Why are we using LoRaWAN?

When we started Beringar we spent a lot of time with clients in the health sector understanding their space utilisation needs. We discovered that to use IoT in the National Health Service in the UK we would have to find a way to get the data out without touching the buildings data infrastructure. So, no wired or Wi-fi connections were possible, partly due to data security concerns and partly due to spotty Wi-Fi that sometimes was there and worked and sometimes did not. A constraint was introduced to our way of working that we had to overcome.

As we have discovered, constraints are our friend. They force us to think of inventive ways to overcome them and usually lead to a better product in the end. This constraint led us to focus on LPWAN technologies as a potential data transmission solution (in turn leading to new constraints around data packet size) and to prototyping using LoRaWAN.

Why LoRaWAN? Well, it was a technology that we could find willing experts to help us with and had the benefit of flexibility compared to SigFox. We wanted to send data every minute and SigFox would not allow us to do that. LPWAN technology also had the benefit of great range and the ability to work from deep in the bowels of a building. Something that we knew that 3G or 4G would not be able to cope with from our initial tests. This choice meant we could get the data out of the building securely and reliably.

Making the choice to work with LoRaWAN meant that we needed to really focus on the data that was important and package it in a way that could let us fulfil the demand to count and position people in a building, report the environment and understand space utilisation. All in a few bytes transmitted once per minute. As I mentioned, constraints force creativity, so we found a way to package this information in a few different message types and stayed within the duty cycle limits for LoRaWAN. This choice reduced our data transmission, storage and management headache a lot.

The other benefit of LoRaWAN, as we have discovered, is the ability for us to be in control of the deployment. We choose how many gateways and when they will be installed. We manage the network and ensure that we deliver a high quality service. This is important in the overall end to end service we deliver to the customer. No waiting for a telco to roll out in that area and no service provider help desk to worry about if something is not right. This choice helped with speed to market and quality of service.

So LoRaWAN has challenges - its radio being the principle one. Doing large scale firmware updates is not trivial and we always have the issue of occasional dropped packets - but the pros outweigh the cons. It’s fast to deploy; you’re in control; it’s quite reliable; it has great range with low cost hardware and you can remain independent of the large carriers. There is a lot to like.

What do you use to get data out for your IoT deployment and what are the pros and cons?


Main image credit: Ryan Stefan via Unsplash

Finding the money to go smart

At Beringar, we realised from the very start that the benefits of smart technology in real estate were a combination of quick wins, medium term returns and long term transformations. That's why we launched our sensors as a service model last May. Today, it seems Siemens want to go further to offer their clients a way to procure entire smart buildings as a service, either as new builds or by retrofitting existing buildings. This is an exciting development, especially if backed by a major corporation with deep pockets.

It does, however, throw up a number of important questions, some of which can be answered by the experiences of public-private partnership or public finance initiative projects (PPP/PFI). In these projects the private sector make a return for the transfer of risk of developing infrastructure on behalf of (usually) public sector clients. The contracting process is long, expensive and complex, but the results are a risk free high quality building from which to operate for up to 25 years. One of our co-founders, Paul Byrne, has 15 years experience in this market and is one of the UKs foremost experts on modelling this type of deal. At the end of the day, it breaks back to a complicated financial model that spells out the risk and reward for each party to the contract. 

We would love to see some of the great tools created to manage PPP/PFI contracts be applied to smart building procurement - this such as clear payment mechanisms and comparators studies. These would allow more transparency and enable all concerned to see you the huge upside to going smart.

We stand ready to help. Waiting for your call Siemens 😀 

Do we need smart construction to get smart buildings?

In a report dating back to June 2016, McKinsey & Company looked at the changes needed to improve productivity in the global construction industry (Imagining construction’s digital future). The industry employs 7% of the global workforce, and with a staggering $10 trillion spent each year on new infrastructure, even tiny changes in productivity will have marked effects on both jobs and sector value. But, according to figures presented by Antony Slumbers in a tweet today, the construction industry spends around 1% on R&D compared to tech companies like Amazon who spend 12%. Clearly, there is scope to spend more to achieve greater productivity returns.

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In our view, truly smart buildings are started on the drawing board. So, if the necessary step of getting them out of the ground is “dumb”, how can we possibly create a smart building? How can we know, for example, that it was built according to the plans; with the materials specified; in weather conditions that were optimum; by people that knew what they were doing, and; were safe while doing it? What impact would this data have on the ongoing use, management and maintenance of the asset? Do we have the spec sheet and is it performing according to spec? If it were a software or hardware package we would, so do we need the same for our buildings along with the mechanisms to check performance against spec?

We think we do, with digital platforms and sensors used before, during and after construction will provide that certainty and a whole lot more besides. The five trends highlighted by McKinsey are really all about platform and sensing capabilities for construction that generate data and insights that can be used to accelerate the build and/or increase the durability of the final output.

Think use pattern feedback; materials durability; occupant satisfaction; repair rates; scheduled vs unscheduled maintenance; thermal and acoustic performance; energy consumption; operational costs. Imagine these statistics being available to the designer, construction firm, lender, developer, owner, occupier and user of the building and think about how quickly innovation would happen once the building is smart from inception.

Can IoT answer real (estate) questions?

If we assume for a moment that more data is a good thing, then having the ability to create more data must also be good right? "What gets measured gets managed" goes to mantra. But assuming just having measurements will impact on productivity might be a little naïve. Perhaps it's what we do with those measurements that is more important.

Given that real estate is the most valuable asset class on Earth, one would assume we would swimming in data and insight about how buildings perform - how else can we decide to build them, rent them or value them? But sadly, the actual use of buildings is a mystery. Apart from fairly simplistic headcount information not much is known about how a building is used, but this is something that IoT has the potential to change. New devices that allow building occupiers, owners, funders and designers to look inside their buildings and understand the true demand for space of different types in real time can help make better, faster real estate decisions.

 For the occupier it might help answer whether they have too much space. Real time demand data.

 For the owner it might help answer whether the configuration of space is optimal. Real time use pattern data.

For the funder it might help answer whether their investment is in demand on a daily basis.  Real time asset performance data.

For the designer, it might help to answer what there next building should include more or less of. Real time design feedback data.

In each of these cases, the core data is the same, but how it is analysed and interpreted can make it useful in many ways. 

 So, as always with data analysis, start by working out your role and your important question. Start with the outputs. There is a way to get the data you need to answer it. That's the power IoT provides today.