data only resides at the network’s edge closer to the end user.

Microsoft announced in April it would invest $5 billion in Internet of Things-related technologies – of which, edge computing is a major component.

“One sector that stands to benefit immensely from edge computing is agriculture“, Microsoft said.  Farmers usually have to perform manual inspections of hundreds or thousands of acres of crops to assess which areas of soil are under-performing and in need of more or less water or chemical spray.  Computing resources have historically been cost prohibitive, especially in remote areas where connectivity is unreliable.

“You figure these things out by marching people through fields and having them do things the hard way, which means the rate at which you can get information is slow, and the notion that you can do things in precise ways is sort of unimaginable,” says Microsoft Corp. Chief Technology Officer Kevin Scott.

Some farmers, though, are now experimenting with a new architecture using Microsoft services in which data about soil moisture and pests, for example, is gathered from sensors and cameras in the field and then analyzed with machine-learning algorithms located on or near the farm, at the edge of the network.

In 2017, Schneider Electric SE began experimenting with Microsoft Corporation’s Azure IoT Edge, which connects devices in the field to gateway hardware that is an extension of its public cloud.  Schneider is using the service to predict costly mechanical problems with rod pumps, which extract oil in remote locations where wireless connectivity isn’t widely available.

“Any of these agricultural or industrial applications, where you have very manual processes like inspections, or humans gathering data … (that’s where) the intelligent edge is going to make those industries massively more efficient,” Mr. Scott said.

“In recent years, advancements have been made in the quality and affordability of sensors as well as the cloud-based platforms required to gather and analyze data being streamed from devices in the field.  But challenges still remain before edge computing becomes widespread,” Mr. Scott said.

Regardless of the industry or the applications, edge computing will bring the data alive and make it perform in new and interesting ways to drive value and reduce manual labour.  It is this core focus to dramatically enhance the level of automation that saves money, reduces time, cuts labour, and enhances quality to provided the business rationale to use edge computing.  The entire business proposition is about to be turned upside down with the advent of edge computing.

About the Author:

Michael Martin has more than 35 years of experience in systems design for broadband networks, optical fibre, wireless, and digital communications technologies.

He is a business and technology consultant. Over the past 15 years with IBM, he has worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).

Martin currently serves on the Board of Directors for TeraGo Inc (TGO: TSX) and previously served on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). 

He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology.

He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now OntarioTech University] and on the Board of Advisers of five different Colleges in Ontario.  For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section. 

He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and five certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has earned 15 badges in next generation MOOC continuous education in IoT, Cloud, AI and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, and more.