Learning representative embeddings for different types of speaking styles, such as emotion, age, and gender, is critical for both recognition tasks (e.g., cognitive computing and human-computer interaction) and generative tasks (e.g., style-controllable speech generation). In this work, we introduce ParaMETA, a unified and flexible framework for learning and controlling speaking styles directly from speech. Unlike existing methods that rely on single-task models or cross-modal alignment, ParaMETA learns disentangled, task-specific embeddings by projecting speech into dedicated subspaces for each style type. This design reduces inter-task interference, mitigates negative transfer, and allows a single model to handle multiple paralinguistic tasks such as emotion, gender, age, and nationality classification. Beyond recognition, ParaMETA enables fine-grained style control in Text-To-Speech (TTS) generative models. It supports both speech- and text-based prompting and allows users to modify one speaking style while preserving others. Extensive experiments demonstrate that ParaMETA outperforms strong baselines in classification accuracy and generates more natural and expressive speech, while maintaining a lightweight and efficient model suitable for real-world applications.

This paper presents CARTS, an adaptive 5G uplink sensing scheduling scheme designed to provide Integrated Communication and Localization services. The performance of both communication and localization fundamentally depends on the availability of accurate and up-to-date channel state information (CSI). In modern 5G networks, uplink CSI is derived from two reference signals: the demodulation reference signal (DMRS) and the sounding reference signal (SRS). However, current base station implementations treat these CSI measurements as separate information streams. The key innovation of CARTS is to fuse these two CSI streams to increase the frequency of CSI updates and to extend sensing opportunities to more users. CARTS addresses two key challenges: (i) a novel channel stitching and compensation method that integrates asynchronous CSI estimates from DMRS and SRS, despite their different time and frequency allocations, and (ii) a real-time SRS triggering algorithm that complements the inherently uncontrollable DMRS schedule, ensuring sufficient and non-redundant sensing opportunities for all users. Our trace-driven evaluation shows that CARTS significantly improves scalability, achieving a channel estimation error (NMSE) of 0.167 and UE tracking accuracy of 85 cm while supporting twice the number of users as a periodic SRS-only baseline with similar performance. By opportunistically combining DMRS and SRS, CARTS therefore provides a practical, standard-compliant solution to improve CSI availability for localization and communication without requiring additional radio resources.

Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). Existing approaches predominantly rely on implicit conditional diffusion models, which compress user behaviors into a single representation during the forward diffusion process. While effective to some extent, this oversimplification often leads to the loss of sequential and contextual information, which is critical for understanding user behavior. Moreover, explicit information, such as user-item interactions or sequential patterns, remains underutilized, despite its potential to directly guide the recommendation process and improve precision. However, combining implicit and explicit information is non-trivial, as it requires dynamically integrating these complementary signals while avoiding noise and irrelevant patterns within user behaviors. To address these challenges, we propose Dual Conditional Diffusion Models for Sequential Recommendation (DCRec), which effectively integrates implicit and explicit information by embedding dual conditions into both the forward and reverse diffusion processes. This allows the model to retain valuable sequential and contextual information while leveraging explicit user-item interactions to guide the recommendation process. Specifically, we introduce the Dual Conditional Diffusion Transformer (DCDT), which dynamically integrate both implicit and explicit signals throughout the diffusion stages, ensuring contextual understanding and minimizing the influence of irrelevant patterns. Extensive experiments on public benchmark datasets demonstrate that DCRec significantly outperforms state-of-the-art methods.

The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposesN LoS, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag.N LoSleverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the target's position.N LoSintroduces several key innovations. First, we designHFD(Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we proposeFS-MUSIC(Frequency-Spatial Multiple Signal Classification), a super-resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluateN LoSusing a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate thatN LoSachieves median localization errors of10.69 cm (X)and11.98 cm (Y)at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.

Figure 1: We review and categorize VMIs aimed at enhancing context awareness.Our key contribution is a Macro-Micro-Macro level (whole-detail-whole) system design framework, providing actionable references from a Data Modality-Driven perspective: (1) Macro-level contextual factors: considerations for context understanding (Section 3); (2) Micro-level system foundations: input data modality (visual + other modalities), data integration stages, multimodal data processing and evaluation strategies (Sections 4, 5); (3) Macro-level design synthesis: application domains, design considerations and key challenges (Sections 6, 7).

Controlling speaking style in text-to-speech (TTS) systems has become a growing focus in both academia and industry. While many existing approaches rely on reference audio to guide style generation, such methods are often impractical due to privacy concerns and limited accessibility. More recently, large language models (LLMs) have been used to control speaking style through natural language prompts; however, their high computational cost, lack of interpretability, and sensitivity to prompt phrasing limit their applicability in real-time and resource-constrained environments. In this work, we propose ParaStyleTTS, a lightweight and interpretable TTS framework that enables expressive style control from text prompts alone. ParaStyleTTS features a novel two-level style adaptation architecture that separates prosodic and paralinguistic speech style modeling. It allows fine-grained and robust control over factors such as emotion, gender, and age. Unlike LLM-based methods, ParaStyleTTS maintains consistent style realization across varied prompt formulations and is well-suited for real-world applications, including on-device and low-resource deployment. Experimental results show that ParaStyleTTS generates high-quality speech with performance comparable to state-of-the-art LLM-based systems while being 30x faster, using 8x fewer parameters, and requiring 2.5x less CUDA memory. Moreover, ParaStyleTTS exhibits superior robustness and controllability over paralinguistic speaking styles, providing a practical and efficient solution for style-controllable text-to-speech generation. Demo can be found at https://parastyletts.github.io/ParaStyleTTS_Demo/. Code can be found at https://github.com/haoweilou/ParaStyleTTS.

Human subjective evaluation plays a crucial role in evaluating speech-related generative tasks such as text-to-speech (TTS) generation. However, current practices are often constrained by limited scalability, fragmented workflows, and inconsistent rating reliability. Researchers frequently rely on manual methods or general-purpose crowdsourcing systems, where recruiting appropriately skilled listeners is challenging, and result analysis is labor-intensive. In this work, we introduce Achoio, a dedicated end-to-end online system designed to streamline and scale human evaluation for the TTS research community. Achoio allows researchers to create and manage evaluation projects, upload synthesized speech samples, and automatically match them with qualified listeners based on linguistic proficiency and domain knowledge. The system provides built-in tools for project status tracking, result aggregation and visualization. In this demonstration, we will walk through the core features of Achoio, including intuitive project setup, skill-based listener matching algorithm, and automated analytics. By addressing the limitations of existing workflows, Achoio offers a scalable, domain-aware, and analysis-ready solution for conducting high-quality subjective TTS evaluations. Our system is live and can be found at https://www.achoio.com. Demo is available on YouTube at https://youtu.be/Ugjj3_YooSM.

Text-To-Speech (TTS) generation plays a crucial role in human-robot interaction by allowing robots to communicate naturally with humans. Researchers have developed various TTS models to enhance speech generation. More recently, diffusion models have emerged as a powerful generative framework, achieving state-of-the-art performance in tasks such as image and video generation. However, their application in TTS has been limited by its slow inference speeds due to their iterative denoising process. Previous work has applied diffusion models to Mel-Spectrograms with an additional vocoder to convert them into waveforms. To address these limitations, we propose LatentSpeech, a novel diffusion-based TTS framework that operates directly in a latent space. This space is significantly more compact and information-rich than raw Mel-Spectrograms. Furthermore, we introduce an alternative latent space of Pseudo-Quadrature Mirror Filters (PQMF), which decomposes speech into multiple subbands. By leveraging PQMF’s near-perfect waveform reconstruction capability, LatentSpeech eliminates the need for a separate vocoder and reduces both model size and inference time. Our PQMF-based LatentSpeech model reduces inference time by 45% and model size by 77% compared to Mel-Spectrogram diffusion models. On benchmark datasets, it achieves 25% lower WER and 58% higher MOS using the same training data. These results highlight LatentSpeech as an efficient, high-quality TTS solution for real-time and human-robot interaction. Code and models are available here.

We propose LightLLM, a model that fine tunes pre-trained large language models (LLMs) for light-based sensing tasks. It integrates a sensor data encoder to extract key features, a contextual prompt to provide environmental information, and a fusion layer to combine these inputs into a unified representation. This combined input is then processed by the pre-trained LLM, which remains frozen while being fine-tuned through the addition of lightweight, trainable components, allowing the model to adapt to new tasks without altering its original parameters. This approach enables flexible adaptation of LLM to specialized light sensing tasks with minimal computational overhead and retraining effort. We have implemented LightLLM for three light sensing tasks: light-based localization, outdoor solar forecasting, and indoor solar estimation. Using real-world experimental datasets, we demonstrate that LightLLM significantly outperforms state-of-the-art methods, achieving 4.4x improvement in localization accuracy and 3.4x improvement in indoor solar estimation when tested in previously unseen environments. We further demonstrate that LightLLM outperforms ChatGPT-4 with direct prompting, highlighting the advantages of LightLLM's specialized architecture for sensor data fusion with textual prompts.

This poster presents CARTS, an adaptive 5G uplink sensing scheme that jointly uses the estimated CSI from both data channel reference signal (DMRS) and channel sounding reference signal (SRS) to improve the UE sensing capacity with minimal degradation in communication performance. In order to efficiently combine the estimated CSIs from these two reference signals, CARTS features a real-time SRS triggering algorithm to complement the channel estimations from the DMRS. Besides, to address asynchornization issues caused by DMRS and SRS, which are sampled at different time and frequency bands, CARTS applies a new channel stitching and compensation method.

Intelligent Reflective Surfaces (IRS) are an emerging research focus aimed at enhancing non-line-of-sight wireless communications by manipulating radio reflections. However, when embedded within objects, IRS may disrupt mmWave radar object classification by altering reflected features. In this study, we explore the adverse effects of a misconfigured IRS on radar classification. We prototyped an IRS with configurations that can either induce destructive interference with the object's reflected signals or deflect these reflections away from the radar using beamforming techniques. Experiments using a 24 GHz radar to detect four everyday objects revealed a significant drop in classification accuracy due to this interference. These findings underscore a significant vulnerability in the increasingly pervasive deployment of mmWave radar for object classification, highlighting the urgent need for robust countermeasures.

We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX using off-the-shelf mmWave radars and collected a total of 7,200 hand hygiene gesture data from 10 subjects performing a 6-step hand-rubbing procedure, as recommended by the World Health Organization, using sanitizer, at 1.5m---over 5 times longer than in prior works. BMX outperforms state-of-the-art approaches by 31--43% and achieves 91% accuracy at boresight by combining only two beams, demonstrating superior gesture classification in low SNR scenarios. BMX maintained its effectiveness even when the subject was positioned 30° away from the boresight, exhibiting a modest 5% drop in accuracy.

