Blood pressure (BP) measurement is an indispensable tool in diagnosing and treating many diseases such as cardiovascular failure and stroke. Traditional direct measurement can be invasive, and wearable-based methods may have limitations of discomfort and inconvenience. Contact-free BP measurement has been recently advocated as a promising alternative. In particular, Millimeter-wave (mmWave) sensing has demonstrated its promising potential, however it is confronted with several challenges including noise and vulnerability to human's tiny motions which may occur intentionally and inevitably. In this paper, we propose mmBP+, a contact-freemmWave-basedBPmeasurement system with high accuracy and motion robustness. Due to the high frequency and short wavelength, mmWave signals received in the time domain are dramatically susceptible to ambient noise, and deteriorating signal quality. To reduce noise,we propose a novel approach to exploit mmWave signal's characteristics and features in the delay-Doppler-fractional Fourier domain to significantly improve signal quality for pulse waveform construction. We also propose a periodic signal feature based functional link adaptive filter leveraging on the periodic and correlation characteristics of pulse waveform signals to alleviate the impact of human's tiny motions. Extensive experiment results achieved by the leave-one-out cross-validation (LOOCV) method demonstrate that mmBP+ achieves the mean errors of 0.65mmHg and 1.31mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively; and the standard deviation errors of 3.92mmHg and 3.99mmHg for SBP and DBP, respectively.

Physical-layer key generation holds significant potential in establishing cryptographic key pairs for emerging LoRa networks. Nevertheless, current key generation solutions may underperform due to critically impaired channel reciprocity, attributed to the low data rate and long range inherent in LoRa networks. In this study, we presentChirpKey, a novel key generation scheme for LoRa networks. We pinpoint the key hurdles as the coarse-grained channel measurement, inefficient quantization methods, and out-of-range device constraints. To capture fine-grained channel information, we introduce a unique, LoRa-specific channel measurement method that focuses on analyzing chirp-level variations in LoRa packets. We also propose a LoRa channel state estimation algorithm to neutralize asynchronous channel sampling. Instead of the traditional quantization approach, we propose an innovative key delivery method based on perturbed compressed sensing, offering enhanced robustness and security. For LoRa devices beyond each other's communication reach, we integrate relay nodes to ensure reliable key generation. To foster secure group communication, we formulate two protocols that facilitate collaborative key generation across both star and chain configurations. Evaluation across diverse real-world scenarios reveals thatChirpKeyenhances the key matching rate by 11.03–26.58% and increases the key generation rate by 27–49× in comparison to existing leading systems. Our security analysis shows thatChirpKeycan effectively withstand a variety of prevalent attacks. Furthermore, we implement aChirpKeyprototype, demonstrating its capability to operate within 0.2 s.

Knowledge Graphs (KGs) have emerged as a critical technique to enhance recommendation performance by modeling complex relationships and semantics within heterogeneous networks. However, it faces issues such as longtail distribution, structural redundancy caused by semantically similar relations, and susceptibility to noise interference, which severely limit the effectiveness of graph-based recommendations. Aiming to tackle the challenges, we propose Relation-aware Contrastive Learning (RACL), a brand-new framework for knowledge-enhanced recommendations. Specifically, relationdriven subgraph construction is employed to cluster the KG into subgraphs with potential semantic associations, addressing the issue of structural redundancy while alleviating the long-tail effect through the integration of relationship types. Besides, we introduce a relation-aware aggregation module to inject relation-specific semantic features from KG into neighborhood propagation, effectively encoding multi-type relational contexts into user and item embeddings. Furthermore, a graph learner is established, which significantly improves the model's robustness in contexts with sparse and noisy data by integrating selfsupervised signals into model training. Comprehensive experiments on two publicly accessible datasets verify that our RACL surpasses the state-of-the-arts in terms of recommendation efficacy.

