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.

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.

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Further, we extend our system to MeatSpec-G to improve its generalizability to varied adulteration conditions and unknown adulterants. To achieve this, we first propose a feature alignment-based training scheme to reduce the feature gap among samples of diverse concentrations and admixture patterns. Then, we propose a cascaded open-set recognition framework that decouples uncertainty quantification and anomaly feature discrimination, to address the limitations of softmax confidence in detecting distribution shifts and reconstruction artifacts. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point. Moreover, our system can generalize to achieve an 88.89% detection accuracy in unknown adulteration conditions with a 27.78% improvement, and an 83.33% detection accuracy for unknown adulterants.

Monitoring sashimi freshness,i.e., histamine levels, in showcases poses a critical challenge for sushi restaurants and fresh food stores. Current histamine monitoring methods involve labor-intensive chemical experiments or expensive devices, making affordable on-site monitoring difficult. This paper proposes FreshSpec, a low-cost and automatic spectral imaging system capable of precisely monitoring histamine levels in sashimi with minimal human intervention. The low concentration of histamine, combined with the potential for other ingredients to mask its spectral characteristics, complicates precise histamine level predictions using coarse or redundant spectral data from low-cost devices. To address this issue, FreshSpec employs an innovative feature- wise spectral reconstruction (SR) framework that effectively eliminates irrelevant and redundant data while preserving critical histamine-related spectral features. Specifically, we redefine the SR reconstruction target by utilizing features derived from the encoder of the spectral foundation model that is enhanced to focus on histamine-related spectral features. Furthermore, inspired by the monotonic accumulation properties of histamine over time, we propose a histamine regression model with unsupervised continual adaptation to new sashimi samples during practical deployment. Experimental results from 240 samples of salmon, tuna, and snapper demonstrate that FreshSpec achieves an R2 of 0.9319 and an RMSE of 3.101 mg/100 g, comparable to laboratory spectral imaging systems, while outperforming baseline schemes with a 46.95% RMSE reduction and a 0.1631 R2 improvement.

Graph-structured data applications promote the development of Graph Neural Networks (GNN) in recent years. Due to privacy concerns, collecting graph data stored in massive client devices for centralized graph learning is prohibitive. It is natural to integrate federated learning (FL) in graph learning to address this issue, which enables clients to collaborate on training a shared model without uploading their data. This generates an emerging paradigm of federated graph learning (FGL). However, due to various costs incurred by FGL training, the collaboration between the server and clients is still a challenging issue in FGL, which remains largely unexplored in existing studies. To bridge this gap, we propose a cost-efficient collaboration framework for FGL of graph convolutional networks on semi-supervised node classification tasks, i.e., Joint Client and Cross-Client Edge Selection (JC3ES) for the server. Specifically, we first characterize how varies graph structure affect the final convergence performance of the FGL model. We then reveal the fundamental supermodular property in client selection. Based on this, we further devise an approximately optimal algorithm for the server and theoretically derive the performance gap between the proposed algorithm and the optimal solution. Extensive numerical evaluations show that our proposed algorithm achieves outstanding performance in cost-efficient collaboration for FGL on popular graph datasets.

With edge intelligence, AI models are increasingly push to the edge to serve ubiquitous users. However, due to the drift of model, data, and task, AI model deployed at the edge suffers from degraded accuracy in the inference serving phase. Model retraining handles such drifts by periodically retraining the model with newly arrived data. When colocating model retraining and model inference serving for the same model on resource-limited edge servers, a fundamental challenge arises in balancing the resource allocation for model retraining and inference, aiming to maximize long-term inference accuracy. This problem is particularly difficult due to the underlying mathematical formulation being time-coupled, non-convex, and NP-hard. To address these challenges, we introduce a lightweight and explainable online approximation algorithm, named ORRIC, designed to optimize resource allocation for adaptively balancing the accuracy of model training and inference. The competitive ratio of ORRIC outperforms that of the traditional Inference-Only paradigm, especially when data drift persists for a sufficiently lengthy time. This highlights the advantages and applicable scenarios of colocating model retraining and inference. Notably, ORRIC can be translated into several heuristic algorithms for different resource environments. Experiments conducted in real scenarios validate the effectiveness of ORRIC.

Accurate channel estimation is crucial for the performance gains of massive multiple-input multiple-output (mMIMO) technologies. However, it is bandwidth-unfriendly to estimate large channel matrix frequently to combat the time-varying wireless channel. Deep learning-based channel prediction has emerged to exploit the temporal relationships between historical and future channels to address the bandwidth-accuracy trade-off. Existing methods with convolutional or recurrent neural networks suffer from their intrinsic limitations, including restricted receptive fields and propagation errors. Therefore, we propose a Transformer-based model, CS3T-UNet tailored for mMIMO channel prediction. Specifically, we combine the cross-shaped spatial attention with a group-wise temporal attention scheme to capture the dependencies across spatial and temporal domains, respectively, and introduce the shortcut paths to well-aggregate multi-resolution representations. Thus, CS3T-UNet can globally capture the complex spatial-temporal relationship and predict multiple steps in parallel, which can meet the requirement of channel coherence time. Extensive experiments demonstrate that the prediction performance of CS3T-UNet surpasses the best baseline by at most 6.86 dB with a smaller computation cost on two channel conditions.

Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes.

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 sensor (ALS), which are ordinary modalities on mobile devices. For the transmitter side, we design a screen display mechanism to embed spatial information and preserve the viewing experience; for the receiver side, we develop a framework to recognize gestures from low-quality ALS readings. We implement and evaluate on both a tablet and several smartphones. Results show that can recognize$9$types of frequently used in-air gestures with an average accuracy of$96.1\%$.

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 signal power and bandwidth. can also detect fleeting radar signals. We implement and perform extensive evaluations with both controlled experiments and field tests. Results show that can sense up to$900$MHz bandwidth with frequency range from 0.5GHz to 7GHz, and the power estimation error is less than$3$dB. can also accurately detect busy 5G channels and can classify the encrypted traffic from the channel usage patterns. We believe that provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

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 incorporate novel designs tailored for stripe features, including a novel hierarchical Transformer backbone in the decoder and a hybrid attention mechanism to extract and fuse correlations in angular and delay domains. Our evaluation results show that our system achieves an over 7dB channel reconstruction gain under a high compression ratio of 64 in multipath-rich scenarios, significantly superior to current state-of-the-art approaches. This gain can be further improved to 17dB given the extended embedded dimension of our backbone.

Network performance monitoring and troubleshooting is a crucial but challenging task in datacenter management. Despite the numerous solutions that have been proposed in recent years, their efforts are often hindered by high costs and unreliable fault localization, making it difficult to deploy them in real-world environments. In this paper, we present LMon, a highly reliable and efficient system for monitoring and troubleshooting in datacenter networks. LMon utilizes the characteristic of ECMP hashing linearity to control the packet routing without any modification of the underlying protocols. Additionally, LMon leverages a lightweight probing technique to reduce monitoring overhead. Furthermore, the system integrates improved LASSO regression and statistical hypothesis testing for higher accuracy and faster processing in link failure localization. The effectiveness of LMon is demonstrated through its implementation and evaluation in ns-3 simulation. The results validate the reliability and efficiency of the system, making it a promising option for ensuring long-term network maintenance in datacenters.

Deep learning has achieved tremendous success in various fields, but its significant computational demands make inference on mobile devices extremely challenging. To address this issue, we propose the COUPLE system, which enables heterogeneous processors to collaborate on mobile devices for accelerating video analytics. Additionally, we design the Co-Optimize strategy which utilizes the inference results of GPU to mitigate the accuracy loss caused by DSP. Experimental results demonstrate that COUPLE can improve the inference Average Precision by up to 5% compared to existing solutions.

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 endow target beams with distinguishing features, so that RIScan can extract stable and accurate direction information from complex and dynamic environments. RIScan is implemented in the real system with our own developed 16 × 16 RIS prototype and COTS Wi-Fi devices. Extensive experiments show that RIScan achieves a median localization error of 47cm and 71cm in static and dynamic environments with only two RIS anchors. Compared to the SOTA methods, RIScan reduces the localization latency by more than an order of magnitude.

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. Thus, the attacker can launch the attack at a distance from the camera, without prior knowledge about the victim. We also implement this attack scenario and verify that the attacker can infer the victims’ activities at a distance as large as 40 m without any knowledge about the victim, neither personal nor environmental.

Characterizing the end-to-end network available bandwidth (ABW) is an important but challenging task. Although a number of ABW estimation tools have been introduced over the past two decades, applying them to the real-world networks is still difficult because of the biased results, heavy load, and long measurement time. In this paper, we propose a novel Burst Queue Recovery (BQR) model to infer the ABW. BQR first induces an instant network congestion and then observes the one-way delay (OWD) variation until the tight link recovers from the congestion. By correlating the OWDs with the queue length variation, BQR can calculate the ABW accurately. Compared to the traditional probe gap model (PGM) and probe rate model (PRM), our theoretical analysis and simulations show that BQR is more tolerant to the transient traffic burst and supports the scenarios with multiple congestible links. Based on the model, we build FABMon, a fast and accurate ABW estimation tool. Our experiments show that FABMon can measure ABW within 50 milliseconds, and achieve much more accurate measurement results than the existing tools with a very small volume of probe packets.

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 estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

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 demo, we present how to transform the low-cost Ultrawideband (UWB) devices into a spectrum sensor and showcase WISE, a low-cost wideband spectrum sensing system, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. Our demo will show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and fleeting radar signals. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

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 SecurePilot can successfully detect 99.6% of attacks, triggering false alarms on 3.1% of legitimate traffic in a typical office environment.

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, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3.4kbps data rate while it preserves the sensing resolution of the radar.

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 attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic.

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 analysis and a variety of measurements. We implement our approach on different mobile devices and conduct extensive experiments in different real scenarios. The experiment results show that our approach achieves high efficiency and satisfactory robustness. Compared with state-of-the-art methods, our approach improves the key generation rate by 38.46% and reduces the bit mismatch ratio by 42.34%.

