Edited transcript of the OpenSensors Webinar on “Evidence Based Design of Workspaces”
Edited transcript of the OpenSensors Webinar on “Evidence Based Design of Workspaces”
Our panelist are: * Arjun Kaicker of Zaha Hadid Architects. An architect and workplace consultant with more than 2 decades of experience in workplace design, he brings a real passion to his work and wants to create workspaces that directly respond to the needs and aspirations of his clients. * Yodit Stanton, CEO of OpenSensors. She has spent the last 15 years as a data engineer building large scale data processing and machine learning technologies in financial trading systems. She has been working with IoT data for the last 3 years. * Sean Murphy, CEO of SKMurphy, Inc. is an advisor to OpenSensors. Acting here as the “voice of the audience:” he poses questions to the panel as webinar attendees type them in.
Yodit Stanton: Thank you everyone. Welcome. This webinar covers data driven design or evidence based design. Essentially what that means is using data from sensors and other things around enabling people to understand how space is being used and also the design of the space. A lot of the trends we’re seeing, is in especially in building occupiers, remotely monitoring the buildings. There are also starting to use these data sets to inform the design and inform the future planning.
Arjun Kaicker: At Zaha Hadid Architects we’re really interested in the potential of sensors. Architects and designers have always struggled to really understand client needs in office projects. An office design is the opposite of a residential design. In a house there is a family with a few people who are the primary users of the building. In a office there are hundreds, possibly thousands, of users with very diverse behaviors and contradictory requirements.
Sensors provide a really powerful tool, for understanding workplace needs as we never have really been able to before. We no longer need to rely on assumptions and preconceptions of how people work, or on benchmarking, or—even worse—copying what other successful companies do with bean bags and foosball. Sensors really give us the opportunity to take the guess work out, take the assumptions out and to understand the real needs of workplace users.
Yodit Stanton: At OpenSensors we see three drivers for work space design:
Arjun Kaicker: Great points Yodit. Workplaces are expensive so we don’t want to waste space. We don’t want meeting rooms that are being underutilized or amenity spaces that are not as popular as people predicted. But the flip side is that we often find some spaces over utilized in the workplace so that they are not available when needed. In that case the problem isn’t about waste, it’s about people not being able to do their job properly. When people can’t communicate and collaborate properly because they can’t find a meeting room at the right time, it can have a real effect on business efficiency and productivity.
Sean Murphy: Arjun, I have a question for you. Yodit presented figures that it was 13 to 15 thousand pounds a year per desk in downtown London. I would think that the energy cost at most a 10% of that—perhaps even less than 1%—yet we’ve spent more time trying to instrument the energy usage than we have the space usage. Why do you think the space usage monitoring has lagged?
Arjun Kaicker: That’s a fantastic point. With energy a few sensors can capture the total use, while you may need more to get a finer grained understanding. I think space usage monitoring has lagged because you need many more sensors to be able to monitor it as carefully. Building management systems have enabled both the monitoring and adjustment of energy usage. Traditional reservation systems have not incorporated data from meeting room occupancy sensors so they have only been able to allocate but not measure actual usage. Swipe card systems that monitor every entrance and exit can be used to assess total occupancy but give very little detail on usage. I think that occupancy monitoring and utilization assessment are going to catch up with energy monitoring.
Arjun Kaicker: We cannot lose sight of the fact that it’s the people and not the real estate or the energy that are asset, and the most expensive cost. Depending upon their skills and experience the total cost of the people is probably seven to ten times that of the space they occupy and energy they consume in an office setting. If a better designed workplace can increase productivity by five or six percent that’s equivalent to half of your real estate cost And, if you can, from getting a better designed place, from getting a better designed workspace. If you can increase productivity, the efficiency by 12 to 15% you have paid for your whole building—that’s where the real savings can come.
Sean Murphy: Currently how are folks monitoring the workplace? And how can using sensors make it more effective?
Arjun Kaicker: Today you can look at the swipe card data to see how many people are occupying a building but that doesn’t really tell you anything once they’ve gone into the building.
