Selected Publications

In this paper, we introduce FlexPose, a multimodal framework for human pose prediction that adaptively determines when fusion is beneficial and when single-modality processing is preferable. Specifically, we design an adaptive modality selection module that dynamically assesses inter-modal complementarity, allowing the model to revert to single-modality learning when fusion becomes detrimental. To further enhance robustness, we develop additional modules to handle missing modalities and to exploit temporal dependencies across frames. These designs collectively yield substantial accuracy improvements for human pose prediction. When evaluated on the MM-Fi dataset, our approach achieves relative improvements of 22.63% in MPJPE and 16.47% in PA-MPJPE over the baseline. Our code is available in https://github.com/xyt-fe/FlexPose_Modality_selection/tree/master.

The surge in low-altitude economic activities has spurred a significant interest in sensing Unmanned Aerial Vehicles (UAVs). With the widespread deployment of 5G infrastructure and the increasing prominence of integrated sensing and communication, monitoring UAVs via 5G base stations is a natural consideration. However, the rapid Doppler shifts of UAVs and sparse 5G reference signals violate Nyquist sampling requirements. To bridge this gap, we propose LaSen, which merges reference and downlink data signals for sensing. A key challenge stems from the fact that the combination of the periodic reference signals and stochastic data signals constitutes a non-uniform, time-varying measurement matrix. LaSen formulates the tracking of UAVs as a sparse recovery problem, where the target’s kinematics are reconstructed from non-uniform, sub-Nyquist observations. LaSen overcomes the challenges of volatile 5G signal patterns in an iterative way, starting from good measurements as an anchor and progressively refining the suboptimal measurements. Real-world experiments show that LaSen significantly extends the velocity sensing capability, where the measurable speed is up to 20.2 m/s. LaSen can detect drones at a distance of 108 m and can continuously track the distance and velocity of multiple targets, even when the downlink channel is sparsely and dynamically occupied. This work demonstrates the feasibility of high-speed target sensing in next-generation dual-function 5G/6G infrastructures.

Integrated sensing and communication (ISAC) is a key technology in 5G-Advanced (5G-A) and 6G; however, current communication systems typically provide limited bandwidth (<100 MHz), which is unfavorable for sensing applications. To address this challenge, we propose a deep generative architecture for wideband channel state information (CSI) extrapolation: given narrowband CSI, it predicts the unknown wide-band channel state. The key idea is a dual-branch generative architecture, a CNN branch and a transformer branch, where a two-stage, bi-directional cross-attention mechanism tightly couples the CNN’s local features with the Transformer’s global context modeling. Compared with the State-of-the- Art (SOTA) baseline, our model demonstrates significant performance gains across all key metrics, improving SNR by 4.5dB in the most challenging (dense-urban) scenarios. Crucially, when applied to a real-world Unmanned Aerial Vehicle (UAV) channel dataset, the extrapolated wideband CSI reduces the ranging error from 3.81m (20MHz observed CSI) to 0.56m (80MHz extrapolated CSI).

As large language models (LLMs) increasingly integrate into every aspect of our work and daily lives, there are growing concerns about user privacy, which push the trend toward local deployment of these models. There are a number of lightweight LLMs (e.g., Gemini Nano, LLAMA2 7B) that can run locally on smartphones, providing users with greater control over their personal data. As a rapidly emerging application, we are concerned about their performance on commercialoff-the-shelf mobile devices. To fully understand the current landscape of LLM deployment on mobile platforms, we conduct a comprehensive measurement study on mobile devices. While user experience is the primary concern for endusers, developers focus more on the underlying implementations. Therefore, we evaluate both user-centric metrics—such as token throughput, latency, and response quality—and developer-critical factors, including resource utilization, OS strategies, battery consumption, and launch time. We also provide comprehensive comparisons across the mobile system-on-chips (SoCs) from major vendors, highlighting their performance differences in handling LLM workloads, which may help developers identify and address bottlenecks for mobile LLM applications. We hope that this study can provide insights for both the development of on-device LLMs and the design for future mobile system architecture.

