Roundup of the Best Workplace Trends Blogs

Blogs are a great way to keep up with the fast changing developments in workspaces.

Blogs are a great way to keep up with the fast changing developments in workspaces. From sensors and software to IoT to real estate, here is a round-up of are some of our current favorite blogs:

Workplace Insight Workplace Insight is a great source of news and information about the design and management of workplaces. Mark Eltringham and his team offer interesting insights into workplace design and management issues.

Memoori Memoori provides thought provoking information and insights on smart buildings.

Dwell Magazine We love this stylish and innovative Magazine, the Workplace & Office section is a special favorite.

Here are a couple of favorite IoT and Smart Building blogs that keep us informed on the latest technology and innovation trends too;

Have a blog you think we should include? Let us know, we’d love to check it out!

Say Goodbye to Clipboards! Why Sensors Are Replacing Manual Desk Occupancy Surveys

How companies are improving their use of space and the potential for reducing costs and energy usage through sensors

Over the past two decades, clipboard reports have been the foundation for desk occupancy studies. In a typical study, 12 undergraduates walk a 5km route through an office workspace to document desk and conference room occupancy. The path takes about an hour to complete and once they finish the start around the path again.

One of the primary benefits of the desk occupancy sensors is that companies can make improvements in how space is used and the potential for reducing costs and energy usage. By capturing and centralizing utilisation information, and doing so in a timely, automatic, non- intrusive manner, analytic programs can find places for improvement.

  • Staffing cost – Manual surveys are expensive, and the biggest expense in such studies is labor. The staff cost is not just for gathering the information, but additional resources are needed to do the reporting on the data.
  • On-going staff training expense – Because of the high turnover rate of these surveyors with clipboards, companies spend a surprisingly high amount of ongoing training and hiring activities. Often this is a very large hidden expense.
  • Errors – Walking a long tedious route gets boring, surveyors make mistakes, and the quality of the study suffers.
  • Sampling rates – Because of the large staff cost, manual surveys are usually constrained to about a week. Sensors enable you to get a better picture of what is going on as you are measuring for a longer period of time i.e. minimum of 8 weeks or permanently. Also, rather than sampling what is going on every hour, you can now sample every 5-10 minutes. The rule of thumb is, you’ve got to sample at twice the event frequency to have confidence in what you’re doing. If you’re doing an hourly survey, you’re really only capturing events that last 90 minutes to 2 hours with any kind of accuracy. On a 10 minute sample, you’re catching stuff that’s 20 minutes, half an hour long. On a 5 minute sample, you’re probably catching events that are 10-15 minutes long.
  • Reporting – The whole point of the study. With manual surveys, whether using pencil and paper or software, staff still need to generate reports. With OpenSensors, the sensors’ data becomes a feed and the reporting and dashboards are ready made and don’t require on-going work to be generated. The whole operation becomes less of a manual process of moving data around; we link with CAFM systems and any other facilities management systems. The process becomes API driven and enables multiple stakeholders to analyse the data.
  • Security – Sensors are less disruptive than having people constantly walking through the office.

Utilisation studies can help you manage desk sharing ratio and unit mix for your flexible working office. Workspace occupancy sensors are replacing manual surveys for a timely, automatic, non- intrusive way to manage wasted desk space and save cost and energy usage.

Why Use Sensors for Workspace Design?

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.

Why use sensors for workspace design?

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.

A game-changer for the industry

  • Winning more deals both for new development or re-fit of iconic buildings
  • Lower cost than manual surveys
  • Real-time information to facilities managers and even tenants
  • Private data combined with public
  • Understand Air Quality factors for building wellness assessments

Sensors to replace manual work

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.

How does it work?

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.

Practical Examples

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.

Emerging Areas of Practice

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.

Integration

  • Digital scale models: OpenSensor data can be integrated with architects’ current CAFM systems and 3D rendering environments.
  • Intelligent / Reactive Environments: OpenSensors data can be integrated with displays for open desk notification.

