The ubiquity of WiFi-enabled devices raises the need for advanced network monitoring and management due to security and privacy issues associated with wireless networks. One method is Network Traffic Classification (NTC). However, robust and resilient NTC when traffic is encrypted and without compromising privacy is challenging. One possibility is to use WiFi Channel State Information (CSI), as it will change depending on the characteristics of the information being transmitted. In this article, we show that it is possible to use CSI for NTC by extracting CSI amplitudes from different network traffic streams, and then using this information to create a feature set that can be used with machine learning classifiers to develop a novel NTC mechanism. We show the robustness of our CSI-based NTC by considering real-world scenarios under different wireless interference, namely overlapping frequencies, location-based interference, and interference generated by various network streams. Consequently, our proposed NTC scheme achieved 0.84 NTC F-score as a baseline, and we identify that spectrally overlapping interference reduces the overall F-score of the CSI-based NTC classifier by upto 0.6. For traffic classes with similar characteristics, the proposed framework achieved an NTC F-score above 0.95, corroborating the scalability of our non-intrusive sensing technology. 1

Ensuring the integrity and safety of perishable goods within cold storage facilities has become a paramount concern for industries ranging from pharmaceuticals to food production. Meanwhile, as Integrated Sensing and Communication (ISaC) capable WiFi communication is on the horizon, We examine the feasibility of wireless sensing for cold storage monitoring. To this end, we aim to leverage the ubiquitous WiFi signals from commercial Internet-of-Things (IoT) devices, combined with lightweight Artificial Intelligence (AI), to monitor cold storage temperature without using dedicated IoT temperature sensors. Specifically, we build a sensing framework implemented on WiFi-enabled IoT hardware devices. The proposed framework uses the channel state information (CSI) of WiFi signals to monitor temperature variations within cold storage facilities by adopting a classification-based machine learning approach. Our experimental results empirically confirm the correlation between CSI and ambient temperature and evaluate the framework's performance at different locations within cold storage. Furthermore, the classification models have been successfully employed to accurately categorise temperature ranges with an accuracy of 92%, demonstrating the feasibility of wireless sensing techniques in monitoring cold storage facilities.

Abstract.Traffic infrastructure safety is vital to society, and critical traffic corridor breakdowns could halt cities. Particularly, vehicular tunnels are notable due to the constrained space and challenges to emergency response. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of Internet-of-Things (IoT) enabled WiFi sensing technology for environmental safety monitoring using versatile, low-cost embedded devices. As a proof-of-concept, we deployed our WiFi sensing devices in an urban underwater tunnel to monitor the tunnel temperature and fire accidents. Specifically, we set up one long-term deployment for temperature monitoring and conducted two fire detection experiments in the tunnel using a small-scaled bonfire and an actual vehicle fire. Our experiments show that the proposed WiFi sensing system could accurately monitor the ambient tunnel temperature to an error of≤0.4∘C during the long-term deployment. In the smallscaled bonfire experiment, we developed a novel empirical model to characterise fire intensity using WiFi signals. This proposed linear regression model has a coefficient of determination of 0.76. Finally, we validate the potential of our wireless environment vision system for detecting fire events from ambient environments using a low-complexity machine learning classifier enabling in situ processing locally on IoT devices.

Road tunnel safety is vital to society, and break-downs due to fire accidents could halt cities. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of our WiFi sensing technology for environmental safety monitoring in a versatile and low-cost manner using commercial WiFi-enabled IoT devices. As a proof-of-concept, we conducted both a small-scale bonfire experiment and a real vehicle fire detection experiment in a traffic tunnel using our proposed WiFi environment vision system. As a result, we verified that WiFi sensing can be used to monitor fire accidents from the bonfire experiment, and developed a novel empirical model to characterise car fire intensity using WiFi signals with a coefficient of determination of 0.319. Additionally, we also demonstrated the potential for detecting fire outbreaks using a low-complexity machine learning-based classifier, capable of identifying the occurrence of fire accidents on the edge.