Plant sensing plays an important role in modern smart agriculture and the farming industry. Remote radio sensing allows for monitoring essential indicators of plant health, such as leaf water content (WC). While recent studies have shown the potential of using millimeter-wave (mmWave) radar for plant sensing, many overlook crucial factors, such as leaf structure and surface roughness, which can impact the accuracy of the measurements. In this article, we introduce Leafeon, which leverages mmWave radar to measure leaf WC noninvasively. Utilizing electronic beam steering, multiple leaf perspectives are sent to a custom deep neural network, which discerns unique reflection patterns from subtle antenna variations, ensuring accurate and robust leaf WC estimations. We implement a prototype of Leafeon using a Commercial Off-The-Shelf mmWave radar and evaluate its performance with a variety of different leaf types. Leafeon was trained in-lab using high-resolution destructive leaf measurements, achieving a mean absolute error (MAE) of leaf WC as low as 3.17% for the Avocado leaf, significantly outperforming the state-of-the-art approaches with an MAE reduction of up to 55.7%. Furthermore, we conducted experiments on live plants in both indoor and glasshouse experimental farm environments. Our results showed a strong correlation between predicted leaf WC levels and drought events.

We introduce LiDARSpectra, a novel approach utilizing mobile-integrated commodity Light Detection and Ranging (LiDAR) signals for synthetic indoor light spectral mapping. Our method incorporates an innovative material estimation algorithm into the LiDAR signal processing pipeline, accurately simulating reflected wavelengths from indoor surfaces. Utilizing low-resolution LiDAR scans enriched with material information, it eliminates the need for deploying dedicated spectral sensors, greatly simplifying the spectral mapping process. We validate our synthetic spectral maps against real sensor data and demonstrate their utility in applications such as indoor localization and solar energy provisioning. This presents an efficient solution for indoor spectral mapping with wide-ranging potential across fields like lighting design, indoor planting, environmental monitoring, and location-based services.

The accuracy of the current localization methods degrades significantly when the direct path between the wireless transmitter and the target is blocked. This paper considers the problem of using a single mmWave radar and a tag to facilitate localization in the non-penetrable non-line-of-sight (NLoS) scenario. We present mN2LoS (short for mmWave based Non-penetrable NLoS LOCalization), which accurately localizes the tag by using the multipath reflections. mN2LoS has a few novel features. First, we design HTRD for detecting reflectors and surroundings while distinguishing them from the tag, using Hybrid utilization of Tag localization code and Reflector localization code based on Direct sequence spread spectrum techniques. Second, we enhance the signal-to-noise ratio by exploiting the correlation features of the designed signal. Evaluation results demonstrate that the developed mN2LoS can achieve median errors (at 5m range) of 12.9cm and 3.8° for distance and AOA estimations for the office configuration, respectively.

Passive visible light communication (VLC) modulates light propagation or reflection to transmit data without directly modulating the light source. Thus, passive VLC provides an alternative to conventional VLC, enabling communication where the light source cannot be directly controlled. There have been ongoing efforts to explore new methods and devices for modulating light propagation or reflection. The state-of-the-art has broken the 100 kbps data rate barrier for passive VLC by using a digital micro-mirror device (DMD) as the light modulating platform, or transmitter, and a photo-diode as the receiver. We significantly extend this work by proposing a massive spatial data channel framework for DMDs, where individual channels can be decoded in parallel using an event camera at the receiver. For the event camera, we introduce event processing algorithms to detect numerous channels and decode bits from individual channels with high reliability. Our prototype, built with off-the-shelf event cameras and DMDs, can decode up to ~2,000 parallel channels, achieving a data transmission rate of 1.6 Mbps, markedly surpassing current benchmarks by 16x.

Non-line-of-sight (NLoS) sensing represents a significant advancement in sensor technology. Unlike traditional sensing methods that rely on direct line-of-sight, NLoS sensing allows for the detection and localization of objects obscured from the sensor's view. In this paper, we introduce mmMirror, a novel Van Atta Array based millimetre-wave (mmWave) reconfigurable intelligent reflecting surface (IRS) that provides: (i) NLoS localization at a range of approximately 3 meters, (ii) seamless communication between radar and IRS using existing frequency-modulated continuous-wave (FMCW) signals, and (iii) support for multiple targets. The mmMirror system is implemented on commodity 24 GHz radars, and the IRS is prototyped on printed circuit boards (PCBs).

Recent years have seen the explosion of edge intelligence with powerful Deep Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud servers and subsequently porting them to mobile devices after being lightweight. Conventional approaches manually specialized DNNs for various edge platforms and retrain them with real-world data. However, as the number of platforms increases, these approaches become labour-intensive and computationally prohibitive. Additionally, real-world data tends to be sparse-label, further increasing the difficulty of lightweight models. In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices. Specifically, we simultaneously optimise a large network family using both labelled and unlabelled data and then automatically search for tailored networks for different hardware platforms. MatchNAS acts as an intermediary that bridges the gap between cloud-based DNNs and edge-based DNNs.

Recent research has demonstrated the huge potential of WiFi for contactless sensing of human activities. Unfortunately, such sensing is highly sensitive to the relative orientation between the user and the WiFi receivers. To overcome this problem, existing solutions deploy multiple WiFi receivers at precise positions to capture orientation-independent view of the human activity. Orientation-independent single-receiver WiFi sensing is still considered an open problem. In this article, we propose a deep neural network architecture that uses radar data during training to learn high-precision Doppler features of human activities from the noisy channel states observed by a single WiFi receiver. Once trained with radars, the network can be used to detect human activities at any arbitrary orientations based only on WiFi signals. Using extensive experiments with millimeter-wave radars, we demonstrate that the proposed approach, called WiFi2Radar in this article, significantly outperforms state-of-the-art for detecting human activities in untrained orientations using only a single WiFi receiver. Our results show that WiFi2Radar can detect orientation-independent human activities with up to 91% accuracy, which outperforms the state of the art by 19%.

Quantifying the chemical process of milk spoilage is challenging due to the need for bulky, expensive equipment that is not user-friendly for milk producers or customers. This lack of a convenient and accurate milk spoilage detection system can cause two significant issues. First, people who consume spoiled milk may experience serious health problems. Secondly, milk manufacturers typically provide a “best before” date to indicate freshness, but this date only shows the highest quality of the milk, not the last day it can be safely consumed, leading to significant milk waste. A practical and efficient solution to this problem is proposed in this paper: a vibration-based milk spoilage detection method called VibMilk that utilizes the ubiquitous vibration motor and Inertial Measurement Unit (IMU) of off-the-shelf smartphones. The method detects spoilage based on the fact that the milk’s physical properties change, inducing different vibration responses at various stages of degradation. Using the InceptionTime deep learning model, VibMilk achieves 98.35% accuracy in detecting milk spoilage across 23 different stages, from fresh (pH = 6.6) to fully spoiled (pH = 4.4).

Existing fuzzy extractor and similar methods provide an effective way for extracting a secret key from a user’s biometric data, but are susceptible to impersonation attack: once a valid biometric sample is captured, the scheme is no longer secure. We propose a novel multi-factor fuzzy extractor that integrates both a user’s secret (e.g., a password) and a user’s biometrics in the generation and reconstruction process of a cryptographic key. We then employ this multi-factor fuzzy extractor to construct personal identity credentials, which can be used in a new multi-factor authenticated key exchange protocol that possesses multiple important features. First, the protocol provides mutual authentication. Second, the user and service provider can authenticate each other without the involvement of the identity authority. Third, the protocol can prevent user impersonation from a compromised identity authority. Finally, even when both a biometric sample and the secret are captured, the user can re-register to create a new credential using a new secret (renewable biometrics-based identity credentials). Most existing works on multi-factor authenticated key exchange only have a subset of these features. We formally prove that the proposed protocol is semantically secure. Our experiments carried out on the finger vein dataset SDUMLA achieved a low equal error rate (EER) of 0.04%, a reasonable computation time of 0.93 seconds for the user and service provider to authenticate and establish a shared session key, and a small communication overhead of 448 bytes.

The widespread integration of the Internet of Things (IoT) is crucial in advancing sustainable development. IoT service providers actively collect user data for analysis using sophisticated Deep Learning (DL) algorithms. This enables the extraction of valuable insights for business intelligence and improving service quality. However, as these datasets contain sensitive personal information, there is a risk of privacy breaches when DL models are employed. This vulnerability may result in Membership Inference Attacks (MIA), potentially leading to the unauthorized disclosure of highly sensitive data. Therefore, developing an efficient and privacy-preserving data analysis system for IoT is imperative. Recent research has highlighted the effectiveness of utilizing Bloom Filter (BF)-encoding in conjunction with Differential Privacy (DP) for safeguarding privacy during data analysis. Given its attributes of low complexity and high utility, this approach proves effective, particularly in resource-constrained IoT domains. With this in mind, we propose a novel framework for privacy-preserving IoT data analysis based on BF-encoded data. Our research introduces an innovative BF-encoding technique combined with Local Differential Privacy (LDP), capable of efficiently encoding various types of IoT data (such as facial images and smart-meter data) while maintaining privacy when integrated into DL algorithms for downstream analysis. Experimental results demonstrate that our BF-encoded data surpasses the utility of standard BF-encoded data when utilized in DL algorithms for downstream tasks, showcasing an approximate 30% improvement in classification accuracy. Furthermore, we assess the privacy of these DL models against MIA, revealing that attackers can only make random guesses with an accuracy of approximately 50%.

Visible light communications (VLC) have gained significant attention as a potential solution for the radio spectrum crunch. To achieve high data rates, emerging transmitter devices like 2D digital micro-mirror devices (DMD) have been proposed, offering significantly faster state flipping rates compared to conventional liquid crystalline shutters. However, previous approaches utilizing DMD suffered from a lack of spatial diversity, as they used all micro-mirrors in the same state. This paper introduces EV-DMD, a novel approach that utilizes DMD as a 2D transmitter, working in tandem with an event-based vision (EV) camera. In this method, multiple bit streams are transmitted in parallel through different mirror blocks of the DMD, while an EV camera simultaneously decodes multiple light blocks, enabling a truly 2D high-speed VLC system. To the best of our knowledge, this is the first implementation of a 2D VLC system that achieves an order-of-magnitude improvement in bit rate compared to state-of-the-art solutions.

Wearable devices-based biometrics has become mainstream in the biometric domain, especially in mobile computing, due to its convenience, flexibility, and potentially high user acceptance. Among various modalities, wearable devices-based gait recognition has been recognized as an effective user authentication method and employed in various applications, such as automated entry systems for home, school, work, vehicles, and automated ticket payment/validation for public transport. However, how secure wearable gait remains an open research question. In this study, we conduct a comprehensive security analysis of the wearable gait. Then, we demonstrate that gait itself is not robust against some attacking methods, such as spoofing or forgery. Therefore, we argue that an anti-spoofing mechanism is important for enhancing the security of wearable gait biometric systems. To this end, we proposed a novel authentication protocol called$Pistis$that embedded gait biometrics and a liveness detection mechanism that is aiming to detect various attacks of gait authentication systems. Our extensive experiments based on 50 subjects demonstrate that$Pistis$is effective in liveness detection and authentication performance enhancement, providing 100% accuracy for human and nonhuman detection, and 99.53% accuracy for user authentication. Pistis can be used as a liveness detection method for wearable devices-based biometrics, significantly for wearable gait.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments demonstrate that SolarGest achieves 99% for six gestures with a single cell and 95% for fifteen gesture with a$2\times 2$solar cell array. The power measuement study suggests that SolarGest consume 44% less power compared to light sensor based systems.