You can interview people or hand out surveys to ask them what they see working well and not so well. But this can end up being very subjective. As a workplace consultant, the only technique that I’ve used that did more than scratch the surface is a space utilization study.
For a space utilization study we start at one end of the building and walk to the other end, floor by floor. We walk to as many desks as we can within an hour and then turn around and start again. For eight hours we mark down what’s happening at the desk or what’s happening in the meeting room.
With one person in a week you can get good coverage on about a hundred desks: you have a snap shot for the week of how those desks were used. This is a very very useful complement to interviews and satisfaction surveys for an organization. The problem is that it’s just one week and it would only be for that second you walked past the desk during the hour. So, if someone happened to get up to use the restroom for five minutes while you were walking by you would mark the desk unoccupied for an hour. It’s a time consuming expensive method: to cover 1,000 desks you would need a least five people full time for a week.
Arjun Kaicker: Sensors have massively cut the cost of carrying out this kind of data gathering and the amount of time it takes. We have also found that although space utilization studies were very useful to give a kind of general picture, they weren’t that persuasive because people knew were only done over a week or they knew that actually, it was only a snapshot within a second within that hour. And, I think that sometimes managers and executives were a bit suspicious of them: it’s much easier to buy into requirements based on the very rigorous data from sensors.
Sean Murphy: How do you tell if people have tried but failed to find the certain type of space? I needed a phone booth but I couldn’t find it? I needed a five person conference room and ended up in one that holds 20.
Arjun Kaicker: You cannot tell directly but you can be pretty sure it’s happening if we see 100% utilization of a particular resource. Normally 80% to 90% is about the limit before problems start to emerge.
Yodit Stanton: Sensors are complementary to satisfaction surveys and in-depth conversations. I would never say, even as a sensor company, that they can replace the in-depth conversations that you need to have with employees.
Arjun Kaicker: To give an example of that, a few years ago I had in client on the West coast of Canada. We did a space utilization survey—sensors were not available then—and we did a questionnaire. In the questionnaire people kept on saying ‘I can never find a meeting room when I need it’ but the space utilization study found that meeting rooms were used 30% of the time. We drilled in to see if there were any particular meetings rooms which had high use—maybe these were the ones people were complaining about—but answer was no, the highest meeting room usage was 60% of the time.
We dug in some more and did some in depth interviews and discovered that they were doing a lot of audio and video conferences with colleagues on the East Coast of Canada. There’s a four hour time difference and now it became obvious. For half the day in the window where their workday overlapped the over coast, these meeting rooms were booked solid. We were able to start designing to meet this need once were were able to reconcile the utilization data with the survey data and insights gleaned from in depth conversation.
Yodit Stanton: How do you see the role of architects changing? My understanding is that architects used to design the space and deliver the project and then move on to their next project. Now with these sensor networks actively collecting occupancy data and other systems generating live data, how do you see the role of architects changing with this ongoing stream of information about how the client is taking advantage of the design?
Arjun Kaicker: Sensors can make a really big difference to the way that architects design space because now we’re designing more for flexibility, for adaptability in the future. We’re not just looking at designing in day one. Sensors provide data that allows us to move beyond rules of thumb and best practice, to they enable us to understand client needs in much more depth so that can we design something that is better for them. Sensors are holding us more accountable to clients for the impact and usefulness of our designs
Yodit Stanton: We are a technology company, most of our customers come to us with a project and ask for help selecting the right sensors, managing their installation and integrating the data streams they emit with existing tools and information systems.
We really do three things:
Arjun Kaicker: We want to get a complete picture of the client’s needs and rely on the following types of sensors to get a full picture:
We can combine different data to answer the following kinds of questions:
Yodit Stanton: So far we have been discussing the various types of data that a sensor network can collect on workspace utilization and environment. Let’s talk about two ways that we use most commonly to visualize it to make sense of it. In space: typically as an overlay on CAFM or workspace CAD drawings. This can be used to answer the question “I want to see what’s going on right now.” In time: what are the trends of usage over the course the day or days of the week or weeks of the month. When is peak usage—and perhaps what does this look like on the seating chart? One thing we like to do is incorporate historical data from manual surveys so that we can potentially uncover trends that started before sensors were installed. Data from reservation systems can be incorporated to forecast near term needs and from swipe card systems to cross check total building occupancy.
