The rapid development of Integrated Sensing and Communication (ISAC) has driven the need for robust and adaptable indoor positioning systems. Channel State Information (CSI)-based fingerprint localization has emerged as a promising solution, but existing methods face significant challenges as their sensitivity to noise often leads to poor localization accuracy and deep neural networks suffer from computational inefficiency. In this paper, we propose KFBK, a novel framework that integrates wavelet decomposition, Kolmogorov-Arnold Network (KAN)-based feature learning, and a dynamic fusion mechanism of the Broad Learning System (BLS) and KAN. It first applies a hybrid wavelet denoising strategy with decomposition and adaptive thresholding to suppress noise while preserving critical CSI patterns. Then, a KAN-based feature extractor with splineoptimized activation functions captures complex nonlinear spatiotemporal dependencies in high-dimensional CSI data, enabling dimensionality reduction without losing essential information. Finally, a temperature-controlled dynamic weighting mechanism adaptively adjusts the contributions of BLS and KAN to improve overall localization performance under varying conditions. Extensive evaluations in two real-world environments demonstrate that KFBK outperforms state-of-the-art methods in localization accuracy and environmental adaptability while maintaining realtime responsiveness and computational efficiency.

In this study, we propose BreathPass, a non-invasive authentication system that characterizes the chest/abdomen movement incurred by human breath to enable unlocking smart devices while wearing various types of face covers, clothing, in different postures, and dynamic status such as walking or running. To capture the breathing pattern, BreathPass uses speakers to emit ultrasound signals. The signals are reflected off the chest wall and abdomen and then back to the microphone, which records the reflected signals. The system then extracts the breathing pattern from the reflected signals, and further extracts fingerprints from the breathing pattern, and use these fingerprints to perform authentication. We carefully design a Deep Neural Network model and explore its capacity for feature abstraction in order to address the challenges associated with tiny position changes resulting in different breathing patterns and the extremely narrow bandwidth of breathing. We implement a prototype and conduct extensive experiments. BreathPass achieves an overall accuracy of 83%, a true positive rate of 73%, and a false positive rate of 5%, according to performance evaluation results.

With the advent of the 5.5G era, integrated sensing and communication (ISAC) technology has emerged, demonstrating its high-precision sensing capabilities across industries like wireless communication, intelligent transportation, and smart homes. Researchers are particularly exploring the use of channel state information (CSI) from WiFi signals for human activity recognition. However, current research primarily relies on complex network models to manage vast CSI data for developing accurate recognition models. It confronts two main challenges in time-sensitive applications: a limited number of samples and the need for timely solutions. In this paper, we present a prototype network model that combines CNN and self-attention to address the issues raised above, namely Proto-CSNet. First, we use time-frequency analysis and filter the data to transfer the data from the time domain to the frequency domain and eliminate noise and outliers from the samples. Then, based on the ACmix model, we modify and build a novel custom feature extraction module that retains the fused CNN's local feature capture capabilities as well as self-attention's global information processing advantage. Finally, we merge the custom modules with CNNs to form a prototype network. A real-world dataset is constructed including 11 different human activities, and the extensive experimental resutls demonstrate that Proto-CSNet outperforms existing algorithms with a model inference accuracy exceeding 95%.

With the rapid development of Integrated Sensing and Communication in 6G, Channel State Information (CSI)-based fingerprint indoor localization technology is becoming crucial. However, during the offline phase, the fingerprint database update process using crowdsourcing techniques is prone to noise interference and incomplete coverage, and fitting Gaussian regression models requires extensive computational resources. In this paper, we propose a fine-grained CSI fingerprint database update method based on Deep-Broad Learning system (DeepBLS) and Generative Adversarial Networks (GAN), termed DBLG. Firstly, we employ the combined DeepBLS network for the initial construction of the global CSI fingerprint database. Subsequently, we utilize GAN to extract features from the raw data, predict and update the global CSI fingerprint database, and construct a high-precision fingerprint database using confidence coefficients. Finally, We implement the proposed algorithm in two real-world environments and conduct extensive experiments to verify its performance. Compared to several existing methods, our approach shows superior performance in updating the CSI finger-print database, achieving a 46.78 % improvement in localization accuracy.