The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.

Recent research has shown the efficacy of machine learning-based IoT network traffic analysis to infer attributes such as IoT device type, IoT device activity state and even user behaviours in smart home environments. Therefore, various traffic obfuscation techniques have been proposed to reduce the classification performance of these machine learning algorithms. However, most of the proposed traffic obfuscation techniques can only alter traffic originating from the IoT device, with the incoming traffic from the communicating servers largely unaffected. We show that IoT device activity can still be successfully inferred by only using incoming network traffic for analysis. Therefore, this research emphasizes the need for obfuscation techniques, which can alter network traffic in both directions between the IoT devices and their communicating servers.

We propose a novel Visible Light Positioning (VLP) method, called Iris, that uses light spectral information (LSI) to localize humans completely passively in the sense that it neither requires the user to carry any device, nor does it require any modifications to existing lighting infrastructure. Iris localizes a user based on the interference they produce on the LSI recorded at an array of spectral sensors embedded in the environment. We design a deep neural network that can effectively learn location fingerprints directly from the sensor LSI data and predict locations accurately under varying lighting conditions. We prototype Iris using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength channels. We benchmark Iris against the state-of-the-art passive VLPs that rely on conventional photo-sensors capable of measuring only a single light intensity value aggregated over the entire visible spectrum. Our evaluations over two typical indoor environments, a 25 m2 one-bedroom apartment and a 13m × 8m office space, demonstrate that Iris can significantly reduce both the localization errors and the number of required sensors, while increasing robustness against changes in environmental lighting.

As the human hand makes direct physical contact with smartphones, significant efforts have recently been made to study the behavioral information of hand gripping of smartphones for user authentication purposes. Most existing methods leverage hand gripping behavior (e.g., gripping gesture, gripping position, gripping strength) of smartphones as biometrics to identify users. However, behavioral-based biometric authentication approaches may suffer from two problems: authentication performance (accuracy) degradation due to high-intra class variations arising from changes in user behavior over time, and vulnerability under spoofing attacks. To address these issues, we propose HoldPass, which is a behavior-independent in-hand user authentication method using vibration. HoldPass is able to adapt to the changes of hand gripping behavior of smartphones by extracting unique and stable physical features of human hands and eliminating the behavior-related prior information. Specifically, in HoldPass, we propose an adversarial neural network to achieve authentication based on unique physical features. Experiments with 10 users show that HoldPass can authenticate users with 97.39% accuracy while keeping False Accepted Rates (FAR) at a minimum of 2.1%.

In this paper, we investigate the impacts of location on the spectral distribution of received light, i.e., the intensity of light for different wavelengths, in indoor environments. Our findings show that, even when using the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. Based on this observation, we present Spectral-Loc, a novel indoor localization method that employs light spectrum information to detect the device's position. Because spectrum sensors are increasingly being used in new products and applications, such as white balance in smartphone photography, Spectral-Loc can be quickly implemented without the need for extra hardware or infrastructure. We used a commercially available light spectrum sensor, the AS7265x, to prototype Spectral-Loc, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations in two indoor areas, a meeting room and a large office, show that using light spectral information considerably decreases the localization error for different percentiles.

Piezoelectric energy harvester (PEH), which generates electricity from stress or vibrations, is attracting tremendous attention as a viable solution to extend battery life of wearable devices. More interestingly, besides the energy harvesting capability, recent research has demonstrated the feasibility of leveraging PEH as an power-free sensor for gait recognition as its stress or vibration patters are significantly influenced by the gait. However, as PEHs are not designed for precise motion sensing, the gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. In this work, to achieve high performance gait recognition and efficient energy harvesting at the same time, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signals. Second, we propose long short-term memory (LSTM) network-based classifiers to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12 percent higher recall and harvests up to 127 percent more energy while consuming 38 percent less power compared to the state-of-the-art.

Physical layer key generation has attracted considerable attention in the past decade since it provides an alternative solution for the key establishment in wireless networks using channel reciprocity. In this paper we explore the possibility of physical layer key generation for emerging Low Power Wide Area Networks (LPWAN) such as LoRa (Long Range). However, due to the lower transmission rates of LPWANs compared to Wi-Fi and Zigbee, the channel reciprocity is relatively low, which makes timely key generation challenging. To address this problem, we propose a novel information-theoretic key generation scheme that can operate at all data rate settings, featuring a model-based key generation method. Furthermore, we derive an optimal window size to calculate the parameters of the channel model based on a random waypoint model to balance the channel reciprocity and entropy. Extensive evaluations on a campus testbed show that our method can achieve up to 13.8 bps key generation rate. Compared to state-of-the-art methods, the proposed method improves key generation rate by 3x to 5x. We also analyzed the security of the proposed approach and demonstrated it to be resilient to eavesdropping attacks.

Localization is important for a large number of Internet of Things (IoT) endpoint devices connected by LoRaWAN. Due to the bandwidth limitations of LoRaWAN, existing localization methods without specialized hardware (e.g., GPS) produce poor performance. To increase the localization accuracy, we propose a super-resolution localization method, called Seirios, which features a novel algorithm to synchronize multiple non-overlapped communication channels by exploiting the unique features of the radio physical layer to increase the overall bandwidth. By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments. We design a Seirios prototype and evaluate its performance in an outdoor area of 100 m × 60 m, and an indoor area of 25 m × 15 m, which shows that Seirios can achieve a median error of 4.4 m outdoors (80% samples < 6.4 m), and 2.4 m indoors (80% samples < 6.1 m), respectively. The results show that Seirios produces 42% less localization error than the baseline approach. Our evaluation also shows that, different to previous studies in Wi-Fi localization systems that have wider bandwidth, time-of-fight (ToF) estimation is less effective for LoRaWAN localization systems with narrowband radio signals.

Research has shown the potential of device-free WiFi sensing for human identification. Each and every human has a unique gait and prior works suggest WiFi devices are able to capture the unique signature of a person's gait. In this article, we show for the first time that the monitored gait could be inconsistent and have mirror-like perturbations when individuals walk through WiFi devices in different directions, provided that the WiFi antenna array is horizontal to the walking path. Such inconsistent mirrored patterns are to negatively affect the uniqueness of gait and accuracy of human identification. Therefore, we propose a system called Gate-ID for accurately identifying individuals' identities irrespective of different walking directions. Gate-ID employs theoretical communication model and real measurements to demonstrate that antenna array orientations and walking directions contribute to the mirror-like patterns in WiFi signals. A novel heuristic algorithm is proposed to infer individual's walking directions. A set of methods are employed to extract and augment the representative spatial-temporal features of gait and enable the system performing irrespective of walking directions. We further propose a novel attention-based deep learning model that fuses various weighted features and ignores ineffective noises to uniquely identify individuals. We implement Gate-ID on commercial off-the-shelf devices. Extensive experiments demonstrate that our system can uniquely identify people with average accuracy of 90.7%-75.7% from a group of 6-20 people, respectively, and improve the accuracy by 12.5%-43.5% compared with baselines.

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the Cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this work, we propose Kryptein, a compressive-sensing-based lightweight encryption scheme for Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Kryptein supports random compressed encryption, statistical computation over cipher, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices. After comparing with other state-of-the-art lightweight ciphers on IoT (Simon and Speck), IoT system with Kryptein is expected to have a much more longevity with about 35 percent extended lifetime. Further, experiments illustrated IoT data variance will not affect Kryptein's accuracy in a long term usage, and Krpytein is also able to support basic analytics tasks like machine learning (e.g., classification).

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we propose Skin-MIMO, a deep learning based CSI acquisition technique to accurately predict CSI entirely based on inertial sensor (accelerometer and gyroscope) measurements at the transmitter, thus obviating the need for channel sounding. Based on experimental vibration data, we show that Skin-MIMO can improve MIMO capacity by a factor of 2.3 compared to Single-Input-Single-Output (SISO) or open-loop MIMO, which do not have access to CSI. A surprising finding is that gyroscope, which measures the angular velocity, is found to be superior in predicting skin vibrations than accelerometer, which measures linear acceleration and used widely in previous research for vibration communications over solid objects.

Taking the advantages of reciprocity and randomness of wireless fading channels, key generation via physical layer is attracting more attention. It becomes a remarkable solution for wireless communication in recent years. However, the feasibility under long-range and low data rate scenarios of narrow band low power wide area network (LPWAN) lacks proper studies. In this poster, we introduce a novel modeling method for Long Range Wide Area Network (LoRaWAN) key generation. The approach combines several signal processing techniques and using measured real-time Received Signal Strength Indicator (RSSI) to improve the applicability of key generation as well as increasing key generation rate (KGR) significantly.

Switchable privacy glass can electronically change its state between opaque and transparent. In this work, we propose to exploit the electronic configurability of switchable glass to modulate natural light, which can be demodulated by a nearby receiver with light sensing capability to realise data communication over natural light. A key advantage is that no energy is used to generate light, as it simply modulates the existing light in the nature. We demonstrate that the proposed data communication using switchable glass modulation can achieve 33.33 bits per second communication with a bit rate below 1% under a wide range of ambient luminance.

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAI). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this work, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of≈67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.

We address the problem of human posture detection with casual loose-fitting smart garments by fabricating a new type of highly sensitive, stretchable, optical transparent and low-cost strain sensor enabled by uniquely designed microcracks within a hybrid conductive thin film. In terms of sensitivity and stretchability, the developed sensor outperformed most of the works reported in recent literature, and has a gauge factor of 103 at the high strain of 58%. By attaching these sensors to an off-the-self casual jacket, we implement E-Jacket, a smart loose-fitting sensing garment prototype. To detect postures from sensor data, we implement a conventional deep learning model, CNN-LSTM, capable of overcoming the noise induced by the loose-fitting of the sensors to the human skin. To evaluate E-Jacket, we conducted three case studies in experimental environments: recognition of daily activities, recognition of stationary postures with random hand movements, and slouch detection. Our evaluation results demonstrate the feasibility of the proposed E-Jacket smart garment system for different posture recognition applications.

We demonstrate a human activity detection with casual loose-fitting smart garment system. By employing a new type of highly sensitive, stretchable, optical transparent and low-cost strain sensor and a deep learning model enabled by CNN-LSTM, the loose-fitting jacket is able to recognize 5 activities with 90.9% accuracy when the system is trained with the user data, and 73.5% accuracy when an unseen user wears the smart jacket, which is comparable with tight-fitting smart garment system. In the demonstration, we will showcase activity recognition of three activities: walk, sit, and stand.

Sensitivity of WiFi channel measurements to the transceiver placement is a major limitation for on-demand deployment of device-free WiFi sensing in environments where the transmitting/receiving devices may move. Using publicly available datasets, we show that even slight deviations of transmitter/receiver placements from the reference values can degrade device-free gesture recognition accuracy significantly. We design a convolutional autoencoder to translate WiFi spectrograms from arbitrary receiver placements to a reference placement configuration in a given area of interest with minimal human effort. Our experiments with the public datasets reveal that the proposed autoencoder can successfully reduce WiFi measurement variability caused by transmitter/receiver movement, which ultimately increases gesture recognition accuracy by up to 58%.