Yodit Stanton: I wanted to cover some practical cost considerations for installing and managing a sensor network. You have to look at the following costs:
There are a number of trade-offs but the one key point I want to make is that manual maintenance, changing batteries, and installing sensors can be significantly more than the raw cost of the sensor hardware.
Arjun Kaicker: It’s really important to clearly explain the reasons behind a workplace project. Normally, it’s something simple like to create a better place for people to work. But if you don’t explain it to them, people often assume that it’s about cost cutting, or it’s about downsizing space. It’s about taking stuff away from them as opposed to enhancing the space for them.
In the absence of clear communication, people assume it’s about them. If you don’t explain that the goal is to understand the needs so you can create better spaces some people might assume that you’re trying to check their work performance.
Showing people the results of the data and not just explaining what you’re doing makes a big difference. With sensors, you don’t just have to provide the information to them at the end of the survey. You can actually do it in real time, so people can maybe click on the dashboard and see what findings of the sensors are in real time. And that can often make people feel much more comfortable with the process.
Sean Murphy: We had one question on that, around sensors only painting part of in picture in large organizations where the issues of utilization are more compelling. Know who is using the space is also important, which would seem to work counter to the privacy concerns. But I can understand where architects might want to know which groups or which category of persons.
Arjun Kaicker: Yeah, there definitely is a balance to be struck there. What we do is to have open discussions about with the client about what level of anonymity they want to have. So, there can be complete anonymity or there can be, for instance, anonymity that doesn’t tell you who the individual is. That might provide data on what group they’re in, what team or department they’re in, or might, alternatively, give information on what level they are within the organization. If they’re executive, or if they’re general staff, et cetera. Obviously, if you start to cross reference that a bit too much, then if there’s only one executive in a particular team, that starts to kind of ruin the anonymity.
But generally, you’d be able to process results that anonymous enough and, really, so no one is ever seeing who the individuals are. I think there’s one for the caveats of that, which is that we do … There’s an obvious issue with if there isn’t hot desking, if people have the permanent desk, then you’ll be able to pretty quickly work out, even if it’s anonymous … If that is the only person who ever sits there, then you kind of know how much time they’re spending at their desk. And I think that was always an issue with the space utilization studies. That people have to be comfortable with that level of visibility of what they’re doing.
Sean Murphy: What I’ve learned today is that architects are using data to fuel design and are moving from rough rules of thumb to incorporate more granular data in the way that they’re making decisions. OpenSensors aggregate and help you understand the data. They are moving to enable this information to be fed into their existing tools and existing systems, the CAFM systems, the reservations systems at co-working facilities, systems like that.
Arjun Kaicker: I think that sensors are a great additional tool for architects and designers. I don’t think that they provide all the answers for understanding, using these, but they’re a really powerful part of a tool kit. I think, that also just interviews, surveys, workshops with people, really bringing users into the process is still as useful and viable as it’s always been. I also think that what sensors can start to do is that they can give us more broad data. When we start looking at a series of buildings and how a sense of data might be different in different buildings, and that might be particularly useful for developers even more so than specific building occupiers and so it can really start to help us to understand how to design spaces better for maybe multiple tenants.
Yodit Stanton: As a technologist, it’s very interesting seeing the kind of maturation of the sensor installs and actually enabling people that are not very technical to work with these types of stats. I’m fascinated what kind of impacts these trends are gonna make. Both in terms of the relationship between the levels and occupiers and how the trends that kind of started with, or are starting with, replacing a lot of the manual subways will drive a lot of automation, a lot of a kind of automation with in terms of meeting rooms and so forth and seeing what kind of change it drives in terms of the designer of these spaces. Because, you know, I think everyone wants to, or at least is trying to go towards multi-use, multi-purpose buildings that, you know, we still have some ways to go with that.
