SECNORA® is a CREST Accredited cyber security focused consulting firm with expertise in Information Security and Governance, Risk, Compliance.
For decades, Wi-Fi has had one job, connecting devices to the internet. Advances in wireless networking are now expanding that role beyond communication alone. With billions of Wi-Fi enabled devices already deployed across homes, enterprises and industrial environments, that expansion has a large installed base to work with.
This shift has given rise to Wi-Fi sensing, a technology that lets wireless networks detect movement, occupancy and subtle human activity by analysing how radio signals interact with the environment around them. Instead of cameras, infrared sensors or wearables, it uses the same radio waves that already power your internet connection. The IEEE ratified the 802.11bf amendment in 2025, formalising Wi-Fi sensing as a standard feature rather than a vendor-specific hack which is what makes broader adoption across smart homes, healthcare, industrial automation and enterprise environments a near-term question rather than a hypothetical.
These capabilities bring real convenience and automation. They also introduce privacy considerations that organisations will need to evaluate carefully as Wi-Fi sensing becomes more widely adopted.
Wi-Fi sensing detects movement and environmental change by analysing variations in Wi-Fi radio signals. Traditional Wi-Fi only transfers data between devices. Wi-Fi sensing pulls additional information from the physical characteristics of the signal itself. Every object in a room, including walls, furniture and people, affects how radio waves travel and by measuring those changes, a network can tell whether someone has entered a room, moved across a space, or made a particular gesture.
802.11bf defines three sensing configurations. Monostatic sensing uses a single device’s own transmit and receive antennas. Bistatic sensing splits the role between two separate devices, typically an access point and a station. Multistatic sensing extends this to multiple transmitters and receivers working together for wider coverage. Wi-Fi sensing can often run on this existing infrastructure which is what makes it attractive for smart buildings and large IoT deployments where minimising additional hardware is a priority. And unlike camera-based systems, it doesn’t capture images or video. It interprets patterns from radio wave reflections instead.
Rather than acting solely as communication platforms, future networks will also function as sensors capable of reading activity within their coverage area. That added awareness lets buildings, devices and software respond more naturally to the people around them.
The technology is already being tested across several industries. Smart homes can automate lighting, heating and security based on occupancy. Healthcare providers are exploring passive monitoring for fall detection and patient movement without a wearable device. Businesses are looking at workspace optimisation and manufacturers see potential in worker safety and industrial monitoring.
Every Wi-Fi signal transmitted by a router interacts with its surroundings before reaching a receiving device. As the signal reflects off walls, furniture and other objects, it creates a unique wireless fingerprint through a phenomenon known as multipath propagation. Even small environmental changes such as a person walking through a room, alter this fingerprint in measurable ways.
IEEE 802.11bf standardises Wi-Fi sensing through a four-phase process: session setup, measurement setup, sensing measurement and reporting. Measurements can be collected using trigger-based (TB) or non-trigger-based (NTB) sensing while Sensing by Proxy enables an access point to perform sensing on behalf of devices with limited capabilities.
Modern Wi-Fi technologies such as Wi-Fi 6 and Wi-Fi 7 further improve sensing accuracy using Orthogonal Frequency-Division Multiplexing (OFDM). By dividing each wireless channel into hundreds of subcarriers, OFDM provides more detailed channel measurements, enabling more accurate movement detection and occupancy estimation.
At the centre of Wi-Fi sensing is Channel State Information (CSI), a detailed measurement of how signals propagate between transmitter and receiver. Rather than recording only signal strength, CSI captures phase, amplitude, timing and frequency response, describing the state of the wireless channel at a given moment.
Someone walking across a hallway produces noticeable fluctuations, opening a door or moving a chair produces a different pattern again. Under controlled conditions, high-frequency Wi-Fi systems have been shown to pick up movement as subtle as chest expansion during breathing. On their own, though, these readings are just large sets of numbers with little practical meaning.
That’s where machine learning comes in. Algorithms analyse CSI samples, gathered either from dedicated Null Data Packets in active sensing or from ordinary ambient traffic in passive sensing, to identify patterns tied to walking, sitting, standing, gesturing and occupancy.
Sensing accuracy depends heavily on the frequency band in use. 802.11bf operates across license-exempt bands roughly between 1 GHz and 7.125 GHz, plus a separate allocation above 45 GHz. The sub-7 GHz range offers enough resolution to detect movement, estimate occupancy and recognise general activity in a room, while penetrating walls and other obstacles reasonably well.
Higher-frequency directional multi-gigabit (DMG) sensing, particularly 60 GHz millimetre-wave communication, offers far greater spatial resolution because of its shorter wavelength and under suitable conditions can pick up fine hand gestures and finger-level movement on a keyboard. Its range is shorter but it points to where Wi-Fi sensing is headed as the technology matures.
Instead of installing dedicated motion sensors or cameras, organisations can often use existing wireless access points to gain insight into their physical environments, lowering deployment cost while extending what a conventional Wi-Fi network can do. Sensing by Proxy also means devices without full sensing hardware of their own can still request measurements through a capable AP which widens the practical deployment footprint beyond flagship hardware.
