Commercial and retail applications of Wi-Fi scanners (aka people counters)

Reality vs marketing statements

It is not surprising that the rise in long range, low cost, low power and unlicensed communication technologies such as LoRaWAN has generated a family of new products which are relatively inexpensive and can be easily deployed.

One of these devices are active Wi-Fi scanners used with the purpose of collecting traffic foot fall data.  This capability provides Local Governments, event organisers, etc, with easy, low cost sensors that provide real time data on crowd density and movement.

SimplyCity has been deploying these devices around Australia for over 3 years with great success.

With every deployment, quite often we are asked four simple questions :

  1. How does it work ?
  2. How accurate is it ?
  3. Can I power this from a battery or solar panel ?
  4. Can I get data such as “dwell time” ?

Let’s answer these questions and debunk some of the myths we have seen around.


  1. How does it work ?

The principle is very simple. The Wi-Fi scanner acts searches for certain signals from Wi-Fi active devices.

Any Wi-Fi client in the area (typically a smart phone) will detect it and reply with a standard message which includes its current MAC address. The Wi-Fi scanner also attaches a signal strength information to the received packet. Please note that I have used the term “current MAC address”. This is because Android and Apple devices have a privacy feature which randomises their MAC addresses. This is done to prevent person tracking and protect anonymity. This information is very important and we will refer to it a bit later.

Once the device scans its surroundings, a tally of the detected unique MAC addresses is performed and the number generated is then sent to an end point. SimplyCity’s end point is the SimplyControl platform. This platform is capable of: storing incoming data, managing device range by remotely adjusting its sensitivity, visualising the incoming data and sending alarms based on various triggers.   Mac addresses are not stored or transmitted.  This is done both to provide privacy and manage the limited memory in the sensor.

At the end of the transmission, the Wi-Fi scanner’s memory is cleared and another scan is performed. This is another critical piece of information, also used in this article a little later.

As said in the beginning, the principle is very simple. But this raises a few challenges.


  1. How accurate is it ?

Wi-Fi scanning is reasonably accurate but it cannot be used to determine exact numbers.

For example, since some individuals can wear 2 smart devices, they can appear to be two. Others are detected as 1. There is also the baseline information to be collected with regards to fixed assets which are Wi-Fi enabled:  laptops, kiosk, tills, cameras, etc.

Needless to say that Wi-Fi scanning couldn’t’ be used for example to enforce occupancy restrictions due to COVID-19 regulations. But it will give a good idea of orders of magnitude of number of people in a given area and how it changes over time. This is very valuable business intelligence and extremely easy to obtain and use.

The benefit of the W-Fi scanners are in providing real time and time based information on crowd movement. This can be easily correlated with weather information, activities/events, promotional activitiesother data streams. SimplyControl platform does just that. Using visualisation options, customers can see average numbers for different times of the day, maximum and minimum numbers detected, estimated current counts, etc.

This information is also an excellent indication of how the crowds move throughout an event area, when the influx of customers has started, how it trended during the day, etc. You can even determine in real time what is the more popular spot of a public event or space.

A good use of this estimate is maintenance management, cleaners, security, which can be despatched based on trending values rather than just set on a schedule.


  1. Can I power this from a battery or solar panel ?


The Wi-Fi scanners are notoriously power hungry. The Wi-Fi modules are quite power consuming and every minute of scan uses milliwatts of battery power. This might not sound like much but when you want to monitor the foot fall for a 3 day event, once can quickly realise that an enormous battery level is required.

The software running on these devices can put the device in “deep sleep” mode but due to the nature of the data collected and the required frequency of scanning times, any gains using deep sleep functions would be obliterated by the sheer number of scans and transmissions.

The current offer from SimplyCity covers a mains-powered device as well as a solar option with a battery. A battery only solution is simply not feasible if we wanted to keep the physical size of such a deployment to minimal levels. Battery based Wi-Fi scanners can be used for very time-limited applications (1 day maximum).


  1. Can I get trending data such as “dwell time” ?


Wi-Fi scanners do not have huge processing power. They have limited memory to work with too. They are very compact devices.

Therefore, most of the data processing and any analytics required by customers is done inside the platform.

The SimplyControl platform can display the historical trend of movement of groups of people through an area, can trigger alerts if the estimated numbers are above a set threshold and will forward this data to the customer’s long-term database or platform for further processing.

Commercial value is easily found in the connection between numbers detected and new initiatives such as street closures for open markets, parklet installation etc. The portability of such devices makes them an ideal investment. Devices don’t have to be locked in one spot and can follow the development of a suburb as the various stakeholders require.

An interesting question that has been asked is related to so called “dwell time”. For those who are not familiar with the term, dwell time, in this context, refers to the average length of time a person or group of people stays in one place. This is an indicator of how engaging or attractive a particular area is for visitors. The more they spend there, the higher the impact of that area has over them. It is a great metric for performance evaluation, BUT..

And here is the BUT:

Let’s refer to the two very important elements mentioned in the first paragraph: MAC address randomisation and memory clearing of Wi-Fi scanners.

If a device changes its MAC address (as we have seen, all new Android phones and all iPhones do), then two different scans of the area will detect the same device twice but the Wi-Fi scanner will not know.

There is a way around this by extending the scan window to capture the device in one scan multiple times. Instead of scanning for devices for 1 minute and then sending the data, the scan is performed over 10 minutes long window. In this time, in a highly mobile environment, a device can be seen appearing (thus being counted) then disappearing. With a bit of simple math applied in the code, the device can send another measurement to its end point: the average dwell time of detected devices in the detection range. Once the packet of information is sent, the memory of the Wi-Fi scanner needs to be cleared (remember the memory is quite limited), therefore there will be no historical information stored for the next scan. If a person remains in an area 15 minutes, the dwell time of that person will still be 10 minutes, the length of the scan window.

In static events such as concert areas  or food truck areas, this becomes useless since most of the people will spend in excess of 10 minutes. In case of outdoor events, due to queues and table searching, the real dwell time could be even 30-40 minutes. Yet the system will not report more than 10 minutes dwell time.

Furthermore, due to the storage requirements of the data being collected during the scan window and because of the calculations required, these devices cannot detect more than 300 devices/people. If your event attracts 700-1000 people in an area, you will never know that. Your count will be off by 4-600. This is far more than an acceptable 15% accuracy in numbers.

It is easy to see why dwell time is important commercially but it causes a serious reduction in detected devices. SimplyCity has found that most of the customers are interested in foot fall traffic and the ability to estimate the numbers entering a premise versus the number of people just passing by.

Such an approach keeps the cost to a minimum and the data granularity to a maximum. If dwell time is not used, measurements can be sent every 3. This approach highlights the dynamics of the population movement and it is more accurate in terms of detected numbers. SimplyCity’s Crowd Density Monitors detect in excess of 1500 devices every scan in busy events.