By Syed Zaeem Hosain, Founder and CTO of Aeris.
Getting your first few connected devices to market can feel like a monumental accomplishment. Then scaling your deployment to thousands, tens of thousands, and hundreds of thousands of devices introduces a whole new set of challenges: In this context, how do we define “network and system intelligence”? How do we assess what value it is bringing? Is it artificial intelligence (AI)? Is it machine learning (ML)? Is it business intelligence?
At Aeris, we define an intelligent IoT network as a network that optimises resource utilisation and minimises manual approaches to help customers achieve successful IoT programs.
Fundamentally, this means using intelligence to help our customer’s IoT solution
maintain or improve performance while also maintaining or improving their program’s return on investment.
An intelligent system seeks to understand what is happening within and takes actions to maintain or improve performance. This awareness is continually providing information and recommending actions to assist humans in optimizing their IoT systems.
- Growing your service delivery and support teams
- Introducing new and more advanced products and features
- Securing your IoT applications and devices
- Managing costs as you expand internationally
All of this makes it incredibly difficult to deliver the intended value of IoT-connected services in a timely way. As programs scale with more and more devices connecting to the network, it’s evident that to maintain or improve the performance of IoT applications, it requires observing and understanding vast amounts of data, not only at the application level but throughout the entire chain of IoT—devices, network, and backend/application servers.
Taking these challenges into account is important as you assemble your requirements and shop for an IoT connectivity provider.
What is Intelligence?
In this context, how do we define “network and system intelligence”? How do we assess what value it is bringing? Is it artificial intelligence (AI)? Is it machine learning (ML)? Is it business intelligence?
At Aeris, we define an intelligent IoT network as a network that optimizes resource utilization and minimizes manual approaches to help customers achieve successful IoT programs.
Fundamentally, this means using intelligence to help our customer’s IoT solution maintain or improve performance while also maintaining or improving their program’s return on investment.
An intelligent system seeks to understand what is happening within and takes actions to maintain or improve performance. This awareness is continually providing information and recommending actions to assist humans in optimizing their IoT systems.
Six Progressive Levels of Intelligence
We view intelligence as a progression across six levels—from observability to autonomy. Initially, it may be tempting to cheat in assessing the business value of these levels, but without the intermediate levels, the intelligence is brittle and confined to narrow scenarios.
For example, someone could establish level 1 observability and then apply a simple rule to claim a level 5 prescription. However, without the transparency delivered by the intermediate levels, the value of the prescription is limited.
1.Observability
The basis of all learning is data. Systems cannot be intelligent if they are not created in such a way that behaviors can be observed and data can be collected.
It describes the availability of high- quality telemetry data (system performance information) from a system. Raw observability makes human and software intelligence possible, but without applying higher levels, there is significant human toil required to leverage this data.
2. Reporting
The most basic assistance for intelligence is to collect data and deliver it as a report. The reporting can vary based on being historic versus near real-time and raw versus aggregated. Humans can readily use their own intelligence to draw conclusions from the data and take necessary actions.
3. Description
More useful than mere reporting is to provide a business-level description of the data. The intelligent system turns the data into information that can save humans significant time in drawing conclusions and taking actions.
4. Prescription
The intelligent system not only draws business-level conclusions but also recommends a prescription, i.e. a course of action. Humans can then simply double-check the assumptions and trigger the course of action.
5. Prediction
When intelligent systems provide prediction, they further assist in drawing the correct business-level conclusions more quickly based on what is likely to occur. This allows humans to take action even more quickly and accurately.
6. Autonomy
The autonomous functions of an intelligent system describe what is happening, predict what will happen by taking various actions, prescribe the best action, and then trigger the action to be taken.
Summary
The fundamental premise of an intelligent IoT network as being an intelligent system is that it works continuously on your behalf to maintain or improve security, performance, reliability, and the value of your IoT programs and applications.
IoT deployments will continue to grow in complexity. Relying on manual interventions to support growing demand while addressing complexities will only add to operational inefficiencies.
To that end, delivering a better customer experience, reducing complexities, and achieving business results requires IoT applications to run on an intelligent IoT network.
