ORCID Profile
0000-0002-1350-6639
Current Organisation
Macquarie University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Ubiquitous Computing | Nanotechnology | Nanotechnology | Biosensor Technologies | Diagnostic Applications | Information Systems | Distributed Computing | Information Systems Development Methodologies | Conceptual Modelling | Mobile Technologies | Networking and Communications
ANZSRC Socio-Economic Objective (SEO)
Communication Networks and Services not elsewhere classified | Information Processing Services (incl. Data Entry and Capture) | Mobile Data Networks and Services | Health Related to Ageing | Higher education | Health Status (e.g. Indicators of Well-Being) | Medical instrumentation | Diagnostic methods |
Related Links
Publications
Exploiting Link Diversity for Performance-Aware and Repeatable Simulation in Low-Power Wireless Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Publisher: IEEE
Date: 04-2020
Interference-Aware SaaS User Allocation Game for Edge Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
GaitTracker: 3D Skeletal Tracking for Gait Analysis Based on Inertial Measurement Units
Publisher: Association for Computing Machinery (ACM)
Date: 16-03-2022
DOI: 10.1145/3502722
Abstract: Gait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, which implies that the patients cannot receive adequate gait assessment anytime and anywhere. In this article, we propose GaitTracker, a novel system to remotely and continuously perform gait monitoring and analysis by three-dimensional (3D) skeletal tracking in a wearable approach. Specifically, this system consists of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. According to the measurements from these IMUs, we can obtain the motion signals of lower limbs during gait rehabilitation. By adaptively synchronizing coordinate systems of different IMUs and building the geometric model of lower limbs, the exact gait movements can be reconstructed, and gait parameters can be extracted without any prior knowledge. GaitTracker offers three key features: (1) a unified 3D skeletal model to depict the precise gait movement and parameters in 3D space, (2) a coordinate system synchronization scheme to perform space synchronization over all the IMU sensors, and (3) an automatic estimation method for the user-specific geometric parameters. In this way, GaitTracker is able to accurately perform 3D skeletal tracking of lower limbs for gait analysis, such as evaluating the gait symmetry and the gait parameters including the swing/stance time. We implemented GaitTracker and evaluated its performance in real applications. The experimental results show that, the average error for skeleton angle estimation, joint displacement estimation, and gait parameter estimation are 3∘, 2.3%, and 3%, respectively, outperforming the state of the art.
Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features
Publisher: Association for Computing Machinery (ACM)
Date: 23-08-2016
DOI: 10.1145/2890511
Abstract: Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy in iduals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy in iduals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.
SILoc: A Speed Inconsistency-Immune Approach to Mobile RFID Robot Localization
Publisher: IEEE
Date: 10-05-2021
MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Demo Abstract: A Novel Firmware Update Over-The-Air System for LoRa Networks
Publisher: ACM
Date: 09-05-2023
Rendezvous Cost-Aware Opportunistic Routing in Heterogeneous Duty-Cycled Wireless Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Towards a Diffraction-based Sensing Approach on Human Activity Recognition
Publisher: Association for Computing Machinery (ACM)
Date: 29-03-2019
DOI: 10.1145/3314420
Abstract: In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable. With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect erse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.
PCube: Scaling LoRa Concurrent Transmissions with Reception Diversities
Publisher: Association for Computing Machinery (ACM)
Date: 30-11-2022
DOI: 10.1145/3545571
Abstract: This article presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique that is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception ersities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency-based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.