In smart homes, Wi-Fi sensing can automate lighting, climate control and security. Healthcare providers are exploring it for fall detection, patient monitoring and sleep analysis. Commercial buildings can use it to optimize workspace use and energy consumption, while industrial facilities are evaluating it for worker safety and equipment monitoring.
Beyond its impressive technical features, Wi-Fi sensing brings along a fresh set of privacy concerns. While the IEEE 802.11bf standard has built-in mechanisms to safeguard the integrity of sensing measurements from spoofing and tampering, the level of privacy protection largely depends on two factors, the standard itself and how different vendors choose to implement these sensing features and manage the data that comes from them.
In contrast to the related IEEE 802.11az ranging standard, IEEE 802.11bf does not specify a secure Long Training Field (secure-LTF) mechanism for sensing Null Data Packets (NDPs). This means that the confidentiality of sensing signals is not consistently defined by the standard, it varies based on how each vendor decides to implement and deploy their solutions. Therefore, organizations looking into Wi-Fi sensing should take a close look not just at the capabilities of devices that comply with IEEE 802.11bf but also at the privacy controls, firmware security and data governance practices that the manufacturer offers.
Wi-Fi sensing really broadens the attack surface beyond just the usual network communications. If attackers manage to sneak into sensing data or take advantage of weaknesses in the wireless infrastructure, they could potentially figure out occupancy patterns or track behavioral trends over time. While these situations often need specialized hardware, good signal conditions or specific vulnerabilities in the implementation, they serve as a crucial reminder that we need to give physical-layer security the same level of attention as we do traditional network security.
Research has also shown that CSI-based classifiers can pick up on privacy-sensitive signals that go beyond just detecting occupancy. They can even identify things like keyboard typing patterns, hand gestures and gait-based biometric identification. Although most of this research is still happening in controlled environments, as CSI access transitions from proprietary vendor hacks to the more open and interoperable MAC-layer signaling defined by 802.11bf, it is becoming easier not harder to run these classifiers on a broader range of everyday hardware.
While Wi-Fi sensing is designed to improve automation rather than monitor individuals, organisations can reduce potential privacy and security risks by following good wireless security practices:
The performance of Wi-Fi sensing is influenced by several factors including the layout of the building, the placement of access points, RF interference, and the surrounding environment. Things like thick walls, metal structures, reflective surfaces and busy wireless environments can disrupt signal propagation, leading to a drop in sensing accuracy. Additionally, variations in antenna design, MIMO capabilities, chipset implementation and access to Channel State Information (CSI) mean that different hardware platforms can experience varying levels of sensing performance.
On the flip side, privacy is another significant hurdle. Although Wi-Fi sensing gathers less personally identifiable information compared to cameras or microphones, it can still provide insights into occupancy, movement patterns and behavioral trends. To safeguard this data, strong governance is crucial, which includes secure data handling, access controls and clear retention policies. As more people start using this technology, it will be vital for vendors to be transparent, adhere to privacy-by-design principles and maintain consistent implementation practices to ensure Wi-Fi sensing is rolled out responsibly while keeping user trust intact.
IEEE 802.11bf represents an important milestone in the evolution of wireless networking by establishing the first standardised framework for Wi-Fi sensing. By defining common procedures for sensing measurements, reporting and interoperability, the amendment enables vendors to develop compatible sensing solutions across consumer, enterprise, healthcare and industrial environments. As Wi-Fi 6, Wi-Fi 7 and future wireless technologies continue to mature, access points are expected to evolve beyond communication devices into intelligent platforms capable of simultaneously providing connectivity and environmental awareness.
Wi-Fi sensing is likely to expand well beyond occupancy detection and gesture recognition into applications such as health monitoring, robotics, smart buildings, industrial automation and context-aware computing. As these capabilities become more sophisticated, equal attention will need to be given to privacy engineering, secure firmware, robust access controls and responsible handling of sensing data. Organisations that combine technical innovation with strong governance and transparent implementation practices will be better positioned to realise the benefits of Wi-Fi sensing while maintaining user trust and regulatory compliance.
Wi-Fi sensing represents a significant evolution in wireless networking. By analysing how radio waves interact with their surroundings, compatible systems can detect movement, estimate occupancy, recognise gestures and enable a new generation of intelligent applications without relying on cameras or wearable devices. Combined with advances in artificial intelligence and modern Wi-Fi standards, it has the potential to transform smart homes, healthcare, industrial automation and enterprise environments.
As wireless networks evolve from communication platforms into environmental sensors, organisations will need to look beyond traditional network security. Privacy, sensing data governance, firmware security and implementation-specific protections will become just as important as encryption and access management. IT directors, compliance officers and security teams evaluating IEEE 802.11bf capable devices should assess not only sensing accuracy and functionality but also how vendors secure sensing measurements, protect collected data and address privacy throughout the product lifecycle. As organisations begin adopting Wi-Fi sensing and other connected technologies, evaluating the security of the underlying IoT ecosystem becomes equally important. SECNORA’s IoT Security services focus on assessing connected devices, wireless infrastructure and IoT ecosystems to help organisations strengthen their overall security posture.
SECNORA® is a CREST Accredited cyber security focused consulting firm with expertise in Information Security and Governance, Risk, Compliance.
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