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SVD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.

Indoor localization availability is still sporadic in industry, especially at the black-hole, i.e., there only exist cellular signals, no GPS or WiFi signals. Based on our 2-year observations at the DiDi ride-hailing platform in China, there are$ 68\,\text{k}$orders everyday created at black-hole. In this paper, we presentTransparentLoc, a large-scale cellular localization system for pickup position recommendation of the DiDi platform. Specifically, we design a CNN model for real-time localization based on a crowdsourcing fingerprint set constructed by outdoor trajectories and abnormal cell tower detection. Then we leverage a DeepFM model to recommend an optimal pickup position for passengers. We share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, temporal variations, and abnormal cell towers in terms of four major service metrics, i.e., pickup position error, over-30-meters ratio, cancel ratio, and call ratio. The large-scale evaluations show that our system achieves a$ 0.54\,\text{m}$lower median pickup position error compared to the iOS built-in cellular localization system, regardless of environmental changes, smartphone brands/models, time, and cellular providers. Additionally, the over-30-meters ratio, cancel ratio, and call ratio have significant reductions of 0.88%, 0.88%, and 5.13%, respectively.

As the development of the Internet of Things, location service plays a more important role in mobile computing. To provide location service for the already deployed devices and objects, we present LiTag, a visible light-based localization and posture estimation solution with commercial off-the-shelf (COTS) cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometric relation in camera imaging. To solve the localization ambiguity, we propose an ambiguity-avoidance method based on a projection relationship. LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1$cm$in the 2D plane, a median error of 5$cm$in the 3D space, and posture estimation with a median error of$0.8^{\circ }$. We believe that LiTag has high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with widely deployed cameras.

Location awareness is the basis for enabling pickup service at ride-hailing platforms. In contrast to the almost pervasive coverage outdoors, indoor localization availability is still sporadic in industry since it largely relies on RF signatures from certain IT infrastructure, e.g., WiFi access points. Based on our 2-year observations at DiDi ride-hailing platform in China, there are 68k orders everyday created at black-hole, i.e., where only cellular signals exist. In this paper, we present the design, development, and deployment of TransparentLoc, a large-scale cellular localization system for pickup position recommendation, and share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, and temporal variations. Our system outperforms the iOS built-in cellular localization system in terms of four major service metrics, regardless of environmental changes, smartphone brands/models, time, and cellular providers.

In this paper, we propose NEC (Neural Enhanced Cancellation), a defense mechanism, which prevents unautho-rized microphones from capturing a target speaker’s voice. Compared with the existing scrambling-based audio cancellation approaches, NEC can selectively remove a target speaker’s voice from a mixed speech without causing interference to others. Specifically, for a target speaker, we design a Deep Neural Network (DNN) model to extract high-level speaker-specific but utterance-independent vocal features from his/her reference audios. When the microphone is recording, the DNN generates a shadow sound to cancel the target voice in real-time. Moreover, we modulate the audible shadow sound onto an ultrasound frequency, making it inaudible for humans. By leveraging the non-linearity of the microphone circuit, the microphone can accurately decode the shadow sound for target voice cancellation. We implement and evaluate NEC comprehensively with 8 smartphone microphones in different settings. The results show that NEC effectively mutes the target speaker at a microphone without interfering with other users’ normal conversations.

There are various methods to monitor human respiration. Traditional methods of monitoring the human respiratory process often rely on complex medical equipment, which makes it difficult for users to operate. Nowadays, more and more researchers are focusing on smartphone-based systems that use mobile phones to transmit ultrasound to the chest and abdomen of the human body and use the unique reverse echo of ultrasound to collect respiratory signals. However, this method is easily disturbed by the environment, clothing, equipment, and other factors. Thus, the accuracy is unsatisfactory. This paper presents a method to optimize the respiratory signals collected by ultrasound. This method is based on supervised learning. Piezoelectric sensors and mobile phones are used to monitor human respiratory signals. A Long-Short Term Memory (LSTM) is established to learn the expression from ultrasonic signals to piezoelectric signals to improve the accuracy of signal acquisition. The results show that the model has good performance in both the time and frequency domains, achieving less than 0.05 mean absolute error (MAE) and 0.8779 intersections over union (IoU). The model can be used to optimize the ultrasound respiratory signals.