Recent work has shown that two wearable devices worn on the same user can exploit gait as a secret source to generate a common key for secure pairing. The main threat of using gait comes from side-channel attackers who can use cameras to record the walking user and extract accelerations from the video to pair with legitimate devices. We propose a novel pre-step that uses a CNN-LSTM deep learning model to classify the acceleration measurement devices, i.e., between IMU vs. Camera. We prototype the pre-step and evaluate it using real subjects. Our results show that the proposed pre-step can achieve high classification success rates. The experiments with different cut-off frequencies show that the higher acceleration frequencies appear to contain more distinguishable features to classify camera from IMU.

We propose Nephelai, a Compressive Sensing-based Cloud Radio Access Network (C-RAN), to reduce the uplink bit rate of the physical layer (PHY) between the gateways and the cloud server for multi-channel LPWANs. Recent research shows that single-channel LPWANs suffer from scalability issues. While multiple channels improve these issues, data transmission is expensive. Furthermore, recent research has shown that jointly decoding raw physical layers that are offloaded by LPWAN gateways in the cloud can improve the signal-to-noise ratio (SNR) of week radio signals. However, when it comes to multiple channels, this approach requires high bandwidth of network infrastructure to transport a large amount of PHY samples from gateways to the cloud server, which results in network congestion and high cost due to Internet data usage. In order to reduce the operation's bandwidth, we propose a novel LPWAN packet acquisition mechanism based on Compressive Sensing with a custom design dictionary that exploits the structure of LPWAN packets, reduces the bit rate of samples on each gateway, and demodulates PHY in the cloud with (joint) sparse approximation. Moreover, we propose an adaptive compression method that takes the Spreading Factor (SF) and SNR into account. Our empirical evaluation shows that up to 93.7% PHY samples can be reduced by Nephelai when SF = 9 and SNR is high without degradation in the packet reception rate (PRR). With four gateways, 1.7x PRR can be achieved with 87.5% PHY samples compressed, which can extend the battery lifetime of embedded IoT devices to 1.7.

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAIs). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this paper, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of < 8% when trained on 10-second segments, which reduces manual labelling overhead by ≈ 67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.

In recent years, we have witnessed the rapid development of the long range (LoRa) technology, together with extensive studies trying to understand its performance in various application settings. In contrast to measurements performed in large outdoor areas, a limited number of attempts have been made to understand the characterization and performance of the LoRa technology in indoor environments. In this article, we present a comprehensive study of the LoRa technology in multifloor buildings. Specifically, we investigate the large-scale fading characteristic, temporal fading characteristic, coverage, and energy consumption of the LoRa technology in four different types of buildings. Moreover, we find that the energy consumption using different parameter settings can vary up to 145 times. These results indicate the importance of parameter selection and enabling the LoRa adaptive data rate feature in energy-limited applications. We hope the results in this article can help both academia and industry understand the performance of the LoRa technology in multifloor buildings to facilitate developing practical indoor applications.

Bloom filter encoding has widely been used as an efficient masking technique for privacy-preserving matching functions. The existing matching techniques, however, are limited to relatively simple types such as string, categorical and signal numerical values. In this paper, we propose a new scheme that significantly extends the class of matching primitives that are based on privacy-preserving Bloom filter mechanism. These primitives include sequence data matching and popular distance-based machine learning algorithms such as KNN and SVM. Our scheme hash-maps a sequence data vector into the Bloom filter space while checking the similarity of the data points efficiently with negligible utility loss by adding a timestamp (bit) for each element in the data represented with its neighboring values. Furthermore, it includes a Laplace-like perturbation method on the constructed Bloom filters to address the weakness of deterministic probability led by encoding techniques. As a result, the proposed work guarantee the private data records are difficult to be discriminated due to collisions and differential privacy. The experimental results on three real-scenario based datasets illustrate that our method can achieve a significantly better trade-off between utility and privacy than the state-of-the-art differential privacy-based method by adding Laplace noise to the data directly.

We propose the use of the conventional energy storage component, i.e., capacitor, in the kinetic-powered wearable IoTs as the sensor to detect human activities. Since activities accumulate energy in the capacitor at different rates, the charging rate of the capacitor can be used to detect the activities. The key advantage of the proposed capacitor-based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal at a high rate, and thus, significantly reduces power consumption of the wearable device. The challenge we face is that capacitors are inherently non-linear energy accumulators, which leads to significant variations in the charging rates. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate in the charging cycles that are approximately linear. We design and implement a kinetic-powered shoe and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 57% less system power compared to conventional motion-sensor-based approaches.

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 h of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month.

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has a pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.

Recent research have devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual's limb motions in the WiFi spectrum could interfere wireless signal propagation which manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of a major challenge. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual's activities. To address this challenge, we propose WiEnhance, a WiFi based activity recognition system that synthesize variant activities data and mitigate the impact of activity inconsistency and subject-specific issues. We conduct extensive experiments and show an average 15.6% performance improvement on activity recognition.

Swing tracking is one of the key information for many sports such as golf. One approach to track swing is to use IMU to measure linear acceleration then get position by two-time integration. However, the complex noise model of the IMU limit the accuracy of the tracking. Another approach is to use depth sensor to measure 3D location of a point of interest directly. Unfortunately, the depth sensor-based approach cannot accurately measure the trajectory of a swing when the sensor is occluded, which happens regularly. To overcome these limitations, we develop a novel solution to make use of these two sensor modalities (i.e., IMU and depth sensor) by a novel deep neural network to produce high precision swing trajectory tracking. The learned network automatically makes use of the IMU when the depth sensor is occluded, and relies on depth sensor when IMU signal is noisy. Our experiment shows that the proposed method outperforms state-of-the-art swing tracking method by 62% of error reduction.

Physical layer key generation that exploits reciprocity and randomness of wireless fading channels has attracted considerable attention in recent years. Despite much research efforts in this field, the problem of wireless key generation at long distance and low data rate remains unknown and has not been studied. In this paper, we conduct extensive experiments and analysis in real indoor and outdoor environments to explore the feasibility of wireless key generation for long range (LoRa)-based network. Our experimental results show that: 1) the low transmission rate will lead to low channel reciprocity which makes wireless key generation significantly challenging and 2) when the requirement of high reciprocity is fulfilled, two nodes can generate the same secret key even when they are far away from each other (a few kilometers). Building on the strengths of existing secret key extraction approaches, we present LoRa-Key, the first complete key establishment protocol for LoRa network by exploring the shared randomness extracted from measured received signal strength indicator. LoRa-Key employs a number of signal processing techniques to improve key generation rate significantly. Moreover, we propose a novel compressive sensing-based reconciliation framework to reduce mismatch rate. Experimental results show that LoRa-Key can achieve key establishment rates of 18 bit/s in stationary scenario and 31 bit/s in mobile scenario. To the best of our knowledge, this is the first work that studies key generation protocol for LoRa network.

There is an increasing interest in Internet of Things (IoT) enabled smart buildings over the past decades. However, the development of smart buildings is impeded by the high installation/maintenance cost and the difficulty of large-scale evaluation in the wild. In this paper, we report the design, implementation, and deployment of an emergency light-based smart building solution. The key advantage of the system is that it is built on the top of the existing facilities in the building (i.e., emergency light). As a case study, we have implemented and deployed our system in nine production smart buildings of different types including residential, commercial office, and warehouse of multiple level building complexes. Using real data from four typical buildings, we show the proposed system can achieve >97% average packet delivery rate. Evaluation results also demonstrate the stability and robustness of the system to environmental changes. The results of this paper provide practical insights to facilitate the development of smart building systems.

With the fast penetration of commercial Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security issues of those devices have attracted significant interests from both academia and industry. Modern VR/AR systems typically use head-mounted devices (i.e., headsets) to interact with users, and often store private user data, e.g., social network accounts, online transactions or even payment information. This poses significant security threats, since in practice the headset can be potentially obtained and accessed by unauthenticated parties, e.g., identity thieves, and thus cause catastrophic breach. In this paper, we propose a novel GaitLock system, which can reliably authenticate users using their gait signatures. Our system doesn't require extra hardware, e.g., fingerprint sensors or retina scanners, but only uses the on-board inertial measurement units (IMUs) equipped in almost all mainstream VR/AR headsets to authenticate the legitimate users from intruders, by simply asking them to walk a few steps. To achieve that, we propose a new gait recognition model Dynamic-SRC, which combines the strength of Dynamic Time Warping (DTW) and Sparse Representation Classifier (SRC), to extract unique gait patterns from the inertial signals during walking. We implement GaitLock on Google Glass (a typical AR headset), and extensive experiments show that GaitLock outperforms the state-of-the-art systems significantly in recognition accuracy (> 98 percent success in 5 steps), and is able to run in-situ on the resource-constrained VR/AR headsets without incurring high energy cost.

Positioning with visual sensors in indoor environments has many advantages: it doesn’t require infrastructure or accurate maps, and is more robust and accurate than other modalities such as WiFi. However, one of the biggest hurdles that prevents its practical application on mobile devices is the time-consuming visual processing pipeline. To overcome this problem, this paper proposes a novel lifelong learning approach to enable efficient and real-time visual positioning. We explore the fact that when following a previous visual experience for multiple times, one could gradually discover clues on how to traverse it with much less effort, e.g., which parts of the scene are more informative, and what kind of visual elements we should expect. Such second-order information is recorded as parameters, which provide key insights of the context and empower our system to dynamically optimise itself to stay localised with minimum cost. We implement the proposed approach on an array of mobile and wearable devices, and evaluate its performance in two indoor settings. Experimental results show our approach can reduce the visual processing time up to two orders of magnitude, while achieving sub-metre positioning accuracy.

With the rapid development of sensor networks and embedded computing technologies, miniaturized wearable healthcare monitoring devices have become practically feasible. For many of these devices, accelerometer-based user authentication systems by gait analysis are becoming a hot research topic. However, a major bottleneck of such system is it requires continuous sampling of accelerometer, which reduces battery life of wearable sensors. In this paper, we present KEH-Gait, which advocates use of output voltage signal from kinetic energy harvester (KEH) as the source for gait recognition. KEH-Gait is motivated by the prospect of significant power saving by not having to sample the accelerometer at all. Indeed, our measurements show that, compared to conventional accelerometer-based gait detection, KEH-Gait can reduce energy consumption by 82.15 percent. The feasibility of KEH-Gait is based on the fact that human gait has distinctive movement patterns for different individuals, which is expected to leave distinctive patterns for KEH as well. We evaluate the performance of KEH-Gait using two different types of KEH hardware on a data set of 20 subjects. Our experiments demonstrate that, although KEH-Gait yields slightly lower accuracy than accelerometer-based gait detection when single step is used, the accuracy problem can be overcome by the proposed Probability-based Multi-Step Sparse Representation Classification (PMSSRC). Moreover, the security analysis shows that the EER of KEH-Gait against an active spoofing attacker is 11.2 and 14.1 percent using two different types of KEH hardware, respectively.