Recent research has been exploring ways to streamline device pairing by introducingtouch-to-accessthat minimizes user interaction. It generates pairing keys by extracting features from a shared information source to ascertain if two devices are being held by the same person. While these solutions focus on verifying the authenticity of the device, they do not consider the legitimacy and pairing intent of the device holder. Moreover, the pairing keys exchanged over an open wireless link may be susceptible to eavesdropping attacks. In this paper, we propose a secure device pairing mechanism that utilizes the unique electrical responses of the human body to generate and transmit user-specific pairing keys, ensuring both the user's legitimacy and pairing intent while also improving key transmission reliability. We accomplish this by using the device's built-in microphone to capture ambient sound as entropy and converting it into an electrical signal transmitted by the body for device pairing. We have built a prototype and conducted extensive experiments with 31 participants to evaluate its security and usability. The results demonstrate that our proposed mechanism offers a more secure and reliable option for user-specific pairing keys, contributing to the field of device pairing.

The widespread deployment of unattended LoRa networks poses a growing need to perform Firmware Updates Over-The-Air (FUOTA). However, the FUOTA specifications dedicated by LoRa Alliance fall short of several deficiencies with respect to energy efficiency, transmission reliability, multicast fairness, and security. This article proposes FLoRa+ , energy-efficient, reliable, beamforming-assisted, and secure FUOTA for LoRa networks, which is featured with several techniques, including delta scripting, channel coding, beamforming, and securing mechanisms. Specifically, we first propose a joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Then, we design a concatenated channel coding scheme with outer rateless code and inner error detection to enable reliable transmission for coding gain. Afterward, we develop a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Finally, we present a securing mechanism incorporating progressive hash chain and packet arrival time pattern verification to countermeasure firmware integrity and availability attacks for security gain. Experimental results on a 20-node testbed demonstrate that FLoRa+ improves transmission reliability and energy efficiency by up to 1.51× and 2.65× compared with LoRaWAN. Additionally, FLoRa+ can defend against 100% and 85.4% of spoofing and Denial-of-Service (DoS) attacks.

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03–26.58% and key generation rate by 27–49× compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2 s.

LoRa has emerged as one of the promising long-range and low-power wireless communication technologies for Internet of Things (IoT). With the massive deployment of LoRa networks, the ability to perform Firmware Update Over-The-Air (FUOTA) is becoming a necessity for unattended LoRa devices. LoRa Alliance has recently dedicated the specification for FUOTA, but the existing solution has several drawbacks, such as low energy efficiency, poor transmission reliability, and biased multicast grouping. In this paper, we propose a novel energy-efficient, reliable, and beamforming-assisted FUOTA system for LoRa networks named FLoRa, which is featured with several techniques, including delta scripting, channel coding, and beamforming. In particular, we first propose a novel joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Afterward, we design a concatenated channel coding scheme to enable reliable transmission against dynamic link quality. The proposed scheme uses a rateless code as outer code and an error detection code as inner code to achieve coding gain. Finally, we design a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Experimental results on a 20-node testbed demonstrate that FLoRa improves network transmission reliability by up to 1.51 × and energy efficiency by up to 2.65 × compared with the existing solution in LoRaWAN.

LoRa has emerged as a novel Internet of Things (IoT) communication paradigm, featuring with long-range and low-power transmission capabilities. With the widespread deployment of LoRa networks, the demand to perform Firmware Update Over-The-Air (FUOTA) tasks has become increasingly critical for unattended LoRa devices. However, in practice, three fundamental problems that hinder the performance of FUOTA tasks are revealed, including low energy efficiency, poor transmission reliability, and biased multicast grouping. In this demo, we present a novel FUOTA system, the first work that offers an effective and sustainable solution to achieve energy-efficient and reliable over-the-air firmware updates in LoRa networks. In particular, this system incorporates threefold key modules: delta scripting, channel coding, and beamforming. The delta scripting algorithm unlocks the capability of incremental update, the channel coding scheme ensures the reliability and robustness of large-scale firmware image distribution, and the beamforming strategy as an optional module can further serve the unicast user. Thus, this demo presents a working example of functionality customization to show the efficacy and feasibility of our FUOTA system in LoRa networks.