In order to better understand the power consumption of each appliance at home, Non-Invasive Load Monitoring (NILM) aims to extract per-appliance power consumption from aggregated whole-home power readings. In recent years, deep learning-based approaches have modeled the problem as a sequence-to-sequence (Seq2Seq) learning task, showing state-of-the-art performance for NILM. However, existing Seq2Seq models perform prediction on short sequences and treat each point in the sequence with equal importance. This is inefficient as only some representative samples in the sequence are more informative than the rest. Furthermore, these short sequences lack global information such as appliances’ ON/OFF moments and duty cycle, leading to sub-optimal model performance. To address this issue, this paper introduces the Sequence-to-Segments-to-Sequence (Seq2Seg2Seq) scheme, which conducts segment-wise feature extraction. Specifically, the input signal is divided into multiple non-overlapping segments, followed by intra-segment feature extraction and inter-segment feature interaction. The Seq2Seg2Seq scheme can handle sequences that are an order of magnitude longer and enables long-time context awareness, with affordable resources on an IoT device.

Thoroughly monitoring the energy consumption of each appliance in a household is essential to assist users in better engaging in power-saving practices. To get per-appliance electrical profile, non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation task, that decomposes the total household electricity consumption into the load profile of each individual appliance. In recent years, deep learning models have demonstrated superior performance in NILM tasks. However, when encountering an unseen household, the model performance degrades seriously. In order to improve the performance on unseen households, we observe that temporal information is beneficial for the model generalization ability, as the ON/OFF patterns of electrical appliances follow a similar time schedule across different households. Thus, in this paper, we propose a time-aware dual-CNN architecture for NILM (TimeNILM), which fully exploits the temporal information and employs feature fusion based on attention mechanism to improve model performance. Experimental results on two datasets (REDD and UK-DALE) demonstrate that our model outperforms state-of-the-art models, achieving 8%-27% mean absolute error (MAE) gain and 8%-35% signal aggregated error (SAEδ) gain for unseen households. As appliance usage patterns are private data, in order to protect user privacy, we extend TimeNILM to federated settings and propose a scheme for aggregation during the learning process. Furthermore, we have successfully deployed TimeNILM on edge devices, and performance evalutation indicates that the model is capable of real-time load monitoring on a Raspberry Pi.

In-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? To answer this question, in this work, we propose, an in-air gesture recognition system leveraging the screen and ambient light …

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present, which not only can give accurate channel occupancy information, but also can precisely estimate the …

Multi-user indoor localization is considered to be one of the most useful wireless applications. Low latency and high robustness to dynamic interference from surrounding people are essential requirements for multi-user localization. However, state-of-the-art (SOTA) indoor localization systems cannot satisfy both requirements at the same time. In this paper, we propose RIScan, a Reconfigurable Intelligent Surface (RIS)-aided localization system that can achieve both low latency and high reliability. We leverage RIS to perform Wi-Fi beam scanning so all clients can figure out their direction in a single scan. However, compared with traditional AP-based systems, the introduction of RIS creates a more complicated signal superposition at the receiver, preventing clients from directly obtaining target beams for direction derivation and localization. To overcome this challenge, we fully utilize the reconfigurability of RIS to …

The market of wearables are growing explosively for the past few years. The majority of the devices are related to health care and fitness. It is embarrassing that users easily lose interest in these devices, and thus fail to improve health condition. Recently, the “be healthy and be rewarded” programs are gaining popularity in health insurance market. The insurance companies give financial rewards to its policyholders who take the initiative to keep healthy. It provides the policyholders with incentives to lead a healthier lifestyle and the insurer can also benefit from less medical claims. Unfortunately, there are hardly any studies discussing how to design the incentive mechanism in this new emerging health promotion program. Improper design would not change policyholders’ unhealthy behavior and the insurer cannot benefit from it. In this paper, we propose a mechanism for this health promotion program. We model it as …

The massive MIMO gain for wireless communication has been greatly hindered by the feedback overhead of channel state information (CSI) growing linearly with the number of antennas. Recent efforts leverage the DNN-based encoder-decoder framework to exploit correlations within the CSI matrix for better CSI compression. However, existing works have not fully exploited the unique features of CSI, resulting in an unsatisfactory performance under high compression ratios and sensitivity to multipath effects. Instead of treating CSI as common 2D matrices like images, we reveal the intrinsic stripe-based correlation across the CSI matrix. Driven by this insight, we propose CSI-StripeFormer, a stripe-aware encoder-decoder framework to exploit the unique stripe feature for better CSI compression. We design a lightweight encoder with asymmetric convolution kernels to capture various shape features. We further …