The rise of mobile crowd sensing has brought privacy issues into a sharp view. In this paper, our goal is to achieve the predictable privacy-preserving mobile crowd sensing, which we envision to have the capability to quantify the privacy protections, and simultaneously allowing application users to predict the utility loss at the same time. TheSalusalgorithm is first proposed to protect the private data against the data reconstruction attacks. To understand privacy protection, we quantify the privacy risks in terms of private data leakage under reconstruction attacks. To predict the utility, we provide accurate utility predictions for various crowd sensing applications using Salus. The risk assessments can be generally applied to different type of sensors on the mobile platform, and the utility prediction can also be used to support various applications that use data aggregators such as average, histogram, and classifiers. Finally, we propose and implement the$P^{3}$application framework. Both measurement results using online datasets and real-world case studies show that the$P^{3}$provides accurate risk assessments and utility estimations, which makes it a promising framework to support future privacy-preserving mobilecrowd sensing applications.

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this article, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

Heart rate is one of the most important vital signals for personal health tracking. A number of approaches were proposed to monitor heart rate, ranging from wearables to device-less systems. While WiFi has been shown to track heart rate accurately, existing solutions rely on directional antennas to improve the signal quality and ultimately the accuracy of heart rate estimation. Special hardware used in these approaches limits their applicability and truly device-less and ubiquitous heart rate monitoring is yet to be achieved.

The feasibility of using vibration energy harvesting (VEH) as an energy-efficient receiver for short-range acoustic data communication has been investigated recently. When data was encoded in acoustic signal within the energy harvesting frequency band and transmitted through a speaker, a VEH receiver was capable of decoding the data by processing the harvested energy signal. Although previous work created new opportunities for simultaneous energy harvesting and communication using the same hardware, the communication makes annoying sounds as the energy harvesting frequency band lies within the sensitive region of human auditory system. In this work, we present a novel modulation scheme to completely hide all communications within background music sound. The proposed modulation exploits sound masking theory to maximize signal to noise ratio of data communication without being audible to the music listener. We capitalize on the existence of repetitive sound patterns within popular music to realize synchronization between the transmitter and the receiver. We implement the proposed modulation within multiple hit songs and demonstrate its efficacy using a real VEH prototype made from off-the-shelf hardware. A user study involving 30 subjects confirms that the proposed modulation can completely hide VEH-based data communication from human perception while achieving up to 14 bps data rate, which is sufficient to transmit short codes or coupons of practical use.

Nowadays, smart wristbands have become one of the most prevailing wearable devices as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to extend the input. Yet by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which firstly leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rate, ViType designs a set of novel mechanisms, including an artificial neural network to process the vibration signals, and a runtime calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust to fight against various confounding factors. The average recognition accuracy is 94.8% with an initial training sample size of 20 for each key, which is 1.52 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98% on average during one month.

We propose a sparse approximation based joint-decoding system for LPWAN (LoRa) PHY-layer frame decoding. Recent research has shown that joint-decoding raw radio ADC samples in the Cloud offloaded from LPWAN gateways can decode weak radio signals by combining coherent frames. However, this approach requires high network bandwidth usage to collect a large amount of ADC samples from each gateway, which results in network congestion and high financial cost due to Internet data usage between the gateway and the Cloud server. In order to reduce the bandwidth usage of this data offloading operation, we propose a LPWAN packet acquisition mechanism based on joint sparse approximation.

Recent work has shown that it is possible for two wearable devices worn by the same user to generate a common key for secure pairing by exploiting gait as a common secret. A key challenge for such device pairing lies in matching the bits of the keys generated by two independent devices despite the noisy on-board sensor measurements. We propose a novel machine learning framework that uses an autoencoder to help one device predict the sensor observations at another device and generate the key using the predicted sensor data. We prototype the proposed method and evaluate it using real subjects. Our results show that the proposed method achieves a 10% increase in bit agreement rate between two keys generated independently by two different wearable devices.

The capability to securely (re)program embedded devices over-the-air is a fundamental functionality for the emerging Internet of Things (IoT). Current approaches work efficiently by exploiting the homogeneity within sensing devices, where all nodes require the update and participate in the process. Due to the heterogeneity of IoT deployments, where the type of devices and program images vary, existing solutions suffer from severe performance degradation. We address this shortcoming with our system SEDA, which leverages a secure multicast approach. SEDA outperforms existing systems since the program image is securely delivered and processed on targeted nodes only, hence reducing the overhead for not involved nodes. In order to enable the multicast approach, we introduce an asymmetric broadcast encryption primitive, which we have optimized towards constrained nodes to reduce the communication/computation overhead as compared to existing approaches. With an extensive experimental study on a public testbed in several practical settings, we show SEDA's efficient performance compared to state-of-the-art approaches. Finally, our theoretical security analysis shows SEDA's security against identified adversary models.

Face recognition is a hot research topic with a variety of application possibilities, including video surveillance and mobile payment. It has been well researched in traditional computer vision community. However, new research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. In this paper, we propose a robust and efficient sensor-assisted face recognition system on smart glasses by exploring the power of multimodal sensors including the camera and Inertial Measurement Unit (IMU) sensors. The system is based on a novel face recognition algorithm, namely Multi-view Sparse Representation Classification (MVSRC), by exploiting the prolific information among multi-view face images. To improve the efficiency of MVSRC on smart glasses, we propose two novel sampling optimization strategies using the less expensive inertial sensors. Our evaluations on public and private datasets show that the proposed method is up to 10 percent more accurate than the state-of-the-art multi-view face recognition methods while its computation cost is the same order as an efficient benchmark method (e.g., Eigenfaces). Finally, extensive real-world experiments show that our proposed system improves recognition accuracy by up to 15 percent while achieving the same level of system overhead compared to the existing face recognition system (OpenCV algorithms) on smart glasses.

Formal security analysis has proven to be a useful tool for tracking modifications in communication protocols in an automated manner, where full security analysis of revisions requires minimum efforts. In this paper, we formally analysed prominent IoT protocols and uncovered many critical challenges in practical IoT settings. We address these challenges by using formal symbolic modelling of such protocols under various adversaries and security goals. Furthermore, this paper extends formal analysis to cryptographic Denial-of-Service (DoS) attacks and demonstrates that a vast majority of IoT protocols are vulnerable to such resource exhaustion attacks. We present a cryptographic DoS attack countermeasure that can be generally used in many IoT protocols. Our study of prominent IoT protocols such as CoAP and MQTT shows the benefits of our approach.

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.

We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion data at high frequency, which promises significant power saving for the sensing device. We prototype a shoe-mounted KEH-powered wearable device and conduct experiments with 10 subjects for detecting 5 different activities. Our results show that compared to the existing time-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power, after considering wireless transmissions, by 75%. CapSense recognizes activities with up to 90%.

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve self-powered wearable system. Apart from generating energy to power the wearable devices, the output electricity signal of the PEH can also be used as an information source as it reflects the activity or motion patterns of the user. In this paper, we have designed and built an insole-based user authentication system by leveraging the AC voltage generated by the PEH during human walking. Meanwhile, the generated power is also collected and stored, which could be later used as the power source of the mobile system. By using a dataset of 20 subjects, we have demonstrated that our system can achieve 89.76% of human recognition accuracy when using only one gait cycle signal, and the accuracy can be further increased to 95.86% when two gait cycles are utilized.

Respiratory conditions significantly impact the health of individuals in the modern society. Long-term breath monitoring is critical for diagnosing the onset of various chronic respiratory diseases. Traditional breathing monitoring methods rely on wearable devices (e.q. face masks or chest bands) which are intrusive and uncomfortable. Recent research has demonstrated that it is possible to use device-free WiFi sensing to monitor breathing. However, these approaches only work when the monitored individual is stationary, i.e., sleeping or sitting perfectly still. In this paper, we propose WiCare, a system that employs the off-the-shelf WiFi devices and is able to monitor in-situ breathing rate in a natural setting where the individual can perform actions such as reading, writing, using phone, etc, which we refer to as micro motions. WiCare exploits Channel State Information (CSI) of WiFi data and can effectively distinguish breathing from the micro motions performed by the monitored individuals. The key idea is that certain specific subcarriers carry strong imprints of breathing motions because of the multipath effect and frequency and spacial diversity of MIMO systems. We model breathing signals as periodical sinusoidal waves and use curve fitting realised by interior point non-linear optimisation to identify breath in time series of each subcarrier. The goodness of fit measured by Dynamic Time Warping is exploited to select subcarriers that effectively capture breathing. Independent component analysis is used to precisely isolate the breathing signals. We recruit five participants to perform 9 common micro motions. Our extensive experiments show WiCare can accurately distinguish breathing from the micro motions and estimate breath rate with an average accuracy of over 90%. WiCare also outperforms the state-of-the-art breath rate estimation methods by up to 80%. WiCare represents a first and important step towards in-situ breath monitoring in natural settings.

This paper investigates the possibility of using a vibration energy harvesting (VEH) device as a communication receiver. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, we aim to transmit small amounts of data at low rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming digital signal processing (DSP), which makes a VEH receiver significantly more power efficient than a conventional microphone-based decoder. To address the extremely narrow bandwidth of VEH, we design a simple ON-OFF keying modulation, but optimized for VEH hardware. Experiments with a real VEH device shows that, at a distance of 2 cm, a laptop speaker with the proposed modulation scheme can achieve 30 bps communication for a target bit error rate of less than 1%, which would enable many emerging short range applications, such as mobile payment. The communication range of a laptop can be extended to 80 cm for 5 bps, allowing a range of other audio-based device-to-device communications, such as a web advertisement on a laptop browser transferring tokens to a nearby smartphone. We also demonstrate that the proposed VEH-based sound decoding is resilient to background noise, thanks to its extremely narrow power harvesting bandwidth, which works as a natural noise filter.

The wearable devices are small and easy to carry but typically with poor interaction experience. For example, Apple iWatch does not support instant text message input feature because of the lack of keyboard availability on the tiny touch screen. To address this problem, we develop a novel system, termed iKey, which enables users to use the back of one of their hands as virtual keyboard for wearable wristbands. iKey recognizes keystrokes based on a location-based training model via body vibration. We will demonstrate a real time functional prototype of iKey in this demo.

The Internet of Things (IoT) is an emerging paradigm, where the ubiquitous devices can form the networks and connect to Internet. Security and management of devices remain open challenges for the IoT. We adopt the management framework of industry consortium THREAD, where a group of devices cooperating to accomplish the same task (called policy) are administrated by a designated device called commissioner and together they form a policy group. All these policy groups are further managed by a centralized server. In this hierarchical network structure, the secure distribution of the policy information, access control, and group key from the centralized server to commissioner and its peers become challenging given the pervasive, complex and heterogeneous properties of devices. To solve this, we propose protocols/mechanisms along with a variant of Broadcast Encryption called Secure Identity-Based Broadcast Encryption (SIBBE) and demonstrate the feasibility for secure distribution of information to the IoT devices from centralized server. Most of the related work is based on the Attribute-based Encryption (ABE) for IoT devices, which has scalability issues with the number of attributes. Our experimental and simulation evaluations show that our scheme outperforms the existing schemes in terms of scalability, latency, and communication overhead.

