Workspace designers are using OpenSensors’ capabilities to enable their customers to optimise their usage of real estate, smart buildings deliver productivity and improved UX for employees.
Designers turn to IoT technology and OpenSensors’ digital data layer to address the needs of the owners, facilities managers and building tenants. Innovative new IoT technology and OpenSensors’ data reports, alerts and dashboards provide designers with detailed understanding of how people are using the space vs. gut feel on building performance.
For the first time deployment and maintenance of smart IoT sensors have become a cheaper alternative to manual occupancy questionnaires and surveys, sensors can have sampling rates of anywhere between once every few seconds to once every 30 mins. This sensor data can be correlated with information from Building Management Systems (BMS) to provide richer context and considerable more insight than manual surveys. Common interfaces include BACnet, KNX and other major systems. These data not only can be combined with private building data but can also be combined with public data like outdoor pollution.
OpenSensors have built hardware, installation and network provider partnerships and relationships to help architectural firms implement smart IoT devices efficiently. We have found that the most successful IoT projects follow a phased implementation approach: Design Phase, Proof of Concept, Pilot, and Deployment. The design phase asks questions such as which sensors, who will be installing and maintaining the sensors. For Proof of Concept, a lab evaluation should include hooking up 5-8 sensors all the way through a gateway to data collection in the cloud. This will give enough real data to verify that the queries and the analytics are feasible. The Pilot Phase ensures that the sensors work at scale and that the gateway configuration has been made easy for the deployment specialists. A pilot phase should be about 40 sensors depending on the density of the sensors. At this point, you can scale up to the number of sensors and the bandwidth required for full deployment.
Heat maps can help define predictable patterns of usage including peak demand for: * Desks – real-time information of which desks that are in use and which that are available * Conference rooms – Do you have the appropriate amount of meeting rooms, and are they of the right size? * Breakrooms – Where do tenants tend to go and hang out? Are some breakrooms over- or under-utilised? * Corridors and hallways (footfall monitors) – Are some paths through the offices more used than others? Why?
Sensors helps in pitching for new work in a world where people are aware of sensors and how they can drive revenue. Firms who have sensor capabilities have adopted data driven design methods which is replacing gut feel.
Using sensor data enables more accurate planning, and by making it available to occupants, you enable them to both change their behavior and allow them real-time insights and finer customization.
On a daily basis our customers and community ask us to recommend a sensor provider to buy from, you should ping me on email@example.com if you want us to recommend your sensor. Often the requirement is vague, “I need an air quality sensor to put on my street for $100?” or “What sensors shall I use to understand my space usage?”. My process of assessment has grown more refined over time because if the sensors we recommend are unsuitable or unusable our company’s reputation is also on the line by association.
So we have come up with our own unscientific way to rate the quality of a sensor that should be applied simply. Most large scale sensor rollout projects of 1K or more often have these requirements as well. It’s possible that sensor providers that don’t rate highly using our criteria produce good sensors but getting the below right takes iteration and discipline in design and the likelihood is that the provider will a higher chance of being able to deliver.
Battery life If a sensor is battery powered, the typical expected life of battery should be clearly stated. Buyers will often want some explanation of what typical means for your sensor i.e. if it’s a PIR sensor have you calculated battery life based on being triggered once a day? The last thing your customers wants to do is invest in a lot of sensors, plus the cost of installation in order to find out that the battery life is only % of what they expected as it will still cost them a lot of money to rip them out and return them.
Bonus point for sensors that publish their battery status as standard so that the sensor owners can have some warning before changing.
Sensors should tell people whether they are still alive or not periodically. Depending on your battery and connectivity constraints, this can vary, the important thing is that the buyer should not find out a bunch of devices are not working because they haven’t been heard from in days or weeks. Top tip; Heartbeats every 10-60 minutes when possible is sufficient, anymore and it ceases to be informative.
In non consumer environments, the people installing and maintaining sensors are often not the technical design firms or manufacturers. Does your device clearly tell people how to install it, do you have helper applications so that they don’t have to configure firmware? We are working on some solutions for this but more on this later; hint it’s all about enabling people to install sensors efficiently and a non technical installer being able to walk away knowing that the device has joined the network correctly. Does your sensor come with mounting and fittings?
Do people have to unscrew the casing to change batteries? Have you tested this with people and verified it?
Quality in my definition means, is the data from your sensor easily understandable for someone that doesn’t know your domain. The reality is that often manufacturers pass on the analogue value of the particular sensor and that is too low of an abstraction for most people trying to read it. Battery voltage is a good example, during its life an AA battery will go from 1.5v to about 0.8v, but it follows a curve specific to the device and the battery. Understanding how this maps to a percentage or days of life is often complex. If it’s not possible to do much conversions or processing on your sensor or gateway, perhaps a handy explainer when people buy your device making them understand what the data means.
Please state clear terms for warranties and return procedures to protect your consumers. Consumer protection should naturally apply.
Finally developing high quality hardware is hard, I am always amazed at the skill and dedication it takes when hardware designers and engineers take an idea and get it to manufacturing stage. We try to manage the community’s expectations on sensors they should buy vs the attitude of ‘just throw around cheap sensors’. It would be better in terms of environmental sustainability and user experience to get into the habit of doing more with less sensor density. For more on this, see Dr Boris Adryan’s excellent blog post
I have purposefully not mentioned security in this post as security assessments come with a lot of complexity, will aim to write up on this sometime soon.