A Reliability-Augmented Particle Filter for Magnetic Fingerprinting Based Indoor Localization on Smartphone
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence
Publisher: ACM
Date: 30-04-2023
Your Breath Doesn't Lie: Multi-user Authentication by Sensing Respiration Using mmWave Radar
Publisher: IEEE
Date: 20-09-2022
Supporting Serendipitous Social Interaction Using Human Mobility Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Energy balanced data collection in Wireless Sensor Networks
Publisher: IEEE
Date: 10-2012
Pragmatic models for the prediction and digital mapping of soil properties in eastern Australia
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR13306
Abstract: To help meet the increasing need for knowledge and data on the spatial distribution of soils, readily applied multiple linear regression models were developed for key soil properties over eastern Australia. Selected covariates were used to represent the key soil-forming factors of climate (annual precipitation and maximum temperature), parent material (a lithological silica index) topography (new topo-slope and aspect indices) and biota (a modified land disturbance index). The models are presented at three depth intervals (0–10, 10–30 and 30–100 cm) and are of variable but generally moderate statistical strength, with concordance correlation coefficients in the order of 0.7 for organic carbon (OC) upper depth, pHca, sum of bases, cation exchange capacity (CEC) and sand, but somewhat lower (0.4–0.6) for OC lower depths, total phosphorous, clay and silt. The pragmatic models facilitate soil property predictions at in idual sites using only climate and field-collected data. They were also moderately effective for deriving digital soil maps over the state of New South Wales and a regional catchment. The models and derived maps compared well in predictive ability to those derived from more sophisticated techniques involving Cubist decision trees with remotely sensed covariates. The readily understood and interpreted nature of these products means they may provide a useful introduction to the more advanced digital soil modelling and mapping techniques. The models provide useful information and broader insights into the factors controlling soil distribution in eastern Australia and beyond, including the change in a soil property with a given unit change in a covariate.
Industrial Vision: Rectifying Millimeter-Level Edge Deviation in Industrial Internet of Things With Camera-Based Edge Device
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Enabling Out-of-Band Coordination of Wi-Fi Communications on Smartphones
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
DynaKey: Dynamic Keystroke Tracking Using a Head-Mounted Camera Device
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
BTrack: Using Barometer for Energy Efficient Location Tracking on Mountain Roads
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Surviving screen-off battery through out-of-band Wi-Fi coordination
Publisher: IEEE
Date: 05-2017
Towards Repeatable Wireless Network Simulation Using Performance Aware Markov Model
Publisher: IEEE
Date: 04-2018
Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Ontology based context modeling and reasoning using OWL
Publisher: IEEE
Date: 2004
Constructing Floor Plan through Smoke Using Ultra Wideband Radar
Publisher: Association for Computing Machinery (ACM)
Date: 27-12-2021
DOI: 10.1145/3494977
Abstract: Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.
Connected Target ϵ-probability Coverage in WSNs With Directional Probabilistic Sensors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Smart Diagnosis: Deep Learning Boosted Driver Inattention Detection and Abnormal Driving Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-03-2022
Minimizing inter-server communications by exploiting self-similarity in online social networks
Publisher: IEEE
Date: 10-2012
Freedom: Online Activity Recognition via Dictionary-Based Sparse Representation of RFID Sensing Data
Publisher: IEEE
Date: 11-2015
Recognizing multi-user activities using wearable sensors in a smart home
Publisher: Elsevier BV
Date: 06-2011
Enabling secure touch-to-access device pairing based on human body's electrical response
Publisher: ACM
Date: 14-10-2022
SBC: Scalable Smartphone Barometer Calibration through Crowdsourcing
Publisher: ICST
Date: 2014
Separating Voices from Multiple Sound Sources using 2D Microphone Array
Publisher: IEEE
Date: 02-05-2022
Exploring traffic congestion correlation from multiple data sources
Publisher: Elsevier BV
Date: 10-2017
Accurate Corruption Estimation in ZigBee under Cross-Technology Interference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Learning from less for better
Publisher: ACM
Date: 12-09-2016
Mobility-aware and Privacy-protecting QoS Optimization in Mobile Edge Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Edge-Eye: Rectifying Millimeter-level Edge Deviation in Manufacturing using Camera-enabled IoT Edge Device
Publisher: IEEE
Date: 05-2022
Rapid
Publisher: Association for Computing Machinery (ACM)
Date: 11-09-2017
DOI: 10.1145/3130906
Abstract: Device-free human sensing is a key technology to support many applications such as indoor navigation and activity recognition. By exploiting WiFi signals reflected by human body, there have been many WiFi-based device-free human sensing applications. Among these applications, person identification is a fundamental technology to enable user-specific services. In this paper, we present Rapid, a system that can perform robust person identification in a device-free and low-cost manner, using fine-grained channel information (i.e., CSI) of WiFi and acoustic information from footstep sound. In order to achieve high accuracy in real-life scenarios with both system and environment noise, we perform noise estimation and include two different confidence values to quantify the impact of noise to both CSI and acoustic measurements. Based on an accurate gait analysis, we then adaptively fuse CSI and acoustic measurements to achieve robust person identification. We implement low-cost Rapid nodes and evaluate our system using experiments at multiple locations with a total of 1800 gait instances from 20 volunteers, and the results show that Rapid identifies a subject with an average accuracy of 92% to 82% from a group of 2 to 6 subjects, respectively.