The increased usage of smart wearables in various applications, specifically in health-care, emphasizes the need for secure communication to transmit sensitive health-data. In a practical scenario, where multiple devices are carried by a person, a common secret key is essential for secure group communication. Group key generation and sharing among wearables have received very little attention in the literature due to the underlying challenges: 1) difficulty in obtaining a good source of randomness to generate strong cryptographic keys, and 2) finding a common feature among all the devices to share the key. In this paper, we present a novel solution to generate and distribute group secret keys by exploiting on-board accelerometer sensor and the unique walking style of the user, i.e., gait. We propose a method to identify the suitable samples of accelerometer data during all routine activities of a subject to generate the keys with high entropy. In our scheme, the smartphone placed on waist employs fuzzy vault, a cryptographic construct, and utilizes the acceleration due to gait, a common characteristic extracted on all wearable devices to share the secret key. We implement our solution on commercially available off-the-shelf smart wearables, measure the system performance, and conduct experiments with multiple subjects. Our results demonstrate that the proposed solution has a bit rate of 750 b/s, low system overhead, distributes the key securely and quickly to all legitimate devices, and is suitable for practical applications.

Cross-correlation is a popular signal processing technique used in numerous location tracking systems for obtaining reliablerangeinformation. However, its efficient design and practical implementation has not yet been achieved on mote platforms that are typical in wireless sensor network due to resource constrains. In this paper, we proposeStructS-XCorr: cross-correlation via structured sparse representation, a new computing framework for ranging based on$\ell _1$-norm minimization[1]and structured sparsity. The key idea is to compress the ranging signal samples on the mote by efficient random projections and transfer them to a central device; where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposedcorrelationdictionary. Through theoretical validation, extensive empirical studies and experiments on anend-to-endacoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework can achieve up totwo orders of magnitudebetter performance compared to other approaches such as working on DCT domain and downsampling. Compared to the standard cross-correlation, it is able to obtain range estimates with a bias of 2-6 cm with 30 percent and approximately 100 cm with 5 percent compressed measurements. Its structured sparsity model is able to improve the ranging accuracy by 40 percent under challenging recovery conditions (such as high compression factor and low signal-to-noise ratio) by overcoming limitations due to dictionary coherence.

Face recognition is one of the important components of many smart devices apps, e.g., face unlocking, people tagging and games on smart phones, tablets, or smart glasses. Sparse Representation Classification (SRC) is a state-of-the-art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV, e.g., Eigenface algorithm. The success of SRC is due to its use of 21 optimization, which makes SRC robust to noise and occlusions. Since 21 optimization is computationally intensive, SRC uses random projection matrices to reduce the dimension of the 21 problem. However, random projection matrices do not give consistent classification accuracy as they ignored the prior knowledge of the training set. In this paper, we propose to exploit the prior knowlege of the training set to improve the recognition accuracy. It first learns the optimized projection matrix from the training set to produce consistent recognition performance then applies 21-based classification based on the group sparsity structure of SRC to further improve the recognition accuracy. Our evaluations, based on publicly available databases and real experiment, show that face recognition using optimized projection matrix is 8-17 percent more accurate than its random counterpart and Eigenface algorithm, and the recognition accuracy can be further improved by up to 5 percent by exploiting group sparsity structure. Furthermore, the optimized projection matrix does not have to be re-calculated even if new faces are added to the training set. We implement the SRC with optimized projection matrix on Android smartphones and find that the computation of residuals in SRC is a severe bottleneck, taking up 85-90 percent of the computation time. To address this problem, we propose a method to compute the residuals approximately, which is 50 times faster with little sacrificing recognition accuracy. Lastly, we demonstrate the feasibility of our new algorithm by the implementation and evaluation of a new face unlocking app and show its robustness to variation of poses, facial expressions, lighting changes, and occlusions.

Recent years have witnessed a remarkable growth in the number of smart wearable devices. For many of these devices, an important security issue is to establish an authenticated communication channel between legitimate devices to protect the subsequent communications. Due to the wireless nature of the communication and the extreme resource constraints of sensor devices, providing secure, efficient, and user-friendly device pairing is a challenging task. Traditional solutions for device pairing mostly depend on key predistribution, which is unsuitable for wearable devices in many ways. In this article, we design Gait-Key, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users’ walking characteristics (gait). The intuition is that the sensors on different locations on the same body experience similar accelerometer signals when the user is walking. However, one main challenge is that the accelerometer also captures motion signals produced by other body parts (e.g., swinging arms). We address this issue by using the blind source separation technique to extract the informative signal produced by the unique gait patterns. Our experimental results show that Gait-Key can generate a common 128-bit key for two legitimate devices with 98.3% probability. To demonstrate the feasibility, the proposed key generation scheme is implemented on modern smartphones. The evaluation results show that the proposed scheme can run in real time on modern mobile devices and incurs low system overhead.

Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.

Ubiquity of wearable and implantable devices sparks a new set of mobile computing applications that leverage the prolific information of sensors. For many of these applications, to ensure the security of communication between legitimate devices is a crucial problem. In this paper, we design Walkie-Talkie, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users' walking characteristics (gait). The intuition is that the sensors on different locations of the same body experience similar accelerometer signal when the user is walking. However, the accelerometer also captures motion signal produced by other body parts (e.g., swinging arms). We address this issue by employing Blind Source Separation (BSS) technique to extract the informative signal produced by the unique gait pattern. Our experimental results show that the keys generated by two independent devices on the same body are able to achieve up to 100% bit agreement rate. To demonstrate the feasibility, we implement the proposed key generation scheme on modern smartphones. The evaluation results show that the proposed scheme can run in real-time on modern mobile devices and incurs low system overhead.

Face recognition is one of the most popular research problems on various platforms. New research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. In this paper, we propose a robust and efficient sensor-assisted face recognition system on smart glasses by exploring the power of multimodal sensors including the camera and Inertial Measurement Unit (IMU) sensors. The system is based on a novel face recognition algorithm, namely Multi-view Sparse Representation Classification (MVSRC), by exploiting the prolific information among multi-view face images. To improve the efficiency of MVSRC on smart glasses, we propose a novel sampling optimization strategy using the less expensive inertial sensors. Our evaluations on public and private datasets show that the proposed method is up to 10% more accurate than the state-of-the-art multi-view face recognition methods while its computation cost is in the same order as an efficient benchmark method (e.g., Eigenfaces). Finally, extensive real-world experiments show that our proposed system improves recognition accuracy by up to 15% while achieving the same level of system overhead compared to the existing face recognition system (OpenCV algorithms) on smart glasses.

In recent years, the proliferation of wireless devices has contributed to the emergence of new set of applications termed as Location Based Services (LBS). LBS provide privileges to mobile users based on their proximity to a facility. In order to gain benefits, users may lie or falsely claim their location. Hence, it is essential to verify the legitimacy of users. In this paper, we propose our novel solution for generating location proof for mobile users and verification of the location claim by application services. Our protocol exploits unique Wi-Fi signal characteristics and employs an information theoretically secure fuzzy vault scheme. We provide a detailed theoretical and experimental evaluation of our protocol. Our solution is faster by an order of magnitude, and the performance of our scheme is independent of the location tag size and distance between the mobile user and location proof provider compared to the state-of-the-art.

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

Real-time target tracking is an important service provided by embedded camera networks. The first step in target tracking is to extract the moving targets from the video frames, which can be realised by using background subtraction. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computationally efficient. We propose a baseline version which uses luminance only and then extend it to use colour information. The key idea is to use random projection matrics to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, to show the computational efficiency of our methods is not platform specific, we implement it on various platforms. The real implementation shows that our proposed method is consistently better and is up to six times faster, and consume significantly less resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present.

Radio tomographic imaging (RTI) enables device free localisation of people and objects in many challenging environments and situations. Its basic principle is to detect the changes in the statistics of radio signals due to the radio link obstruction by people or objects. However, the localisation accuracy of RTI suffers from complicated multipath propagation behaviours in radio links. We propose to use inexpensive and energy efficient electronically switched directional (ESD) antennas to improve the quality of radio link behaviour observations, and therefore, the localisation accuracy of RTI. We implement a directional RTI (dRTI) system to understand how directional antennas can be used to improve RTI localisation accuracy. We also study the impact of the choice of antenna directions on the localisation accuracy of dRTI and propose methods to effectively choose informative antenna directions to improve localisation accuracy while reducing overhead. Furthermore, we analyse radio link obstruction performance in both theory and simulation, as well as false positives and false negatives of the obstruction measurements to show the superiority of the directional communication for RTI. We evaluate the performance of dRTI in diverse indoor environments and show that dRTI significantly outperforms the existing RTI localisation methods based on omni-directional antennas.

Exploiting unique wireless channel characteristics like signal strength for secret key generation has been recently studied by researchers. These schemes are lightweight and suitable for resource constrained wearable devices. However, a major drawback of existing schemes is that the successive channel samples with small sampling interval will have high correlation in time. This reduces the entropy and bit rate of keys. In this paper, we present dual-link based Radio Frequency fingerprinting solution - DLINK, which dynamically identifies the suitable multipath link to generate secret keys with improved entropy and bit rate in fast as well as slow fading channel conditions. We conduct an extensive set of experiments with real sensor devices mounted on subjects in multiple indoor environments. Our results show that, DLINK reduces the correlation of successive channel samples by 67%, and has 5 times higher bit rate, and improved entropy in all channel conditions compared to existing solutions.

The Internet of Things (IoT) is envisioned to digitize the physical world, resulting in a digital representation of our proximate living space. The possibility of inferring privacy violating information from IoT data necessitates adequate security measures regarding data storage and communication. To address these privacy and security concerns, we introduce our system that stores IoT data securely in the Cloud database while still allowing query processing over the encrypted data. We enable this by encrypting IoT data with a set of cryptographic schemes such as order-preserving and partially homomorphic encryptions. To achieve this on resource-limited devices, our system relies on optimized algorithms that accelerate partial homomorphic and order-preserving encryptions by 1 to 2 orders of magnitude. Our early results show the feasibility of our system on low-power devices. We envision our system as an enabler of secure IoT applications.

Point-to-point traffic has emerged as a widely used communications paradigm for cyber-physical systems and wireless sensor networks in industrial settings. However, existing point-to-point communication protocols often entail substantial overhead to find and maintain reliable routes. In recent research, protocols that rely on the phenomenon of constructive interference have thus emerged. They allow to quickly, efficiently, and reliably flood packets to the entire network. As all nodes in the network need to (re-)broadcast all packets in such protocols by design, substantial energy is consumed by nodes that do not even contribute to the actual point-to-point transmission. We propose a novel point-to-point communication protocol, called RFT, which attempts to discover the most reliable route between a source and a destination. To achieve this objective, RFT selects the minimum number of participating nodes required to ensure reliable communications while allowing all other devices in the network to sleep. During data transmissions, the nodes on the direct route as well as all helper nodes broadcast the data packets and exploit the benefits of constructive interference in order to reduce end-to-end latency.