Facial expression recognition plays a vital role to enable emotional awareness in multimedia Internet of Things applications. Traditional camera or wearable sensor based approaches may compromise user privacy or cause discomfort. Recent device-free approaches open a promising direction by exploring Wi-Fi or ultrasound signals reflected from facial muscle movements, but limitations exist such as poor performance in presence of body motions and not being able to detect multiple targets. To bridge the gap, we propose mmFER, a novel millimeter wave (mmWave) radar based system that extracts facial muscle movements associated with mmWave signals to recognize facial expressions. We propose a novel dual-locating approach based on MIMO that explores spatial information from raw mmWave signals for face localization in space, eliminating ambient noise. In addition, collecting mmWave training data can be very costly in practice, and insufficient training dataset may lead to low accuracy. To overcome, we design a cross-domain transfer pipeline to enable effective and safe model knowledge transformation from image to mmWave. Extensive evaluations demonstrate that mmFER achieves an accuracy of 80.57% on average within a detection range between 0.3m and 2.5m, and it is robust to various real-world settings.

Radio Frequency (RF)-based gait recognition has emerged as a promising technology to authenticate individuals in a pervasive and unobtrusive way. However, a fundamental challenge remains in collecting extensive data of the same user in the same environment. To address this challenge, this paper introduces XGait, a cross-modal gait recognition framework that does not require the prior deployment of RF devices or explicit data collection. The key idea is to leverage the signals of the Inertial Measurement Unit (IMU), which is widely available in modern mobile devices, to simulate the RF signals that would be generated if the same person walked near RF devices. Despite the straightforward idea, several technical challenges need to be addressed due to the diversity of RF devices, the intrinsic difference between IMU signals and RF signals, and the complexity of gait. First, we propose an RF spectrogram generation method to consistently extract essential RF gait data features across different RF signals. Secondly, we propose a generative network-enabled IMU-to-RF translation approach that accurately converts IMU data to RF data. Finally, we design an RF gait spectrogram-specific transformer model to further improve the recognition performance. We conduct a comprehensive evaluation of XGait, involving thirty subjects in three different environments, utilizing three RF devices and seven mobile devices. Experimental results show that XGait consistently achieves over 99% Top-3 accuracy in various scenarios.

Recent efforts in reducing user involvement during device pairing have successfully introduced touch-to-access. To detect whether two devices are being held by the same person, existing touch-to-access solutions extract features from a shared information source to generate pairing keys. They focus on validating the device's authenticity by only requiring the user's simple touching of the device, however, ignore the device holder's legitimacy and pairing intent. Moreover, the pairing keys may be vulnerable to eavesdropping attacks since they are exchanged over an open wireless link (e.g., WiFi or Bluetooth). In this paper, we develop a secure device pairing mechanism that essentially uses the human body to generate and transmit user-specific pairing keys, ensuring the user's legitimacy and pairing intent, as well as improving key transmission reliability. Our work is based on the observation that the human body produces a unique response to the electrical signal flowing through it, and different bodies induce distinct responses to the signal. The built-in microphone on devices captures ambient sound as an entropy source and converts it into an electrical signal, which is subsequently processed and transmitted by the human body for device pairing. We build a prototype using off-the-shelf microphones and conduct extensive experiments with 31 participants to evaluate its security performance and usability. The results show that our system achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.

We present a secure device pairing approach, called BiTouch, using the human body as a conductor to generate and transmit user-specific pairing keys for advancing touch-to-access policy. BiTouch is designed based on the observation that the human body responds uniquely to electrical signals flowing through it. Built-in microphones on devices are essentially used to capture ambient sound as entropy and convert it into an electrical signal, which is subsequently transmitted by the body for device pairing. We implement BiTouch using off-the-shelf microphones and evaluate it with 31 participants. The results demonstrate that BiTouch ensures the user's legitimacy and key transmission reliability, and achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.