Network measurement provides operators an efficient tool for many network management tasks such as performance diagnosis, traffic engineering and intrusion prevention. However, with the rapid and continuous growth of traffic speed, it needs more computing and memory resources to monitor traffic in per-flow or per-packet granularity. Sample-based measurement systems (e.g., NetFlow, sFlow) have been developed to perform coarse-grained measurement, but they may miss part of records, especially for mice flows, which are important for some network management tasks (e.g., anomaly detection, performance diagnosis). To address these issues, data streaming algorithms such as hash tables and sketches have been introduced to balance the trade-off among accuracy, speed, and memory usage. In this article, we present a systematic survey of various data structures, algorithms and systems which have been proposed in recent years to perform fine-grained measurement for high-speed networks. We organize these methods and systems from a software-defined perspective. In particular, we abstract fine-grained network measurement into three-layer architecture. We introduce the responsibility of each layer and categorize existing state-of-the-art works into this architecture. Finally, we conclude the article and discuss the future directions of fine-grained network measurement.

In industrialized wine production, brewing and aging are two key steps. These two processes require the liquors to be bottled for a long time, sometimes more than ten years. The liquor is vulnerable and highly susceptible to microbial contamination during storage, causing undetectable deterioration. During the production process, wineries control the indoor temperature and carbon dioxide concentration to slow down other microorganisms' reproduction speed. These methods, however, do not prevent pathogenic microorganism growth. Currently, microbial culture methods are not suitable for real-time liquor quality monitoring in wineries. Therefore, we have designed a lightweight monitoring system called Lili, which uses light signals to monitor real-time liquor quality changes. Lili detects the changes in surface tension and absorption spectrum caused by microbial metabolites and growth during deterioration. Lili employs eight LEDs and one photodiode to achieve fine-grained surface tension and absorption spectrum measurements. By analyzing these changes, Lili realizes real-time quality monitoring. In this paper, the characteristic offset degree measurement and the absorption spectrum dimension expansion are two critical technologies. In addition, we implemented countermeasures against ambient light noise and sloshing interference. Lili's surface tension and absorption spectrum measurement errors are only 0.89 mN/m and 2.4%, respectively, making it useful to identify the contamination duration, microorganism content and microorganism composition. These two data points can be used to determine potential issues with liquor quality when the liquor becomes health-threatening or even just contaminated, with an accuracy of 97.5%.

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?

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?

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 each of the source domains and the target domain to eliminate all gesture irrespective information. We also present an attention scheme based on discriminative information of each source and target domain to promote positive transfer from source to target domain. Our model is evaluated on the Widar 3.0 data set and achieves an improvement of 3%-12.7% in cross-domain average accuracy, demonstrating the superiority.

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 intensities at different angles towards the light source. We conduct a set of measurement study, and the results show that the light sensor data have several nice properties, i.e., space-varying, sensitive to the angle of measuring device, and time varying. These properties demonstrate that the ambient light is a good medium for secret key generation. We implement a prototype on different mobile devices, and conduct extensive experiments to evaluate its performance. The experiment results show that compared with the state-of-the-art key generation methods, our approach has a superior performance in key generation efficiency and robustness.

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 that it can support more spatial streams than the number of antennas. The experimental results show that we can enable up to 20 tags transmitting concurrently. With the ubiquitous connectivity of battery-free tags in the near future, FreeScatter can significantly boost the network throughput.

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 address challenges which stem from ALS hardware, high-order light aliasing, and environmental interference cancellation. Besides, ALS can also enable a lightweight VLC via changing LED frequencies. We implement the ALS-P on commercial LED bulbs and the ALS of existing smartphones. The evaluation shows that the ALSP can enable robust and efficient LED frequency decoding with at least 4LEDs bulbs in practical scenarios. As a result, ALS-P can enable VLC at a data rate of 5bit/s per each LED bulb and achieve a sub-meter level indoor localization accuracy.

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 Smart-U can enable automatic food intake tracking, and provide personalized food suggestions based on nutrient recommendations and prior consumption. In the long run, Smart-U can contribute to the study of chronic diseases.

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 communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits, and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds’ walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.

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 gather the guarded data. Our experiments with different mobile devices show that the covert channel can achieve 1.6 bps and transmit as far as 2 meters. In a through-the-wall scenario, it can transmit up to 70 cm.

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 Bluetooth radio. When working together with a smartphone, the glasses can provide detailed information on intake schedule, the number of chewing cycles and broad food category. Extensive experiments are conducted on seven subjects and the results show appealing prospects for our diet-aware glasses. The prototype achieves 96% accuracy for counting the number of chewing cycles and up to 90.8% accuracy for classifying five types of food.

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 communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds' walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.

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 PISA, but also extensively evaluate its performance. Evaluation results show that PISA achieves good spectrum allocation efficiency with light computation and communication overheads.

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 evaluation results show that PRIDE achieves good spectrum redistribution efficiency and fairness with low overhead.

Abstract 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.

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.