In recent years, Location Based Services (LBS) and related applications are gaining popularity for providing access to the resources. LBS can either increase or decrease the access privileges of the users based on their location. Current mobile devices lack the intelligence to prove their location when requested. In this paper, we propose our novel solution for generating location proof for mobile users leveraging unique wireless characteristics and verification of the location claim by application services.

Current Wireless Sensor Networks (WSNs) approaches do not provide an efficient and secure code dissemination function due to emerging issues of IoT applications. In this work, we adopt a multicast approach instead of the existing end-to-end or epidemic approaches. In order to enable the multicast approach, we propose an efficient/robust group key distribution scheme. We will evaluate and quantify the performance of our prototype implementation in a public testbed, while emulating several practical IoT settings, and show our security measures against known attack models.

The Internet of Things, by digitizing the physical world, is envisioned to enable novel interaction paradigms with our surroundings. This creates new threats and leads to unprecedented security and privacy concerns. To tackle these concerns, we introduce Talos, a system that stores IoT data securely in a Cloud database while still allowing query processing over the encrypted data. We enable this by encrypting IoT data with a set of cryptographic schemes such as order-preserving and partially homomorphic encryption. In order to achieve this in constrained IoT devices, Talos relies on optimized algorithms that accelerate order-preserving and partially homomorphic encryption by 1 to 2 orders of magnitude. We assess the feasibility of Talos on low-power devices with and without cryptographic accelerators and quantify its overhead in terms of energy, computation, and latency. With a thorough evaluation of our prototype implementation, we show that Talos is a practical system that can provide a high level of security with a reasonable overhead. We envision Talos as an enabler of secure IoT applications.

With the fast development and increasing population of the wearable devices involves in our daily life, the security of the privacy information on those devices is attracting significant attentions. One of the possible solution is to enable the devices to recognise the real owner with authentication system. Biometrics recognition is popular used for authentication systems. The biometrics used including faces, fingerprints, gait cycles and etc. Using gait cycles as the criteria for identities recognition is superior than other biometrics as the gait information can be collected by the IMU sensors which are most popular embedded on portable devices and they cannot be reproduced by the invaders. We propose, Securitas, the continuous authentication system exploits the information from IMU sensors on the smart glasses to distinguish different wearers.

Face recognition is one of the most popular research problems on various platforms. New research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. In this paper, we have prototyped a robust and efficient sensor-assisted face recognition system on smart glasses by exploring the power of multimodal sensors including the camera and Inertial Measurement Unit (IMU) sensors. Evaluation shows that the prototyped system is up to 10% more accurate than the state-of-the-art face recognition methods while its computational cost is in the same order as an efficient benchmark method (e.g., Eigenface).

Given the limited resources and computational power of current embedded sensor devices, memory protection is difficult to achieve and generally unavailable. Hence, the software run-time buffer overflow that is used by the worm attacks in the Internet could be easily exploited to inject malicious codes into wireless sensor networks (WSNs). As a result, the remote attestation on the application codes installed in WSNs is the first important step to detect any unauthorized changes through the buffer overflow attack. Previous software-based remote code verification approaches such as SoftWare-based ATTestation and Secure Code Update By Attestation have been shown difficult to deploy in recent work. In this paper, we propose and implement a remote attestation protocol for detecting unauthorized tampering in the application codes running on sensor nodes with the assistance of Trusted Platform Modules (TPMs), a tiny, cost-effective and tamper-proof cryptographic micro-controller. In our design, each sensor node is equipped with a TPM, and the firmware running on the node could be verified by the other sensor nodes in a WSN, including the sink. Specifically, we present a hardware-based remote attestation protocol, discuss the potential attacks an adversary could launch against the protocol and provide comprehensive system performance results of the protocol in a multi-hop sensor network testbed. The experimental results demonstrated that our scheme is able to attest the application codes running in sensor node with small delay (less than 25 s for single-hop), considerable network lifetime and reasonable communication and energy overhead. Copyright © 2015 John Wiley & Sons, Ltd.

Crowd-sourced mobile embedded systems allow people to contribute sensor data, for critical applications, including transportation, emergency response and eHealth. Data integrity becomes imperative as malicious participants can launch software and Sybil attacks modifying the sensing platform and data. To address these attacks, we develop (1) a Trusted Sensing Peripheral (TSP) enabling collection of high-integrity raw or aggregated data, and participation in applications requiring additional modalities; and (2) a Secure Tasking and Aggregation Protocol (STAP) enabling aggregation of TSP trusted readings by untrusted intermediaries, while efficiently detecting fabricators. Evaluations demonstrate that TSP and STAP are practical and energy-efficient.

Radio connectivity in wireless sensor networks is highly intermittent due to unpredictable and time-varying noise and interference patterns in the environment. Because link qualities are not predictable prior to deployment, current deterministic solutions to unreliable links, such as increasing network density or transmission power, require overprovisioning of network resources and do not always improve reliability. We propose a new dual-radio network architecture to improve communication reliability in wireless sensor networks. Specifically, we show that radio transceivers operating at well-separated frequencies and spatially separated antennas offer robust communication, high link diversity, and better interference mitigation. We derive the optimal parameters for the dual-transceiver setup from frequency and space diversity in theory. We observe that frequency diversity holds the most benefits as long as the antennas are sufficiently separated to prevent coupling. Our experiments on an indoor/outdoor testbed confirm the theoretical predictions and show that radio diversity can significantly improve end-to-end delivery rates and network stability at only a small increase in energy cost over a single radio. Simulation experiments further validate the improvements in multiple topology configurations, but also reveal that the benefits of radio diversity are coupled to the number of available routing paths to the destination.

We investigate the problem of scheduling sensor node up-times to maximize the utility of the data they collect while operating within their resource constraints. We show that the optimal scheduling algorithm can improve data utility by more than 70% compared to naive schedules. We consider a suite of sensors with different capabilities and resource demands and represent their subsets as virtual sensors. For each virtual sensor, we calculate its optimal data fusion parameters and evaluate the sensors' performance in a given environment. The selection of virtual sensors best suited to collect data in a given environment can be modeled as an Integer Linear programming problem, and we study three different algorithms to solve the problem efficiently. We evaluate the performance of virtual sensor scheduling algorithms by extensive simulation. We show that even though the naive greedy scheduling approaches work well in some scenarios, none of them are able to match our best scheduling algorithm consistently, under varying environmental conditions and sensor resources.

The global positioning system (GPS) system is a dominant wireless technology that enables reliable location sensing for a diverse range of outdoor mobile applications. Following rising demands for location sensing, low-cost GPS receivers are becoming widely available; but their energy demands are still too high to be useful for many of these applications. For energy efficient GPS sensing, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose SparseGPS: a lightweight GPS acquisition mechanism based on sparse approximation.

The information-preserving sampling properties of compressive sensing have found a number of successful applications, such as sensor scheduling, localisation and tracking to deal with the resource constraints of the embedded systems. In this paper, we investigate an approach to improve the performance of compressive sensing applications through a novel strategy for optimising the projection matrix. We formulate the projection matrix optimisation problem and apply greedy algorithm to solve the optimisation problem efficiently. We evaluate the proposed approach by an emerging background subtraction method designed specifically for the embedded systems and show the proposed approach outperforms existing approaches significantly with little overhead.

Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based feature-less, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC), ℓ1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.

Wireless sensor networks (WSNs) enable the collection of physical measurements over a large geographic area. It is often the case that we are interested in computing and tracking the spatial-average of the sensor measurements over a region of the WSN. Unfortunately, the standard average operation is not robust because it is highly susceptible to sensor faults and heterogeneous measurement noise. In this paper, we propose a computational efficient method to compute a weighted average (which we will call robust average) of sensor measurements, which appropriately takes sensor faults and sensor noise into consideration. We assume that the sensors in the WSN use random projections to compress the data and send the compressed data to the data fusion centre. Computational efficiency of our method is achieved by having the data fusion centre work directly with the compressed data streams. The key advantage of our proposed method is that the data fusion centre only needs to perform decompression once to compute the robust average, thus greatly reducing the computational requirements. We apply our proposed method to the data collected from two WSN deployments to demonstrate its efficiency and accuracy.

Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. We propose cross-correlation via sparse representation: a new framework for ranging based on l1-minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two order of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Furthermore, compared to cross-correlation results, 30-40% measurements are sufficient to obtain precise range estimates with an additional bias of only 2-6cm for high accuracy application requirements, while 5% measurements are adequate to achieve approximately 100cm precision for lower accuracy applications.

Background subtraction is often the first step in many computer vision applications such as object localisation and tracking. It aims to segment out moving parts of a scene that represent object of interests. In the field of computer vision, researchers have dedicated their efforts to improve the robustness and accuracy of such segmentations but most of their methods are computationally intensive, making them non-viable options for our targeted embedded camera platform whose energy and processing power is significantly more constrained. To address this problem as well as maintain an acceptable level of performance, we introduce Compressive Sensing (CS) to the widely used Mixture of Gaussian to create a new background subtraction method. The results show that our method not only can decrease the computation significantly (a factor of 7 in a DSP setting) but remains comparably accurate.

Sparse approximation has now become a buzzword for classification in numerous research domains. We propose a distributed sparse approximation method based on l1 minimization for frog sound classification, which is tailored to the resource constrained wireless sensor networks. Our pilot study demonstrates that l1 minimization can run on wireless sensor nodes producing satisfactory classification accuracy.

This paper presents the design, implementation and evaluation of an automated method for distributing symmetric cryptographic keys in a Wireless Sensor Network (WSN). Unlike previous methods for key distribution in WSNs, we do not rely on sensitive knowledge to be stored in program memory prior to network deployment. Additionally, the protocol proposed uses dominant security primitives to ensure strong security and interoperability with existing networks (such as the Internet), while operating independent of the network layer protocol. Through both hardware experimentation and simulation, we show that this protocol can provide strong confidentiality, integrity and authenticity protection to the symmetric keys as they are distributed throughout a network, while maintaining the ability to scale to large-size networks and remain energy efficient.

Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. The design of a successful participatory sensing application is met with two challenges - (1) user privacy and (2) data trustworthiness. Addressing these challenges concurrently is a non-trivial task since they result in conflicting system requirements. User privacy is often achieved by removing the links between successive user contributions while such links are essential in establishing trust. In this work, we present a way to transfer reputation values (which is a proxy for assessing trustworthiness) between anonymous contributions. We also propose a reputation anonymization scheme that prevents the inadvertent leakage of privacy due to the inherent relationship between reputation information. We conduct extensive simulations using real-world mobility traces and practical application. The results show that our solution reduces the probabilities of users being tracked via successive contributions by as much as 80%. Moreover, this improvement has no discernible impact on the normal operation of the application.

