IoT solutions usually need to be learning solutions

 The value in LoRaWAN solutions comes from allowing people to take actions to address issues with things or control things. In many cases while people know what they want to achieve in a business sense they often can not initially be as precise about: exactly what data they need, at what frequency, with what accuracy, with what fidelity; how that data is best converted into meaningful information that can be used by business or non-technical people, to affect what they actually do. 

So systems need to be set up initially to learn about these things and then continually be refined to achieve an optimal solution. This will often require evolving sensors, devices, fittings, on device logic/AI, device messages, network components, back end systems logic, scoring, thresholds, etc. It is effectively an agile and iterative process but it needs to take place across: hardware, firmware and software.

Too frequently people try too early to fix an aspect of the system and then work around that as a fixed constraint. What is needed initially is a solution that can capture information and allow it to be analysed in a range of ways e.g. using basic instrumentation, analyticals tools and allowing the application of data science to filter, aggregate, compare, correlate, interpolate, extrapolate etc. and compare calculated values (actual and predicted) against observed data.

What is learned will allow one to understand how to calibrate sensors (modes, frequency, thresholds etc.), configure devices, develop interpretations, present information, identify incidents that require people's attention.

This has important implications for the overall process and sequencing of solution design and development.  Usually a highly iterative approach will make sense with cycles of: user experience and improvements in data analytics and devices (and within that iterations focused on improving the data, analytics, device). So the systems will be usually be developed as follows:

• Get an initial system operative 

• common business objects and associations that provide initially an information  harness or context for readings and controls. 

• gather data from real world entities in real world environments

• share access to information with expert users

• Analyse the data, rules, sensor parameters, etc and convert data into meaningful information e.g. accurate, reliable, credible etc. 

• diagnose the cause of inconsistencies between what we know of the real world and what the solution says

• use the diagnosis to recalibrate and reconfigure the solution

• Polish user experience so users can get the value they seek in the way they want it

• Operationalise the system

To achieve this evolution, avoid stranded pilots and minimise wasted effort and cost, it is critical that the platform on which the solution is built is end to end and configurable at all points.