The physical and physiological development of infants and toddlers requires the proper amount of macronutrient intake, making it an essential problem to estimate the macronutrient in baby food. Nevertheless, existing solutions are either too expensive or poor performing, preventing the widespread use of automatic baby nutrient intake logging. To narrow this gap, this paper proposes a cost-effective and portable baby food macronutrient estimation system, BabyNutri. BabyNutri exploits a novel spectral reconstruction algorithm to reconstruct high-dimensional informative spectra from low-dimensional spectra, which are available from low-cost spectrometers. We propose a denoising autoencoder for the reconstruction process, by which BabyNutri can reconstruct a 160-dimensional spectrum from a 5-dimensional spectrum. Since the high-dimensional spectrum is rich in light absorption features of macronutrients, it …

This paper proposes MousePath, a novel lightweight communication system between PC web pages and smartphones. MousePath works in two modalities, MousePath-OPT and MousePath-BL. MousePath-OPT works by putting the optical mouse on top of the smartphone’s screen, then its transmission starts and instantly finishes without association and pairing fraction. It encodes data into the movement of the smartphone’s display content and leverages the optical mouse of the computer to sense the movement for decoding the data. MousePath-BL works by emulating the smartphone as a Bluetooth wireless mouse. Then the smartphone can directly transmit information to the web page via generating mouse events. We prototype and evaluate the system with commercial computers and smartphones. A key benefit of MousePath is that it seamlessly bridges smartphones to co-located PC web applications …

Wireless home surveillance cameras are gaining popularity in elderly/baby care and burglary detection in an effort to make our life safer than ever before. However, even though the camera’s traffic is encrypted, attackers can still infer what the residents are doing at home. Although this security loophole has been reported, it does not attract much attention from the public, as it requires the attacker to be in close proximity to the camera and have some prior knowledge about the victims. Due to these requirements, the attacker has a low chance of success in the real world. In this article, we argue that the capability of attackers has been greatly underestimated. First, the attacker can leverage the characteristics of video transport protocols to recover the metadata of missing packets. Second, the attacker can build the inference model using the public datasets and adapt the model to the real traffic.

Thanks to the wide bandwidth, large antenna array, and short wavelength, millimeter wave (mmWave) has superior performance in both communication and sensing. Thus, the integration of sensing and communication is a developing trend for the mmWave band. However, the directional transmission characteristics of the mmWave limits the sensing scope to a narrow sector. Existing works coordinate sensing and communication in a time-division manner, which takes advantage of the sector level sweep during the beam training interval for sensing and the data transmission interval for communication. Beam training is a low frequency (e.g., 10Hz) and low duty-cycle event, which makes it hard to track fast movement or perform continuous sensing. Such time-division designs imply that we need to strike a balance between sensing and communication, and it is hard to get the best of both worlds. In this paper, we try to …

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely …

Human activity recognition (HAR) plays an important role in a wide range of applications, such as health monitoring and gaming. Inertial sensors attached to body segments constitute a critical sensing system for HAR. Diverse inertial sensor datasets for HAR have been released with the intention of attracting collective efforts and saving the data collection burden. However, these datasets are heterogeneous in terms of subjects and sensor positions. The coupling of these two factors makes it hard to generalize the model to a new application scenario, where there are unseen subjects and new sensor position combinations. In this paper, we design a framework to combine heterogeneous data to learn a general representation for HAR, so that it can work for new applications. We propose an Augmented Adversarial Learning framework for HAR (AALH) to learn generalizable representations to deal with diverse …

In-air gesture control extends a touch screen and enables contact-less interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? To answer this question, in this work, we propose SMART, an in-air gesture recognition system leveraging the screen and …

To build a secure wireless networking system, it is essential that the cryptographic key is known only to the two (or more) communicating parties. Existing key extraction schemes put the devices into physical proximity and utilize the common inherent randomness between the devices to agree on a secret key, but they often rely on specialized hardware (e.g., the specific wireless NIC model) and have low bit rates. In this article, we seek a key extraction approach that only leverages off-the-shelf mobile devices, while achieving significantly higher key generation efficiency. The core idea of our approach is to exploit the fast varying inaudible acoustic channel as the common random source for key generation and wireless parallel communication for exchanging reconciliation information to improve the key generation rate. We have carefully studied and validated the feasibility of our approach through both theoretical …