Cross-correlation is a popular signal processing technique used for obtaining reliable range information. Recently, a practical and efficient implementation of cross-correlation (via sparse approximation) was demonstrated on resource constrained wireless sensor network platforms, where the key idea was to compress the received signal samples, and transfer them to central device where the range information was retrieved by l1-minimization. Although, this mechanism yields accurate ranging results, its applicability is limited due to its slow execution speed and inaccurate recovery of the correlation peak magnitude, which implicitly provides the useful measure of signal-to-noise ratio. In this work, we propose Fast Gradient Projection (F-GP), a new l1-minimization algorithm, which overcomes the existing limitations, and provides fast and accurate ranging.

Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

Usecases for wireless sensor networks, such as building automation or patient care, often collect and transmit sensitive information. Yet, many deployments currently do not protect this data through suitable security schemes. We propose an end-to-end security scheme build upon existing internet standards, specifically the Datagram Transport Layer Security protocol (DTLS). By relying on an established standard existing implementations, engineering techniques and security infrastructure can be reused which enables easy security uptake. We present a system architecture for this scheme and show its feasibility through the evaluation of our implementation.

We describe and evaluate a new programming and communications framework that eases the creation of complex heterogeneous systems comprising robots and sensor networks. We use a light-weight RPC-based service framework that allows robots and static sensor nodes to be considered as services, accessible to an internet connected end-user, or to each other. We present experimental results for a very large environmental monitoring application comprising a floating sensor network and a robotic boat.

We describe our current work towards a framework that establishes a hierarchy of devices (sensors and actuators) within a wireless multimedia node and uses frequent sampling of cheaper devices to trigger the activation of more energy-hungry devices. Within this framework, we consider the suitability of servos for Wireless Multimedia Sensor Networks (WMSNs) by examining their functional characteristics and energy consumption [2].

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps.

Given the limited resources and computational power of current embedded sensor devices memory protection is difficult to achieve and generally unavailable. Hence, the buffer overflow that is used by the worm attacks in the Internet can be easily exploited to inject malicious code into Wireless Sensor Networks (WSNs). We designed a hardware-based remote attestation protocol to counter the buffer overflow attack. In our attestation protocol, each sensor node is equipped with a Trusted Platform Module (TPM) board. The TPM is responsible for content verification of the program flash. To the best of our knowledge, it is the first remote attestation protocol in WSNs with each sensor node equipped with TPM.

Participatory sensing is a revolutionary new paradigm in which volunteers collect and share information from their local environment using mobile phones. The inherent openness of this platform makes it easy to contribute corrupted data. This paper proposes a novel reputation system that employs the Gompertz function for computing device reputation score as a reflection of the trustworthiness of the contributed data. We implement this system in the context of a participatory noise monitoring application and conduct extensive real-world experiments using Apple iPhones. Experimental results demonstrate that our scheme achieves three-fold improvement in comparison with the state-of-the-art Beta reputation scheme.

This paper is concerned with the application of wireless sensor network (WSN) technology to long-duration and large-scale environmental monitoring. The holy grail is a system that can be deployed and operated by domain specialists not engineers, but this remains some distance into the future. We present our views as to why this field has progressed less quickly than many envisaged it would over a decade ago. We use real examples taken from our own work in this field to illustrate the technological difficulties and challenges that are entailed in meeting end-user requirements for information gathering systems. Reliability and productivity are key concerns and influence the design choices for system hardware and software. We conclude with a discussion of long-term challenges for WSN technology in environmental monitoring and outline our vision of the future.

This article presents the design and implementation of a trusted sensor node that provides Internet-grade security at low system cost. We describe trustedFleck, which uses a commodity Trusted Platform Module (TPM) chip to extend the capabilities of a standard wireless sensor node to provide security services such as message integrity, confidentiality, authenticity , and system integrity based on RSA public-key and XTEA-based symmetric-key cryptography. In addition trustedFleck provides secure storage of private keys and provides platform configuration registers (PCRs) to store system configurations and detect code tampering. We analyze system performance using metrics that are important for WSN applications such as computation time, memory size, energy consumption and cost. Our results show that trustedFleck significantly outperforms previous approaches (e.g., TinyECC) in terms of these metrics while providing stronger security levels. Finally, we describe a number of examples, built on trustedFleck, of symmetric key management, secure RPC, secure software update, and remote attestation .

We consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial field of some scalar physical quantities. Our goal is to obtain a sufficiently accurate approximation of the temporal-spatial field with as little energy as possible. We propose an adaptive algorithm, based on the recently developed theory of adaptive compressive sensing, to collect information from WSNs in an energy efficient manner. The key idea of the algorithm is to perform ¿projections¿ iteratively to maximise the amount of information gain per energy expenditure. We prove that this maximisation problem is NP-hard and propose a number of heuristics to solve this problem. We evaluate the performance of our proposed algorithms using data from both simulation and an outdoor WSN testbed. The results show that our proposed algorithms are able to give a more accurate approximation of the temporal-spatial field for a given energy expenditure.

Noise map can provide useful information to control noise pollution. We propose a people-centric noise collection system called the Ear-Phone. Due to the voluntary participation of people, the number and location of samples cannot be guaranteed. We propose and study two methods, based on compressive sensing, to reconstruct the missing samples.

This article investigates a wireless acoustic sensor network application—monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing, wide area sensing coverage and long-lived unattended operation. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to ninth frog species found in northern Australia. Our first prototype consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates four algorithms designed to account for the sampling, processing, energy, and communication bottlenecks of the Mica2s (1) high frequency sampling, (2) thresholding and noise reduction, to reduce data transmission by up to 90%, (3) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate, and (4) harvesting-aware energy management, which exploits sensor energy harvesting capabilities to extend the system lifetime. Our evaluation shows the performance of our systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

In this paper, we describe the design and the implementation of a management system called SRM for controlling data reliability in Wireless Sensor Networks. SRM is based on a hierarchical management architecture and policy-based network management paradigm. SRM consists of four modules: a user policy specification module, an evaluation module, a decision making module and an action module. The interaction among these modules ensures that the network provides adequate information to the users while reducing energy consumption. To demonstrate the effectiveness of the management framework, we design a policy for balancing energy consumption and data reliability. Our experimental results show that SRM can meet the reliability requirements, and reduces energy consumption by up to 50% compared to the case of no management.

Communication security for wireless sensor networks (WSN) is a challenge due to the limited computation and energy resources available at nodes. We describe the design and implementation of a public-key (PK) platform based on a standard Trusted Platform Module (TPM) chip that extends the capability of a standard node. The result facilitates message security services such as confidentiality, authenticity and integrity. We present results including computation time, energy consumption and cost.

This paper investigates a mobile, wireless sensor/actuator network application for use in the cattle breeding industry. Our goal is to prevent fighting between bulls in on-farm breeding paddocks by autonomously applying appropriate stimuli when one bull approaches another bull. This is an important application because fighting between high-value animals such as bulls during breeding seasons causes significant financial loss to producers. Furthermore, there are significant challenges in this type of application because it requires dynamic animal state estimation, real-time actuation and efficient mobile wireless transmissions. We designed and implemented an animal state estimation algorithm based on a state-machine mechanism for each animal. Autonomous actuation is performed based on the estimated states of an animal relative to other animals. A simple, yet effective, wireless communication model has been proposed and implemented to achieve high delivery rates in mobile environments. We evaluated the performance of our design by both simulations and field experiments, which demonstrated the effectiveness of our autonomous animal control system.

This paper investigates a wireless sensor network deployment - monitoring water quality, e.g. salinity and the level of the underground water table - in a remote tropical area of northern Australia. Our goal is to collect real time water quality measurements together with the amount of water being pumped out in the area, and investigate the impacts of current irrigation practice on the environments, in particular underground water salination. This is a challenging task featuring wide geographic area coverage (mean transmission range between nodes is more than 800 meters), highly variable radio propagations, high end-to-end packet delivery rate requirements, and hostile deployment environments. We have designed, implemented and deployed a sensor network system, which has been collecting water quality and flow measurements, e.g., water flow rate and water flow ticks for over one month. The preliminary results show that sensor networks are a promising solution to deploying a sustainable irrigation system, e.g., maximizing the amount of water pumped out from an area with minimum impact on water quality.

This short-paper presents the design and experimental validation of model-based, mobile routing protocol for a delay tolerant network (DTN), where herds of animals are utilised as message ferries. We develop a novel routing protocol that utilises knowledge of the predicted behaviour of each ferry in order to choose optimal ferries for carrying messages from source to sink nodes, as well as minimise routing overhead of the network via adaptive beaconing based on current behaviour.

This paper investigates the problem of how to improve TCP performance in multi-rate ad-hoc networks with congested links. To improve network performance, different rate adaptation algorithms, such as automatic rate fallback (ARF) and receiver-based autorate (RBAR), have been proposed to adapt the data rate according to the current channel quality. Opportunistic auto rate (OAR) protocol is an optimisation for any existing rate adaptation algorithm which leads to a significant performance gain by providing temporal fairness. We analyze the reasons for the high performance gain obtained using OAR, and show that the OAR protocol does not work well for TCP communications in ad-hoc networks where nodes use different data rates to communicate with each other (heterogeneous). Based on these analysis, we propose a congestion reactive opportunistic auto rate (CROAR) protocol, which is a new rate adaption enhancement tailored to improve TCP performance in heterogeneous multi-hop ad-hoc networks. Extensive simulations show that CROAR, compared to OAR and RBAR, produces significant throughput and end-to-end transmission latency improvements while only marginally relaxing temporal fairness

In this paper, we design and evaluate an energy efficient data retrieval architecture for continuous aggregate queries in wireless sensor networks. We show how the modification of precision in one sensor affects the sample-reporting frequency of other sensors, and how the precisions of a group of sensors may be collectively modified to achieve the target quality of information (QoI) with higher energy-efficiency. The proposed collective adaptive precision setting (CAPS) architecture is then extended to exploit the observed temporal correlation among successive sensor samples for even greater energy efficiency. Detailed simulations with synthetic and real data traces demonstrate how the combination of weak consistency semantics and temporal correlation can dramatically lower the energy consumption in practical sensor environments

This paper investigates a wireless, acoustic sensor network application-monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate. We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

This demonstration shows a wireless, acoustic sensor network application--- monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our system uses automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see Fig. 1). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage [2]. Our prototype consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica motes operating in concert. The Mica motes are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks.

The paper investigates an anycast communication paradigm for a hybrid sensor/actuator network, consisting of both resource-rich and resource-impoverished devices. The key idea is to exploit the capabilities of resource-rich devices (called micro-servers) to reduce the communication burden on smaller sensor nodes which are energy, bandwidth and memory constrained. The goal is to deliver sensor data to the nearest micro-server, which can (i) store it, (ii) forward it to other micro-servers using out-of-band communication, or (iii) perform the desired actuation. Our approach is to construct an anycast tree rooted at each potential event source, which micro-servers can dynamically join and leave. Our anycast mechanism is self-organizing, distributed, robust, scalable, and incurs very little overhead. Simulations using ns-2 show that our anycast mechanism can reduce network energy consumption by more than 50%, both the mean end-to-end latency of the transmission and the mean number of transmissions by more than 50%, and achieves 99% data delivery rate for low and moderate micro-server mobility rate.