Blood pressure (BP) measurement is an indispensable tool in diagnosing and treating many diseases such as cardiovascular failure and stroke. Traditional direct measurement can be invasive, and wearable-based methods may have limitations of discomfort and inconvenience. Contact-free BP measurement has been recently advocated as a promising alternative. In particular, Millimetre-wave (mmWave) sensing has demonstrated its promising potential, however it is confronted with several challenges including noise and vulnerability to human's tiny motions which may occur intentionally and inevitably. In this paper, we propose mmBP, a contact-free mmWave-based BP measurement system with high accuracy and motion robustness. Due to the high frequency and short wavelength, mmWave signals received in the time domain are dramatically susceptible to ambient noise, and deteriorating signal quality. To reduce noise, we propose a novel delay-Doppler domain feature transformation method to exploit mmWave signal's characteristics and features in the delay-Doppler domain to significantly improve signal quality for pulse waveform construction. We also propose a temporal referential functional link adaptive filter leveraging on the periodic and correlation characteristics of pulse waveform signals to alleviate the impact of human's tiny motions. Extensive experiment results achieved by the leave-one-out cross-validation (LOOCV) method demonstrate that mmBP achieves the mean errors of 0.87mmHg and 1.55mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively; and the standard deviation errors of 5.01mmHg and 5.27mmHg for SBP and DBP, respectively.

Mobile deep learning (MDL) has emerged as a privacy-preserving learning paradigm for mobile devices. This paradigm offers unique features such as privacy preservation, continual learning and low-latency inference to the building of personal mobile sensing applications. However, squeezing Deep Learning to mobile devices is extremely challenging due to resource constraint. Traditional Deep Neural Networks (DNNs) are usually over-parametered, hence incurring huge resource overhead for on-device learning. In this paper, we present a novel on-device deep learning framework named MDLdroidLite that transforms traditional DNNs into resource-efficient model structures for on-device learning. To minimize resource overhead, we propose a novel release-and-inhibit control (RIC) approach based on Model Predictive Control theory to efficiently grow DNNs from tiny to backbone. We also design agate-basedfast adaptation mechanism for channel-level knowledge transformation to quickly adapt new-born neurons with existing neurons, enabling safe parameter adaptation and fast convergence for on-device training. Our evaluations show that MDLdroidLite boosts on-device training on various PMS datasets with 28× to 50× less model parameters, 4× to 10× less floating number operations than the state-of-the-art model structures while keeping the same accuracy level.

Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model inference and update. Since data collection is costly in reality, Google’s Federated Learning offers not only complete data privacy but also better model robustness based on data from multiple users. However, personal mobile sensing applications are mostly user-specific and highly affected by environment. As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning. In addition, deploying Federated Learning on a local server, e.g., edge server, may quickly reach the bottleneck due to resource limitation. Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, we propose a ChainSGD-reduce approach which includes a novelchain-directedSynchronous Stochastic Gradient Descent algorithm to effectively reduce overhead among multiple devices. We also design an agent-basedmulti-goalreinforcement learning mechanism to balance resources in a fair and efficient manner. Our evaluations show that our model training on off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device training, and 1.5x faster on average than the existing master-slave approach.

MDLdroid is a novel decentralized mobile deep learning framework, which enables resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, MDLdroid uses a chain-directed Synchronous Stochastic Gradient Descent (ChainSGD-reduce) approach to effectively reduce overhead among multiple devices. In addition, MDLdroid includes an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Real-world experiments demonstrate that our model training on off-the-shelf mobile devices achieves 2× to 3.5× faster than single-device training, and 1.5× faster than the master-slave approach.

Mobile Deep Learning (MDL) has emerged as a privacy-preserving learning paradigm for mobile devices. This paradigm offers unique features such as privacy preservation, continual learning and low-latency inference to the building of personal mobile sensing applications. However, squeezing Deep Learning to mobile devices is extremely challenging due to resource constraint. Traditional Deep Neural Networks (DNNs) are usually over-parametered, hence incurring huge resource overhead for on-device learning. In this paper, we present a novel on-device deep learning framework named MDLdroidLite that transforms traditional DNNs into resource-efficient model structures for on-device learning. To minimize resource overhead, we propose a novel Release-and-Inhibit Control (RIC) approach based on Model Predictive Control theory to efficiently grow DNNs from tiny to backbone. We also design a gate-based fast adaptation mechanism for channel-level knowledge transformation to quickly adapt new-born neurons with existing neurons, enabling safe parameter adaptation and fast convergence for on-device training. Our evaluations show that MDLdroidLite boosts on-device training on various PMS datasets with 28x to 50x less model parameters, 4x to 10x less floating number operations than the state-of-the-art model structures while keeping the same accuracy level.