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful …

With the arrival of the Internet of Things era, IoT devices and the services built on them make our lives more convenient and also raise public concerns on their vulnerability to attacks. Recent literature advocates physical-layer solutions to help IoT devices detect attacks instead of using sophisticated cryptographic methods. However, there is still no satisfying solutions for IoT devices with a single antenna and sparse traffic. Thus, we introduce SecurePilot to fill this gap. SecurePilot is an unsupervised and plug-and-play solution which works without an attacker’s knowledge in advance. It leverages the strengths of two orthogonal physical-layer information, propagation signatures and device signatures embedded in pilot signals to enable effective attack detection. It could work on single-antenna IoT devices with sparse traffic and also work compatibly with communication protocols. The experimental results show that …

This paper proposes MousePath, a novel lightweight communication system between PC web pages and smartphones. MousePath works by putting the optical mouse on top of the smartphone’s screen, then its transmission starts and is instantly finished without association and pairing friction. It encodes data into the movement of smartphone’s display content and leverages the optical mouse of the computer to sense the movement for decoding the data. We prototype and evaluate the system with commercial computers and smartphones. A key benefit of MousePath is that it can be seamlessly integrated into web pages. Two representative web applications, i.e., sensor sharing and message sharing, have been developed to demonstrate MousePath’s potential in enhancing PC web page functionalities.

Recently, significant efforts have been made to enable WiFi-based gesture recognition. However, models trained with data collected from specific domain suffer from significant performance degradation when applied in a new domain. In practice, various WiFi sensing techniques have provided us with a full knowledge of domain information including discrete variables, i.e., environment and subject, as well as continuous variables, i.e., location and orientation. Previous works haven’t fully explored these domain information or need to integrate substantial links’ information to use them. Intuitively, we can boost gesture recognition accuracy by accounting for all these domain information with different properties. We propose a new framework not being restricted to link number which combines an adversarial learning scheme with feature disentanglement modules. They together conduct two-stage alignment between …

Accurate device motion tracking enables many applications like Virtual Reality (VR) and Augmented Reality (AR). To make these applications available in people’s daily life, low-cost acoustic-based motion tracking methods are proposed. However, existing acoustic-based methods are all based on distance estimation. These methods measure the distance between a speaker and a microphone. With a speaker or microphone array, it can get multiple estimated distances and further achieve multidimensional motion tracking. The weakness of distance-based motion tracking methods is that they need large array size to get accurate results. Some systems even require an array larger than 1 m. This weakness limits the adoption of existing solutions in a single device like a smart speaker. To solve this problem, we propose Acoustic Strength-based Angle Tracking (ASAT) System and further implement a motion tracking …

Miniature radar has demonstrated its great potential in smart homes, such as understanding the wellness of the residents and providing ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication? With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar …

Due to the benefits of small latency, low energy consumption and increased data rate, device-to-device (D2D) communication is recognized as one of the promising techniques in the 5G era. However, the distributed nature of D2D communication makes it non-trivial to generate symmetric keys for the involving parties. Many efforts have been devoted to dynamically generate cryptographic keys for D2D communication in mobile network. However, most of them have limited applicability to practical scenarios due to low key generation efficiency or limited compatibility with commercial mobile devices. In this paper, we design an ambient light based key generation approach, which works on commercial off-the-shelf mobile devices, and achieves high key generation efficiency. We observe that mobile devices (e.g., smartphones, tablets) are often equipped with ambient light sensors and devices sense different light …

With the recent advance of technologies, smart homes are no longer just the subjects of science fiction and are now becoming a reality. it is essential for a smart home to understand its residents and provide entertainment and healthcare services. Researchers have demonstrated the great potential of RF sensing in smart homes, including understanding the residents’ gesture commands and monitoring the residents’ health status unobtrusively. Although RF sensing has appealing properties, it brings congestion to wireless networks. To make the situation worse, there is an unprecedented amount of wireless traffic in smart homes. in this article, we investigate the joint design of sensing and communication systems to alleviate the strain on wireless resources. instead of designing sensing and communication systems as separate objectives, we tend to address them jointly. We discuss this issue from two directions. The …

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.