Tips for Installing a Community Air Quality Sensor Network

Understanding your sensor choices for collecting air quality data

Small air pollution sensor technologies have enabled deployment of numerous sensors across a small geographic area that can supplement existing monitoring networks and significantly reduce the cost of longer-term community air pollution studies. This helps mitigate the risk of current approaches to monitoring air quality in a region that rely on only a dozen or so stations and may give you an average that is not be representative of what’s happening where you live.

What are you trying to do?

Air quality is affected by many possible contaminants, in fact the Environmental Protection Agency (EPA) has identified six “criteria pollutants” as pollutants of concern because of their impacts on health and the environmentx. The criteria pollutants (http://www.epa.gov/airquality/urbanair/) are:

  1. ozone (O3) http://www.epa.gov/air/ozonepollution/
  2. particulate matter (PM) http://www.epa.gov/air/particlepollution/
  3. carbon monoxide (CO)http://www.epa.gov/airquality/carbonmonoxide/
  4. nitrogen dioxide (NO2) http://www.epa.gov/air/nitrogenoxides/
  5. sulfur dioxide (SO2) http://www.epa.gov/air/sulfurdioxide/
  6. lead (Pb). http://www.epa.gov/air/lead/

Under the Clean Air Act, the EPA has established primary and secondary National Ambient Air Quality Standards (NAAQS) for these six pollutants. As you begin, keep in mind what you want to measure and how that information will be used. Is there some final output or final report you’ve got to get to?

Understand your sensor choices for collecting air quality data

Commercially available sensors can measure the level of potential contaminants including O3, NO2, NO, SO2, CO, PM2.5 and lead. These devices should be designed to be easy to connect and provide quality data measurements so that non technical community groups can deploy them.

Here are some factors to consider in assessing options for sensors to collect air quality data * cost * operating lifetime * accuracy, precision,and bias of measurement * range of sensitivity * speed of response time * maintenance requirements * reliability

More information on what and how to measure see https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=519616

Beyond the sensors, you will need to make tradeoffs between cost and redundancy for the best network connectivity.

Point to point – lowest cost, greater number of coverage points, least redundancy for each individual point Mesh – higher cost, greater redundancy

Most community-based sensor networks are adopting point-to-point network connectivity because of the ease of connection and low-cost structure. Here is a guide that we already have around pros and cons around connectivity, use that to find the best connectivity network

Our Process

OpenSensors recommends a phased approach, from proof of concept to full-scale deployment, to ensure a successful installation of an IoT network in a business environment. Our aim is to reduce the time to go live and minimize risk.

Phase 1 Evaluate sensors:

Evaluate different sensors for quality, signal-to-noise ratio, power consumption and ease of setup by trying them out on a very small scale in a lab.

Phase 2 Proof of concept:

Do a full end-to-end test to verify that the queries and analytics were feasible. Connect 5 to 10 sensors to a cloud infrastructure.

Phase 3 Pilot phase:

Move out of the lab into your actual environment. Typically, this requires somewhere between 30 to 100 sensors. We suggest a one to two-month test to ensure that the sensors work at scale and the gateway can handle the load, similar to production usage.

In addition to testing the sensors in the wild, this is the time to think through your onboarding process for the devices. Questions like; who will install the sensors feeds into design decisions on the firmware of how much pre-configuration has to be done. We recommend a ‘just works’ approach and an assumption that all sensors will be installed by people who will not configure firmware. If you need to deploy 200-300 sensors, the installation engineers need to be able to deploy a lot of sensors in a distributed physical environment over a short amount of time. It is much more efficient for your sensors to be pre configured. In these situations, we give usually give people a simple interface to enable them to add meta data such as location and elevation. Sensors should be labelled clearly and details pre-loaded on a cloud platform like OpenSensors before they are deployed so that adding meta data information is a matter of 1-2 steps.

Phase 4 Plan and implement full-scale deployment:

After the pilot phase, there should be enough data to verify network performance and your choices for sensors and connectivity, after which, full deployment can be planned in detail and implemented.

