Wi-Fi Sensing 101
Wi-Fi as a Sensor Modality
Wi-Fi sensing utilizes the Physical (PHY) and Medium Access Control (MAC) layer properties of the IEEE 802.11 standard to acquire measurements that estimate specific features of objects within an area of interest.1
- Features: Range, velocity, angular motion, and presence.
- Objects: People, vehicles, or the environment itself, depending on the application task.
- Area of Interest: A strictly bounded physical space, such as a single room, a vehicle cabin, or a parking lot.
The Physical Layer
The Physical layer is where we look at how Wi-Fi packets are modulated into electromagnetic waves (EM waves) and transmitted over the air (EM waves do not require a medium to travel through, but are nonetheless affected by one if it is present). We also look at how that signal gets affected as it propagates through the environment via phenomena such as scattering, diffraction, and interference. These effects occur on the wave while it is travelling through the channel, changing its amplitude and phase. The change from each effect depends on the channel’s state (e.g., are there obstacles blocking the channel, is the channel moving or static).
A key effect one needs to consider when using Wi-Fi (whether for sensing or communication) is multipath propagation. As radio frequency (RF) signals travel from transmitter to receiver, they reflect, diffract, and scatter off physical boundaries and dynamic obstacles. Each multipath component travels a different path, meaning each has its own unique journey that must be accounted for and compensated for at the receiver to recover the original signal. The receiver captures these signal variations as Channel State Information (CSI). Currently, CSI is the most robust metric available for Wi-Fi sensing.2
The primary utilization of the Physical layer in Wi-Fi sensing is to extract CSI, via sending known training symbols and measuring the channel’s response to those symbols, allowing us to infer the channel’s state.
Radio Frequency (RF) refers to a specific range (band) of the electromagnetic spectrum, spanning from around 20 kHz to 300 GHz. The most common Wi-Fi bands are 2.4 GHz, 5 GHz, and 6 GHz. As the band increases in frequency, the wavelength decreases, and the signal becomes more easily affected by its environment. This means it can capture more detailed information, but it is also more susceptible to noise and attenuation (the weakening of a signal as it travels through a medium). Consequently, specific applications are better suited for certain frequency bands over others.
The Medium Access Control Layer
When mapping Wi-Fi sensing to the OSI model, the MAC layer is the highest layer from which we extract metadata. The MAC layer is responsible for governing flow control, channel allocation, multiplexing, multiple access, and MAC addressing.
In Wi-Fi sensing, we utilize the MAC layer properties of MAC addressing to manage multiple stations, enabling the construction of targeted sensing topologies (e.g., a sensing mesh).
Which topology we use depends on the application. For example, a star topology yields more accurate results for specific human activity recognition (HAR) applications, but the complexity of setting up and maintaining a star topology may not be worth it for a simple presence detection application, where a simpler point-to-point topology may suffice.
To maintain a high sampling rate without paralyzing the network with massive amounts of data, Wi-Fi sensing systems often exploit specific MAC frame types, such as Null Data Packets (NDPs) or beacon frames. An NDP contains no payload data; it consists entirely of the preamble and header necessary for the receiver to measure the channel. Because they are exceptionally short, NDPs consume minimal airtime and reduce the probability of collisions.
TLDR
The basic premise of using Wi-Fi as a sensing modality is to analyze how the EM waves behave as they propogate through the enironment, and infer from that the state of the environment, as the IEE 802.11 is vast and enables many protocols to build on it (such as ESP-NOW), we focus our attention on two OSI layers and their corresponding properties:
- Physical Layer: Used for channel estimation and extracting signal metadata (trhough metrics such as Channel State Information).
- Medium Access Control Layer: Used to manage sensing topologies via MAC addressing, coordinate multiple sensors, and to exploit specific frame types (like Null Data Packets) to maintain high sampling rates without causing network congestion.
When to Utilize Wi-Fi Sensing
Compared to dedicated sensing hardware like cameras or LiDAR, Wi-Fi transceivers are ubiquitous and inexpensive. This accessibility makes Wi-Fi an optimal candidate for experimenting with edge computing deployments, enabling localized processing on resource-constrained microcontrollers without external servers. Furthermore, it offers three distinct operational advantages:
- NLOS Capability: RF signals penetrate common building materials, allowing for non-line-of-sight operations.
- Device-Free: Targets do not need to carry or wear transceivers or peripherals.
- Privacy-Preserving: The coarse-grained nature of RF sensing provides protection against personally identifiable information, such as facial features.
Each Wi-Fi frequency band offers unique trade-offs between spatial resolution and environmental penetration, making them suitable for different sensing applications.
Sub-6 GHz Bands (2.4 GHz, 5 GHz, 6 GHz) These bands offer broad spatial coverage and high environmental penetration, making them optimal for macroscopic tasks such as:
- Human Activity Recognition (HAR): Detecting falls or tracking routines in care facilities.
- People Counting: Estimating crowd density or room occupancy.
- Sleep Analysis: Passively monitoring sleep stages and macro-movements.
mmWave Bands (60 GHz) Millimeter-wave operates at higher frequencies with wider channel bandwidths, yielding high spatial resolution at the cost of limited range and strict line-of-sight (LOS) requirements.
- Micro-Movement Detection: Capturing localized physiological metrics like precise heart rate and respiration.
- Gesture Recognition: Identifying fine-grained finger movements for human-computer interaction.
Limitations and Challenges
Of course, the No Free Lunch theorem applies here as well. We have to acknowledge the limitations of Wi-Fi sensing and approach the task of building Wi-Fi sensing systems with these constraints in mind, which include:
- Coarse-Grained Sensing: The very same property that makes Wi-Fi sensing privacy-preserving also limits its ability to capture fine-grained details. This also means that the resultant data of Wi-Fi sensing systems is often not directly human-interpretable, requiring machine learning or advanced signal processing to extract meaningful information and insights.
- Domain Dependency: The models used to process PHY metadata are stochastic and highly environment-dependent. Models trained in one physical space frequently fail when deployed in another due to entirely different multipath characteristics. Systems require baseline calibration and remain highly sensitive to static environmental changes (e.g., moved furniture).
- Hardware & Data Fragmentation: The 802.11 PHY layer is mathematically optimized for robust data transmission, not radar telemetry. Most standard network interface controllers (NICs) do not expose PHY metadata to higher level layeers. Accessing it requires reverse engineering or relying on specific firmware patches for limited hardware, although this is becoming less of an issue as we will explore later on.
Originally coming from the optimization field, the No Free Lunch theorem states that there is no such thing as a free lunch. In other words, you don’t get something for nothing, and the benefits you get from using one algorithm often come at the cost of losing out on the benefits of another algorithm. As the solution for this problem is to use a combination of algorithms, we can assume that the same applies to using Wi-Fi as a sensing modality; using a multimodal approach can help us squeeze out the benefits of WLAN sensing.
To address these systems engineering challenges, the IEEE 802.11bf Task Group (WLAN Sensing) has developed an amendment to standardize sensing operations. This standard formalizes capability exchanges and measurement setups at the MAC and PHY layers, providing a unified framework to mitigate current hardware fragmentation.