DeepKey
Publisher: Association for Computing Machinery (ACM)
Date: 31-05-2020
DOI: 10.1145/3393619
Abstract: Biometric authentication involves various technologies to identify in iduals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.
An Analytical Model for Coding-Based Reprogramming Protocols in Lossy Wireless Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Target-Aware, Transmission Power-Adaptive, and Collision-Free Data Dissemination in Wireless Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
SMinder: Detect a Left-behind Phone using Sensor-based Context Awareness
Publisher: Springer Science and Business Media LLC
Date: 26-12-2017
Who should I invite for my party?
Publisher: ACM
Date: 07-09-2015
Mining Emerging Sequential Patterns for Activity Recognition in Body Sensor Networks
Publisher: Springer Berlin Heidelberg
Date: 2012
Investigating the association between sleep parameters and the weight status of children: night sleep duration matters
Publisher: Elsevier BV
Date: 04-2018
DOI: 10.1016/J.SLEH.2017.12.009
Abstract: To examine the associations between sleep parameters and weight status in a large s le of preschool children. Cross-sectional survey data from the Effective Early Educational Experiences for children (E4Kids) study were analyzed. 1111 children aged 3 to 6 years from Queensland and Victoria, Australia. General linear modeling, with adjustment for significant control variables, assessed the impact of night sleep duration, total sleep duration, napping frequency, sleep timing (onset, offset and midpoint), and severity of sleep problems on standardized body mass index (BMI z score). General linear modeling was conducted for the total s le and then separately by sex. For the total s le, there was a significant association between short sleep duration (≤10 hours) and increased BMI z score. No other sleep parameters were associated with BMI z score in this s le. Analyses by sex revealed that, among girls, there were no associations between any sleep parameter and BMI z score. However, among boys, short night sleep duration and napping frequency were both significantly associated with weight status even after adjustment for controls. Night sleep duration is a consistent independent predictor of body mass in young children. These results identify a complex relationship between sleep and body mass that implicates sex. Potential mechanisms that might explain sex differences warrant further investigation.
WiPhone
Publisher: Association for Computing Machinery (ACM)
Date: 19-03-2021
DOI: 10.1145/3448092
Abstract: Recent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI litude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.
Crowdsourced smartphone sensing for localization in metro trains
Publisher: IEEE
Date: 06-2014
Recognizing Multiuser Activities Using Wireless Body Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
DOI: 10.1109/TMC.2011.43
C-FMCW Based Contactless Respiration Detection Using Acoustic Signal
Publisher: Association for Computing Machinery (ACM)
Date: 08-01-2018
DOI: 10.1145/3161188
Abstract: Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the s ling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.
Mining Traffic Congestion Correlation between Road Segments on GPS Trajectories
Publisher: IEEE
Date: 05-2016
BloomCast: Efficient and Effective Full-Text Retrieval in Unstructured P2P Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Energy-Efficient Task Offloading in RIS-Aided HetNets With Wireless Backhaul
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization using Satellite Images
Publisher: IEEE
Date: 04-2020
Cantor: Improving Goodput in LoRa Concurrent Transmission
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
OmniResMonitor: Omnimonitoring of Human Respiration using Acoustic Multipath Reflection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Assessing User Mental Workload for Smartphone Applications With Built-In Sensors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
WiFi-Sleep: Sleep Stage Monitoring Using Commodity Wi-Fi Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-09-2021
Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/3410350
Abstract: This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame’s transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.
