Passive microwave measurement

The measurement landscape

The landscape for measurement is constantly evolving. Perimeter detection, one of the first technologies used for occupancy measurement, has the greatest limitation that it requires a well-defined perimeter, ideally with a small number of entries/exits. The technology is not well-suited to open spaces like malls, lobbies, or locations with either very wide entrances or large numbers of entrances. Camera technologies also carry concerns over facial recognition, require suitable lighting that may change over the course of the day, and are very sensitive to field of view. While perimeter cameras once constituted the state of the art, cumulative measurement errors over the course of the day can skew occupancy measurements late in the day.

Mobile apps that share the location of a smartphone, determined using the smartphone’s GPS location services, have historically been the most likely mechanism to compromise consumer privacy. While a consumer may have clicked “allow” when asked if a mobile app could use location services, most consumers don’t understand the implications of allowing their flashlight application to track their location at every moment of the day.

Today, the smartphone operating system vendors (e.g. Google & Apple) have imposed limits on the capacity of 3rd party applications to geolocate, limiting choices essentially to “never”, “one-time”, and “only when using the app”. These changes hugely limit the use of freeware applications for foot traffic measurement. Government regulation, manifested by GDPR in Europe and CCPA in California, have also begun to limit the collection and sharing of personally identifiable information (PII).]

Among the many technologies for direct measurement, advantages and disadvantages vary widely. The following table omits nascent technologies including RADAR, LiDAR, infrared ToF, and beacon-based solutions.

Passive microwave measurement technology

Two passive microwave technologies are used for audience measurement:  Wi-Fi and Bluetooth Low Energy (BLE).  Bluetooth “Classic”, used by smartphones to connect to wireless headphones, is not suitable for audience measurement because smartphones only broadcast when in pairing mode.

Passive Wi-Fi was the first technology to leverage the fact that smartphones, beginning with the first generations of Apple (iOS) and Google (Android) operating systems, spontaneously broadcast Wi-Fi probes for purposes of geolocation.  Audience measurement leverages this behavior to count smartphones, using smartphones as a proxy for people.

Despite the fact that Wi-Fi and BLE both operate in the same 2.4GHz microwave band, the two technologies have very different attributes, summarized in the table below.  The three most important differences (as the world stands in 2026) are that:

1. All smartphones broadcast Wi-Fi probes, while essentially only Apple phones broadcast BLE advertisements, making Android phones largely invisible in BLE
2. BLE was designed for “personal networks” and the transmission range of BLE advertisements is short because the power levels are low.  Wi-Fi, designed for “local area networks,” has much longer range.
3. The median time between BLE advertisements is 1.2 seconds, much shorter than the median time between Wi-Fi probes from a smartphone of 32 seconds.

Here’s a summary of the differences:

The advantages of passive Wi-Fi

Wi-Fi was designed for local area networking, allowing smartphones to be detected over much longer distances than BLE. This longer detection range makes passive Wi-Fi particularly well suited for large-area audience measurement, including retail environments, public spaces, and out-of-home (OOH) advertising.

Another key advantage of passive Wi-Fi is the relatively slow rotation of randomized MAC addresses. Because devices typically retain the same randomized address for extended periods, BlueZoo can more effectively measure visitor recurrence, shopper journeys, and movement between locations.

It’s also important to note that audience measurement relies on the 2.4 GHz Wi-Fi band, not the newer 5 GHz or 6 GHz bands. The propagation characteristics of 2.4 GHz signals provide greater detection distances and more reliable audience measurement across large physical environments.

Wi-Fi is best suited for:

• Large-area audience measurement
• Direct measurement of both Android and iOS smartphones
• Unique visitor recurrence measurement
• In-store shopper journey mapping

The advantages of Bluetooth Low Energy (BLE)

Bluetooth Low Energy (BLE) became a powerful audience measurement technology with the introduction of Apple’s Continuity Protocol, which causes iPhones to broadcast BLE advertisements approximately once every second. This frequent broadcasting enables highly precise measurement of short dwell times and fast-moving audiences.

Because only iPhones broadcast BLE advertisements and BLE has a much shorter detection range than Wi-Fi, passive BLE is best viewed as a complement to passive Wi-Fi rather than a replacement. It excels in applications requiring precise measurement within small detection zones, such as retail media displays, checkout lanes, kiosks, and smaller digital signage viewsheds.

BLE is best suited for:

• Measuring short dwell times with high temporal precision, where the detecting only iOS smartphones will have minimal impact
• Counting fast-moving audiences of signage with small viewsheds (e.g. in-aisle) with high precision,
• Environments where aggregate counts of impressions are satisfactory and the unit share of iPhone is substantial (because BLE only detects iPhones)

The BlueZoo product architecture

BlueZoo sensors passively listen for Wi-Fi probes. These sensors can be BlueZoo’s own hardware devices or can be third-party hardware running BlueZoo firmware.  Our sensors listen for phones in a “detection zone”.  The shape of the detection zone (either spherical or conical) is defined by the “pattern” of the antenna employed.  The “size” of the detection zone is determined by the minimum signal power of the probes received from the smartphone.  The closer the smartphones are to our sensor’s antenna, the higher their signal power. 

Based on the trillions of probes heard by our sensors worldwide, our cloud systems are able to calculate a real time count of smartphones in the detection zone, along with length of time (i.e. dwell time) a phone remains in a zone, among other details.  We don’t know who owns the phone or why the owner has visited, but we often know if they’ve been here before and sometimes where they have come from.