The design paradigm for backscatter tags is to avoid complex functionality and make tags as simple as possible. However, such a design principle leads to the prevalence of signal collision as tags cannot sense other tags’ ongoing transmissions. The high probability of tag collision will result in low overall throughput. Although there are some existing efforts to resolve tag collisions, they either require good channel conditions or can only resolve a limited number of tags as channel capacity is deficient when SNR is low. In this article, to overcome this limitation, we bring in antenna arrays to boost the channel capacity. We propose FreeScatter, which can support scalable concurrent backscatter transmission using an antenna array. FreeScatter extracts the path that signals traveled and formulates the tags’ channel coefficient using the path representations. FreeScatter further exploits the frequency agnostic property so …

In recent years, smart speakers and robotic vacuum cleaners have played important roles in many people’s daily life. With the development in technology, more and more intelligent devices will become parts of home infrastructure, making life more convenient and comfortable for residents. When different types of specialized intelligent devices are connected and operated over the Internet, how to minimize network latency and guarantee data privacy are open issues. In order to solve these problems, edge computing in smart homes becomes the future trend．In this article, we present our research work along this direction, covering the topics on edge sensing, communication and computation．As for sensing, we focus on the pervasive sensing capability of the edge node and present our work on contactless breath monitoring; as for communication, we work on the joint design of sensing and communication, so that sensing and communication systems can work harmoniously on limited spectrum resources; as for computation, we devote our efforts to personalized machine learning at the edge, building personalized model for each individual while guaranteeing their data privacy．

Visible light positioning (VLP) is a promising direction for indoor localization. VLP depends on Visible light communication (VLC) to receive location anchors sent by light bulbs. In order to decode high-frequency VLC signals, today’s VLP systems require the receiver to equip either rolling shutter cameras or high-frequency light sensors, which bring considerable overhead or are even unavailable on many mobile devices. This paper introduces ALS-P, a lightweight VLP approach which only requires the commercially widely available ambient light sensor (ALS). ALS is conventionally not treated as a feasible VLC receiver as its sampling rate is far less than that of VLC signals. Our basic idea is to leverage the property of frequency aliasing. Through dynamically adjusting the sampling rate of the ALS sensor, the down-converted signals can be uniquely distinguished. To realize this idea, we propose novel designs to …

The vision of battery-free communication has made backscatter a compelling technology for on-body wearable and implantable devices. Recent advances have facilitated the communication between backscatter tags and on-body smart devices. These studies have focused on the communication dimension, while the security dimension remains vulnerable. It has been demonstrated that wireless connectivity can be exploited to send unauthorized commands or fake messages that result in device malfunctioning. The key challenge in defending these attacks stems from the minimalist design in backscatter. Thus, in this paper, we explore the feasibility of authenticating an on-body backscatter tag without modifying its signal or protocol. We present SecureScatter, a physical-layer solution that delegates the security of backscatter to an on-body smart device. To this end, we profile the on-body propagation paths of …

Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the users as long as being functioning. In this article, we propose a battery-free sensing platform for wearable devices in the form factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing, and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter …

Mobile devices, including laptops, smartphones, wearables, etc., have become essential tools in modern life. We rely on them for social activities, document processing, and health status monitoring. As these devices contain sensitive personal information, various security mechanisms, such as firewalls, traffic monitors, and information flow control systems [1], have been developed for mobile devices to prevent unauthorized data leakage.

Mobile sensing enables unobtrusive monitoring of our daily activities, sleep quality, breathing and heart rate, revolutionizing the health-care system. Dietary information is also a critical dimension for health management but has no convenient solution yet. In this paper, we ask whether we can track meal composition unobtrusively. We introduce Smart-U, a new utensil design that can recognize meal composition during the intake process, without user intervention or on-body instruments. Smart-U makes use of the fact that light spectra reflected by foods are dependent on the food ingredients. By analyzing the reflected light spectra, Smart-U can recognize what food is on top of the utensil. We describe the prototype design of Smart-U and the food recognition algorithm. We demonstrate that Smart-U can recognize 20 types of foods with 93 % accuracy. It can work robustly under different conditions. We envision that …