OpenSensors co-hosted panelists who gave their views of the current state of data driven workspaces
OpenSensors co-hosted a panel for invited guests on the Future of Workspaces with Cushman & Wakefield. The panel also included Yodit Stanton, CEO of OpenSensors, Uli Blum, Architect at Zaha Hadid and Simon Troup, Founder of Fractalpha. Juliette Morgan, a Partner at Cushman & Wakefield moderated the panel. It was a lively crowd with a sense of urgency – wanting the future now!
Our panelists gave a view of the current state of data driven workspaces through their different lenses.
For Uli Blum, Architect at Zaha Hadid the world is increasingly driven by data. It gives us much more understanding of the technical aspects of how people work and are living in our spaces. He shared about different work styles, variations of acoustics across a floor, lighting conditions, proxemics, adjacencies, and connectivity. Zaha Hadid wants to better understand all of these aspects and take into account in design.
Simon Troup, Founder of Fractalpha shared how with data you are trying to find that secret sauce that differentiates you from the competition. He gave an example from the financial market where having access to early data before your competition is a huge edge over them.
Yodit Stanton, CEO at OpenSensors shared about the traction she was seeing, the practical side of how companies are deploying sensors and how to get started. Lots of people are putting in desk meeting room footfall sensors and trying to understand how many people are in the space and how to design better. But we also see combining this workspace occupancy data with facilities data from access control and building management systems for a full view of what is happening.
Workspace designers are wising up and using OpenSensors’ capabilities to optimise their usage of real estate
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.
Space planner or architect? We have broken down the steps to get you to your desired end goal maximising efficiency and cost within buildings
Whether you are a building manager planning efficient space usage or an architect looking to design state-of-the-art buildings, we have broken down the steps to get you to your desired end goal. IoT planning should start with the business needs, of course, and quickly moves from the component layer all the way up to the application layer. We need to figure out what core data should be gathered and ways to effectively leverage that data. These IoT solutions require an end-to-end or device-to-cloud view.
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.
We have built hardware, installation and network provider partnerships and relationships to help customers get rollouts live efficiently. Either roll out your own network or we will put you in touch with your local sensor installation specialist to take care of the install and maintenance. We are working with customers and the community to understand what is required at each level for your IoT solution and can ease development and integration issues.
There are many different ways to connect your sensors to the web, but how to know which are best for your project?
How sensors communicate with the internet is a fundamental consideration when conceiving of a connected project. There are many different ways to connect your sensors to the web, but how to know which are best for your project?
Having just spent the better part of a week researching these new network technologies, this brief guide outlines the key aspects to focus on for an optimal IoT deployment:
There are also a number of legal, cost, market and power focused aspects worth considering that I shall not cover here. But, critically, it’s worth mentioning that the majority of these technologies operate on ISM (industrial – scientific – medical) frequency bands, and as a result are unlicensed. These bands are regulated and there are rules, however anyone operating on these bands can do so without purchasing a license. Notably, you don’t have sole ownership of a slice of the spectrum, you don’t get exclusive access. Therefore, with a variety of other vendors blasting away across the radio waves, these technologies encounter significantly more interference than the licensed spectrum. However, the new networks, LoRa, Sigfox, NWave etc are based on protocols and technologies designed to better sort through this noisy environment, grab a channel and send a message.
Understanding that the airwaves are a chaotic mess underlines the importance placed on features such as adaptive data rates, node handoff and power saving methods such as asynchronous communication. Wired networks do not have to consider such things. But for most it’s not just a case of who shouts loudest wins. The majority of wireless protocols ‘play nice’ opting for a polite “listen, then talk” approach, waiting for a free slot in the airwaves before sending their message.
Some protocols such as Sigfox forego such niceties and adopt a shout loud, shout longer approach, broadcasting without listening. A typical LoRaWAN payload takes a fraction of a second to transmit, Sigfox by comparison sends messages 3-4 seconds in length. However, if you just broadcast without listening, Sigfox must therefore operate with severe cycle duty limitations, which translate into a limited number of messages sent per device per day and severe data rate limitations.
These choices also translate into varying costs, and critically, into battery life limitations and gains, the crux of any remote deployment.
See this link for a matrix of the major technologies currently vying for network domination.
Confused about why it’s better than Zigbee? Let us help…
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.