Thermal source localization is crucial in detecting fires and other anomalies, allowing emergency responders to quickly locate the source of the hazard to minimize damage. With recent advancements in WiFi sensing technology for thermal detection and the emergence of Integrated Sensing and Communication (ISAC) on WiFi systems, we are interested in exploring the potential of using WiFi sensing technology for thermal source localization using commercially available Internet of Things (IoT) WiFi hardware. Our study investigates the potential of using WiFi Channel State Information (CSI) to locate thermal sources. Using the cost-effective and power-efficient ESP32 microcontroller, we propose a WiFi sensing-based system to predict the location of a heat source. We demonstrate that the WiFi CSI signatures can be used to identify localized temperature changes in a timely manner. Our key contribution is effectively filtering out undesired components from the CSI and using smart subcarrier selection to form the feature set for the machine learning algorithm. We present a Support Vector Machine (SVM)–based classifier that achieves up to 99% accuracy in predicting the relevant heat source location. We also identify the optimal WiFi sensing device configuration, demonstrate our technology’s fast response time, and shed insights on the impact of heat source distance on localization performance. Our findings offer promising solutions for low-cost, environmentally friendly monitoring systems.

With the emergence of next generation networks, Network Traffic Classification (NTC) has seen greater importance in network management and security. Recently, Channel State Information (CSI) based WiFi sensing techniques have shown their potential for NTC applications [1], [2] as a privacy-preserving yet effective tool. As CSI could be prone to interference, this paper examines the performance of CSI-based NTC models under interference-induced adversarial attacks. Specifically, the impact of spectral allocation of the interference, underlying interfering network traffic type, and physical location of the interference are studied and quantified. We conducted experiments using off-the-shelf devices to test the NTC performance, with and without the adversarial interference attack of ping, buffered video streaming, and live video streaming network traffics. Subsequently, we found that spectral allocation of the attacking interference and the underlying traffic types of interference could be used to deceive the established NTC model, and different network traffic types show different robustness across the interference cases. Namely, ping suffers the most in the spectrally manipulated attack, with the classification accuracy down to as low as 23.2%, whereas Twitch might be completely misidentified as other traffic in underlying traffic type controlled attack.

WiFi Sensing has received tremendous attention in Recent Literature, demonstrating the ability to leverage ubiq-uitous commercial WiFi devices to sense Human activities and environmental occupancy. We identify that in all environments fire-safety is vital, and this paper demonstrates the suitability for using WiFi to sense fire. Using commodity Raspberry Pi devices on the 5GHz WiFi band we demonstrate a temporal shift in WiFi Channel State Information (CSI) Amplitude, before, during, and after the ignition of a flame. We further emphasise the presence of fire by observing the spread of CSI Amplitudes, noting that CSI takes much more diverse values in the presence of fire. This result is exacerbated by the frequency selective behaviour of OFDM subcarriers, where some subcarriers displayed larger variation in CSI amplitude due to the fire. The WiFi Fire sensing model was evaluated in an ideal setup with a gas flame to remove material deformation as a variable, and subsequently in a real-world scenario with the ignition of building cladding.

Extensive literature has shown the possibility of using WiFi to sense large scale environmental features such as people, movement, and human gestures. To our best knowledge, there has been no investigation on identifying the microscopic changes in a channel due to atmospheric temperature variations. We identify this as a real world use case, since there are scenarios such as Data Centres where WiFi traffic is omnipresent and temperature monitoring is important. We develop a framework for sensing temperature using WiFi Channel State Information (CSI), proposing that the increased kinetic energy of ambient gas particles will affect the wireless link. To validate this, our paper uses low wavelength 5GHz WiFi CSI from commodity hardware to measure how the channel changes as the ambient temperature is raised. Empirically, we demonstrate that the CSI amplitude value drops at a rate of 13 per degree Celsius rise in the ambient temperature based on the testing platform, and developed regressions models with ± 1°C accuracy in the majority of cases. Moreover, we have shown that WiFi subcarriers exhibit a frequency-selective behaviour in their varying responses to the rise in ambient temperature.