You’ve invited all your friends and family over your house for a holiday gathering. You’ve cleaned your house, put out snacks in the living room, set out tables and chairs in the dining room, and drinks in the back room. You’ve perfectly planned out the setup - everything is in its place.
The guests will arrive and disperse themselves evenly among various rooms in the house, mingling and drifting from space to space, conversation to conversation. Lively chatter and warm holiday music fill your home.
But that’s not how it turns out. What really ends up happening is all the adults cram into the kitchen and all the kids hide out in the basement. You end up bringing all the food back into the kitchen because that’s where everyone is. Nobody goes into the living room, nobody sits in the dining room for dinner. You can’t help but feel all the effort you put into setting up the house was wasted.
If this sounds familiar, you’ve been subjected to a classic problem in the realm of space utilization. You believed that you had set up your home for the party in a way that would best maximize the use of all rooms, but your friends and family ended up clumping together in one or two places in the house.
The same happens in the real world: a designer - whether it’s an architect, property manager, or interior designer - envisions a space - such as an office building, hotel lobby, or an event venue - to be used in a particular way. But in practice, the tenants, guests, and visitors end up using the space in an entirely different way.
The designer is left trying to answer some questions: where is everyone? And why is this happening? How can we best monitor, measure, and take action on how a space is used? And what does IoT have to do with it?
The objective of measuring space utilization is to figure out how many people are using a space. The scope of a space to be measured can vary - from individual desks in a coworking space, to conference rooms in an office, to exhibition halls at a convention.
Current approaches use very rough approximations for occupancy, such as looking back at scheduling records, conference room bookings at an office, or ticket sales for an event. In some situations, organizations engage consultants to perform manual observation and on-site assessments of how many people move through a given area. While it could get you close to the answer, using these methods present several limitations:
Not real-time: Going back to booking systems to look at data can be helpful, especially if you don’t have a bonafide space utilization system in place. But they don’t give an accurate sense of what is happening at the present moment. Depending on the use case it could be important to have a real-time view.
Time-consuming: Many current methods are labor-intensive or tedious. For example, hiring a third party to perform an on-site assessment can get expensive and aggregating information from other systems to piece together a picture of occupancy at a given point can be time-consuming.
Not quantifiable: Doing manual observation or using other systems such as conference room booking systems as a stand-in for occupancy do not provide the precision to truly count the number of people over time. For the meeting scheduled in a conference room, how many people actually ended up attending?
Inflexible: Gathering data from systems like security badge swipes or other forms of self-reporting requires either the physical installation of new infrastructure (e.g. card swipe panels and wiring of door lock mechanisms) or operations training (to teach people how to report their usage). This makes it hard to quickly change which areas you want to measure and get consistent reporting.
With IoT, organizations can measure space utilization in a systematic way that frees themselves from the limitations of the highly manual, time-intensive, and less quantifiable processes of the past. By implementing a system using IoT, measuring space utilization becomes:
Automated: By placing sensors in locations to measure the intended space, the hardware does the data collection and counting so you don’t have to rely on a physical person or self-reporting to do the work.
Repeatable: By using software to process the data collected by sensors to produce a count or level of occupancy, the measurement process becomes consistent and repeatable. Users can rely on the occupancy levels they read knowing that they were generated in a consistent, non-biased way that operates around the clock.
Real-time: Because sensors are continuously gathering data to report occupancy levels, interfaces can be built to present that occupancy information equally as fast. In this way, real-time views of how a space is being used are easily accessible to users.
In any industry, being able to thoughtfully design a space to match the intended usage of the room is a difficult but important job. By gathering space utilization data and observing trends over time, IoT has the capability to transform a variety of industries, examples of which include:
Office and Property Management: Use occupancy data to understand which rooms, office spaces, or floors are being used the most to inform office designs or make heating and cooling system adjustments for energy savings.
Retail: Analyze trends of how customers spend time in different areas of a store and move from section to section. Use that data to make merchandising and product placement decisions.
Events and Entertainment: Look at space utilization numbers to gauge actual attendance numbers, perform heat mapping of most and least used spaces in the event, and reconfigure vendors or A/V systems to match foot traffic and provide best coverage of the venue. Set alerts when capacity is reached in a given space to redirect guests to other areas.
The exact technology used in a space utilization system will vary based on the use case. It also depends on what is being measured. This could be the occupancy of a desk, utilization of a room, or capacity of more broadly-defined space.
When looking at sensors, there are several considerations to take into account:
For connectivity to the cloud, sensors can either communicate directly with the cloud or, depending on the use case, communicate to gateways which in turn communicate to the cloud.
If considering direct connectivity to the internet, options include Wi-Fi and cellular. The advantage of Wi-Fi is that it is typically already available at an installation site and adequate coverage exists where people are normally located. However, Wi-Fi connections could be spotty, access could vary based on enterprise security rules, and connectivity could go down whenever the Wi-Fi goes down. Cellular is ubiquitous and more robust, but the costs of data could get expensive depending on the number of sensors and reporting frequency of the solution.
If looking at network connectivity to the cloud via gateways, a low-powered wide area network (LPWAN) may fit your needs. These networks emphasize long-range, low-bandwidth communications such that even a single gateway can provide coverage for all sensors located in a large radius. LPWAN can be implemented for both indoor and outdoor use cases, and being low-bandwidth results in energy savings for battery-powered sensors. Examples of LPWAN protocols include LoRa, NB-IoT, and Sigfox.