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, the idea of piracy tracing is not widely used at the moment, since existing fingerprinting-based streaming systems fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, a polarized and randomized SVD watermarking scheme suitable for short segments, and a collusion-resistant fingerprinting scheme optimized for large-scale streaming services. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing SVD watermarking schemes, the proposed watermarking scheme improves the watermark extraction accuracy by 2.25x at most and 1.5x on average. Compared with existing collusion-resistant fingerprinting schemes, the proposed scheme catches more colluders and improves the recall rate by 26%.

As the most widely applied public-key cryptographic algorithm, RSA is now integrated into many low-cost devices such as IoT devices. Due to the limited resource, most low-cost devices only ship a 2048-bit multiplier, making the longest supported private key length as 2048 bits. Unfortunately, 2048-bit RSA keys are gradually considered insecure. Utilizing the existing 2048-bit multiplier is challenging because a 4096-bit message cannot be stored in the multiplier. In this paper, we perform a thorough study of RSA and propose a new method that achieves the 4096-bit RSA cryptography with the existing hardware. We use the Montgomery modular multiplication and the Chinese Remainder Theorem to reduce the computational cost and construct the necessary components to compute the RSA private key operation. To further validate the correctness of the method and evaluate its performance, we implement this method on a micro-controller and build a testbed named CanoKey with three commonly used cryptography protocols. The result shows that our method is over 200x faster than the naive method, a.k.a., software-based big number multiplications.

Visible Light Positioning (VLP) has attracted much research effort recently. Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding the mobile phone). This incurs a high deployment, maintenance and usage cost. We present RainbowLight, a low-cost ambient light 3D localization approach that is easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference, and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. We implement RainbowLight and extensively evaluate its performance in various environments. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in the daytime.

The widespread adoption and ubiquity of smart devices equipped with microphones (e.g., cellphones, smartwatches, etc.) unfortunately create many significant privacy risks. In recent years, there have been several cases of people's conversations being secretly recorded, sometimes initiated by the device itself. Although some manufacturers are trying to protect users' privacy, to the best of our knowledge, there is not any effective technical solution available. In this work, we present Patronus, a system that can both prevent unauthorized devices from making secret recordings while allowing authorized devices to record conversations. Patronus prevents unauthorized speech recording by emitting what we call a scramble, a low-frequency noise generated by inaudible ultrasonic waves. The scramble prevents unauthorized recordings by leveraging the nonlinear effects of commercial off-the-shelf microphones. The frequency components of the scramble are randomly determined and connected with linear chirps, and the frequency period is fine-tuned so that the scramble pattern is hard to attack. Patronus allows authorized speech recording by secretly delivering the scramble pattern to authorized devices, which can use an adaptive filter to cancel out the scramble. We implement a prototype system and conduct comprehensive experiments. Our results show that only 19.7% of words protected by Patronus' scramble can be recognized by unauthorized devices. Furthermore, authorized recordings have 1.6x higher perceptual evaluation of speech quality (PESQ) score and, on average, 50% lower speech recognition error rates than unauthorized recordings.

The development of Internet of Things calls for ubiquitous and low-cost localization and posture estimation. We present LiTag, a visible light based localization and posture estimation solution with COTS cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometry relation between the camera image plane and the real world. Unlike existing marker-based visible localization and posture estimation approaches, LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1.6 cm in the 2D plane, a median error of 12 cm in the 3D space, and posture estimation with a median error of 1°. We believe that LiTag has a high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with existing widely deployed cameras.

Visible Light Positioning (VLP) has attracted much research effort recently. Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding mobile phone). This incurs a high deployment, maintenance and usage overhead. We present RainbowLight, a low cost ambient light 3D localization approach easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. We implement RainbowLight and extensively evaluate its performance in various environments. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in daytime.

Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding mobile phone). This incurs a high deployment, maintenance and usage overhead. In this demo, we present RainbowLight, a low cost ambient light 3D localization approach easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. This demo shows our prototype of implementation, with simple photo capturing and deriving location of camera on mobile phone. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in daytime.