Many Thanks to Toby Jaffey for editing.
Even long-time IoT enthusiasts struggle with the wealth of technologies that are on offer these days. One of the most confusing phenomena for someone who isn’t a RF engineer is the scale and range of LoRaWAN. If you’ve been in the game for a while, you may have used a ZigBee radio module for wireless data transmission in your own projects. ZigBee-compliant modules had become a gold standard for many industrial applications in the 2000s, featuring >10m range (it was said to be 100m, but that was hardly ever achieved), up to hundreds of kbit/second transfer rate (depending on the model and radio band used) and message encryption by default. Over most cheap proprietary RFM22 transceivers, ZigBee also offered an industry standard following the IEEE 802.15.4 specification for mesh networking. This allowed ZigBee devices to forward messages from one to another, extending the effective range of the network. Despite their rich features, ZigBee devices are limited in range and limiting when it comes to their power consumption and the potential use in IoT application. And this is where LoRaWAN comes into play: It’s a Low-Power Wide Area Network (LPWAN) standard promising a reach of tens of kilometres for line-of-sight connections and aiming to provide battery lives of up to ten years. How can this work?
First, let’s contrast short-range radio standards like the ZigBee with the LPWAN standards like LoRaWAN. RFM22, ZigBee and LPWAN all use radio frequencies in the ultra high frequency (UHF) range. Following the ITU 9 classification, these are devices that use a carrier frequency of 300 MHz to 3 GHz. That is, the radio waves have a peak-to-peak distance of 10-100 cm — a tiny proportion of the electromagnetic spectrum. Here, we find television broadcasts, mobile phone communication, 2.4 GHz WiFi, Bluetooth, and various proprietary radio standards. We all know that television broadcasting transmitters have a significant range, but clearly that’s because they can pack some punch behind the signal. There must be another reason that LoRaWAN does better than the other radio standards. The carrier frequency itself can therefore not explain the range of LPWAN standards.
There is all sorts of hardware trickery that can be applied to radio signals. Rather than allowing those electromagnetic waves orientate randomly on their way to the receiver, various polarisation strategies can increase range. A circular-polarised wave that drills itself forward can often more easily penetrate obstacles, whereas linear-polarised signals stay in one plane when progressing towards the receiver, concentrating the signal rather than dispersing it in different directions of the beam. However, these methods require effort and preparation on both the sender and receiver side, and wouldn’t really lend themselves to IoT field deployment…
The secret sauce of LPWAN is the modulation of the signal. Modulation describes how information is encoded in a signal. From radio broadcasting stations you may remember ‘AM’ or ‘FM’, amplitude or frequency modulation. That’s how the carrier signal is changed in order to express certain sounds. AM/FM are analog modulation techniques and digital modulation interprets changes like phase shifts in the signal as binary toggle. LPWAN standards are using a third set of methods, spectrum modulation, all of which get away with very low, noisy input signals. So as the key function of LPWAN chipsets is the demodulation and interpretation of very faint signals, one could think of a LoRaWAN radio as a pimped ZigBee module. That’s crazy, isn’t it? To understand a little more in detail how one of the LPWAN standards works, in the following we are going to focus on LoRaWAN as it is really ‘the network of the people’ and because The Things Network -a world-wide movement of idealists who install and run LoRaWAN gateways- supports our idea of open data.
LoRaWAN uses a modulation method called Chirp Spread Spectrum (CSS). Spread spectrum methods contrast narrow band radio as ‘they do not put all of their eggs into the same basket’. Consider a radio station that transmits its frequency-modulated programme with high power at one particular frequency, e.g. 89.9 MHz (the carrier is 89.9 MHz with modulations of about 50 kHz to encode the music). If you get to receive that signal, that’s good, but if there is a concurrent station sending their programme over the same frequency, your favourite station may get jammed. With spread spectrum, the message gets sent over a wide frequency range, but even if that signal is just above background noise, it is difficult to deliberately or accidentally destroy the message in its entirety. The ‘chirp’ refers to a particular technique that continuously increases or decreases the frequency while a particular payload is being sent.
The enormous sensitivity and therefore reach of LoRaWAN end devices and gateways has a price: throughput. While the effective range of LoRaWAN is significantly higher than ZigBee, the transmitted data rate of 0.25 to 12.5 kbit/s (depending on the local frequency standard and so-called spreading factor) is a minute fraction of it – but, hey, your connected dishwasher doesn’t have to watch Netflix, and a payload of 11-242 bytes (again, depending on your local frequency standard etc) is ample for occasional status updates. Here is where the so-called spreading factor comes into play. If your signal-to-noise ratio is great (close proximity, no concurrent signals, etc), you can send your ‘chirp’ within a small frequency range. If you need to compensate for a bad signal-to-noise ratio, it’s better to stretch that ‘chirp’ over a larger range of frequencies. However, that requires smaller payloads per ‘chirp’ and a drop in data rate.
Power consumption, reach and throughput are all linked. To burst out a narrow transmission consumes more power than to emit a spread signal. Hence, LoRaWAN implements an adaptable data rate that can take into account the signal-to-noise ratio as well as the power status of a device.