Today’s mobile devices contain sensitive data, which raises concerns about data security. This paper discusses a covert channel threat on existing mobile systems. Through it, malware can wirelessly leak information without making network connections or emitting signals, such as sound, EMR, vibration, etc., that we can feel or are aware of. The covert channel is built on a communication method that we call NICScatter. NICScatter transmitter malware forces mobile devices, such as mobile phones, tablets or laptops, to reflect surrounding RF signals to covertly convey information. The operation is achieved by controlling the impedance of a device’s wireless network interface card (NIC). Importantly, the operation requires no special privileges on current mobile OSs, which allows the malware to stealthily pass sensitive data to an attacker’s nearby mobile device, which can then decode the signal and thus effectively …

Dietary monitoring can provide valuable information for disease diagnosis, body weight control, and dietary habit management, and thus it is welcomed by patients, dieters, and nutritionists. While various techniques have been used for dietary monitoring in clinical trials and user studies, they are not ready for daily use. Existing solutions either require tedious manual recording or may impede normal daily activities. In this paper, a pair of diet-aware glasses is designed. The key idea here is that when people wear glasses, the temples of the glasses are in touch with the lower part of the temporalis muscle, one of the mastication muscles. By integrating an electromyography (EMG) sensor into glasses, the glasses can measure the muscle activity of the temporalis to detect intake-related events. This paper instantiates the idea by building a prototype equipped with an EMG sensor, a microcontroller, SD shield/card and a …

Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the people as long as being functioning. In this study, we propose a battery-free sensing platform for wearable devices in the form-factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter …

Auctions have been shown to be able to tackle the problem of spectrum scarcity effectively, but most of existing works only focus on static scenarios. They cannot deal with the requests of spectrum users as they arrive and leave dynamically. Bidders can either cheat by bidding untruthfully or cheat about the arrival and departure time. In this paper, we model the radio spectrum allocation problem as a sealed‐bid online combinatorial auction and propose a truthful mechanism called TRADE. TRADE is a truthful and an individual rational mechanism with polynomial time complexity. It can prevent bidders from cheating in the auction while achieving good bidder satisfaction, spectrum utilization, and social welfare. Copyright © 2013 John Wiley & Sons, Ltd.

Auctions are among the best-known market-based tools to solve the problem of dynamic spectrum redistribution. In recent years, a good number of strategy-proof auction mechanisms have been proposed to improve spectrum utilization and to prevent market manipulation. However, the issue of privacy preservation in spectrum auctions remains open. On the one hand, truthful bidding reveals bidders’ private valuations of the spectrum. On the other hand, coverage/interference areas of the bidders may be revealed to determine conflicts. In this paper, we present PISA, which is a PrIvacy preserving and Strategy-proof Auction mechanism for spectrum allocation. PISA provides protection for both bid privacy and coverage/interference area privacy leveraging a privacy-preserving integer comparison protocol, which is well applicable in other contexts. We not only theoretically prove the privacy-preserving properties of …

The problem of dynamic spectrum redistribution has been extensively studied in recent years. Auctions are believed to be among the most effective tools to solve this problem. A great number of strategy-proof auction mechanisms have been proposed to improve spectrum allocation efficiency by stimulating bidders to truthfully reveal their valuations of spectrum, which are the private information of bidders. However, none of these approaches protects bidders’ privacy. In this paper, we present PRIDE, which is a PRIvacy-preserving anD stratEgy-proof spectrum auction mechanism. PRIDE guarantees k-anonymity for both single- and multiple-channel auctions. Furthermore, we enhance PRIDE to provide l-diversity, which is an even stronger privacy protection than k-anonymity. We not only rigorously prove the economic and privacy-preserving properties of PRIDE, but also extensively evaluate its performance. Our …

The problem of dynamic spectrum redistribution has been extensively studied in recent years. Auction is believed to be one of the most effective tools to solve this problem. A great number of strategy-proof auction mechanisms have been proposed to improve spectrum allocation efficiency by stimulating bidders to truthfully reveal their valuations of spectrum, which are the private information of bidders. However, none of these approaches protects bidders’ privacy. In this paper, we present SPRING, which is the first Strategy-proof and PRivacy preservING spectrum auction mechanism. We not only rigorously prove the properties of SPRING, but also extensively evaluate its performance. Our evaluation results show that SPRING achieves good spectrum redistribution efficiency with low overhead.