Want to create your own Air Quality project?

The EPA Smart City Air Challenge (https://www.challenge.gov/challenge/smart-city-air-challenge/) is now live. The challenge is trying to help communities figure out how to manage installations of 250 to 500 sensors and make the data public. OpenSensors.io is free to use for community projects working on IoT Open Data projects and will be supporting the EPA’s iniative.

Contact us if you would like assistance on sensor selection, network design, or planning a proof of concept deployment.

What Is LoRaWAN?

Confused about why it’s better than Zigbee? Let us help…

What is LoRaWAN and why is it “better” than Zigbee?

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.

European Parliament Approves eCall Technology

The Internet of Things threatens to revolutionise everyday life

European Parliament approves eCall connected car platform

The Internet of Things threatens to revolutionise everyday life, embedding and imbuing everyday objects and the world around us with sensors, software and electronics. Through machine-to-machine communication, automation and advanced analytics, we are able to understand and scrutinise our environment and the processes which surround us in ways never conceived. From high level analysis allowing automated condition monitoring of critical engine parts, giving engineers the tools to reduce costly operational downtime to embedding real-time sensors in bridges to predict stresses and flooding. Beyond the Cloud, the Internet of Things brings the internet to the everyday, and there are clear use cases for such technologies in the realm of road safety.

This is where eCall comes in. eCall is a European Commission initiative coming into force on 31 March 2018, making mandatory the deployment of internet-connected sensors into cars that enable emergency services to be immediately contacted and requested automatically after a serious road incident within the European Union. EC VP for Digital, Neelie Kroes, argues “EU-wide eCall is a big step forward for road safety. When you need emergency support it’s much better to be connected than to be alone.” eCall will drastically cut European emergency service response times, even in cases where passengers are unable to speak through injury, by sending a Minimum Set of Data (MSD), including the exact location of the crash site.

The deployment of eCall is one of most ambitious EU-wide programs since the 2007 enlargement, rolling out implementation of the eCall platform to some 230 million cars and 33 million trucks in the European Union. Implementation of eCall at a European level (including Norway, Switzerland etc) however benefits consumers and industry through reducing costs due to economies of scale, reducing the installation cost to as little as €100. The basic pan-European eCall service will be free at the point of use for equipped vehicles. It is likely that the eCall technology platform (i.e., positioning, processing and communication modules) will be exploited commercially too, for stolen vehicle tracking, dynamic insurance schemes, eTolling and emerging forms of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) road safety systems. eCall will be based upon a standardised platform, one system for the entirety of Europe, aimed at enabling both car and telecoms industries a quick roll out and to avoid crippling OEM versioning and patching issues.

In terms of privacy, the basic eCall system has been given the green light by the European Commission on the express condition that firm data protection safeguards are in place and that the sensor-equipped vehicles will not push data to external servers except in the case of a crash, or by the actions of the driver, in order to contact the PSAP (Public Safety Answering Point) and will lie dormant until that point. The data transmitted to the emergency services, described as MSD, Minimum Set of Data, are those strictly needed by the emergency services to handle the emergency situation. While in normal operation mode the system is not registered to any telecoms network and no mediating parties have access to the MSD that is transmitted to the PSAPs.

Today the European Parliament’s Internal Market and Consumer Protection Committee MEPs voted on and approved eCall pushing forward a life-saving Internet of Things technology that will significantly improve European road safety. The UK Government however, has not followed suit, whilst welcoming the implementation in other member states, feels that “it is not cost-effective … given the increasing responsiveness of our road network, we feel that smart motorways do the same thing,” remarked Minister Perry on behalf of the Department of Transport. Whilst it can be argued that ‘Smart Motorways’ are far from a worthy substitute to connected cars & V2V/V2I systems, the UK’s criticism belies a certain caution with regards to green-lighting large and costly IT projects. Only time will tell whether the UK Govt’s decision has left those drivers not on Britain’s Smart Motorways in the lurch.