SwipePass: Acoustic-based Second-factor User Authentication for Smartphones
Publisher: Association for Computing Machinery (ACM)
Date: 06-09-2022
DOI: 10.1145/3550292
Abstract: Pattern lock-based authentication has been widely adopted in modern smartphones. However, this scheme relies essentially on passwords, making it vulnerable to various side-channel attacks such as the smudge attack and the shoulder-surfing attack. In this paper, we propose a second-factor authentication system named SwipePass, which authenticates a smartphone user by examining the distinct physiological and behavioral characteristics embedded in the user's pattern lock process. By emitting and receiving modulated audio using the built-in modules of the smartphone, SwipePass can sense the entire unlocking process and extract discriminative features to authenticate the user from the signal variations associated with hand dynamics. Moreover, to alleviate the burden of data collection in the user enrollment phase, we conduct an in-depth analysis of users' behaviors under different conditions and propose two augmentation techniques to significantly improve identification accuracy even when only a few training s les are available. Finally, we design a robust authentication model based on CNN-LSTM and One-Class SVM for user identification and spoofer detection. We implement SwipePass on three off-the-shelf smartphones and conduct extensive evaluations in different real-world scenarios. Experiments involving 36 participants show that SwipePass achieves an average identification accuracy of 96.8% while maintaining a false accept rate below 0.45% against various attacks.
Supporting pervasive computing applications with active context fusion and semantic context delivery
Publisher: Elsevier BV
Date: 02-2010
Object relevance weight pattern mining for activity recognition and segmentation
Publisher: Elsevier BV
Date: 02-2010
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
Publisher: IEEE
Date: 03-2018
Your Eyes Reveal Your Secrets: An Eye Movement Based Password Inference on Smartphone
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
mmFER: Millimetre-wave Radar based Facial Expression Recognition for Multimedia IoT Applications
Publisher: ACM
Date: 02-10-2023
Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage
Publisher: MDPI AG
Date: 09-01-2019
DOI: 10.3390/S19020225
Abstract: Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently ide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.
Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing
Publisher: IEEE
Date: 2009
LoEar
Publisher: Association for Computing Machinery (ACM)
Date: 06-09-2022
DOI: 10.1145/3550293
Abstract: Acoustic sensing has been explored in numerous applications leveraging the wide deployment of acoustic-enabled devices. However, most of the existing acoustic sensing systems work in a very short range only due to fast attenuation of ultrasonic signals, hindering their real-world deployment. In this paper, we present a novel acoustic sensing system using only a single microphone and speaker, named LoEar, to detect vital signs (respiration and heartbeat) with a significantly increased sensing range. We first develop a model, namely Carrierforming, to enhance the signal-to-noise ratio (SNR) via coherent superposition across multiple subcarriers on the target path. We then propose a novel technique called Continuous-MUSIC (Continuous-MUltiple SIgnal Classification) to detect a dynamic reflections, containing subtle motion, and further identify the target user based on the frequency distribution to enable Carrierforming. Finally, we adopt an adaptive Infinite Impulse Response (IIR) comb notch filter to recover the heartbeat pattern from the Channel Frequency Response (CFR) measurements which are dominated by respiration and further develop a peak-based scheme to estimate respiration rate and heart rate. We conduct extensive experiments to evaluate our system, and results show that our system outperforms the state-of-the-art using commercial devices, i.e., the range of respiration sensing is increased from 2 m to 7 m, and the range of heartbeat sensing is increased from 1.2 m to 6.5 m.
Schema matching for context-aware computing
Publisher: ACM
Date: 21-09-2008
SpiderWalk
Publisher: Association for Computing Machinery (ACM)
Date: 26-03-2018
DOI: 10.1145/3191774
Abstract: This paper presents the design and implementation of the SpiderWalk system for circumstance-aware transportation activity detection using a novel contact vibration sensor. Different from existing systems that only report the type of activity, our system detects not only the activity but also its circumstances (e.g., road surface, vehicle, and shoe types) to provide better support for applications such as activity logging, location tracking, and smart persuasive applications. Inspired by but different from existing audio-based context detection approaches using microphones, the SpiderWalk system is designed and implemented using an ultra-sensitive, flexible contact vibration sensor which mimics the spiders' sensory slit organs. By sensing vibration patterns from the soles of shoes, the system can accurately detect transportation activities with rich circumstance information while resisting undesirable external signals from other sources or speech that may cause the data assignment and privacy preserving issues. Moreover, our system is implemented by reusing existing audio devices and can be used by an unmodified smartphone, making it ready for large-scale deployments. Finally, a novel temporal and spatial correlated classification approach is proposed to accurately detect the complex combinations of transportation activities and circumstances based on the output of each in idual classifiers. Experiments conducted on a real-world data set suggest our system can accurately detect different transportation activities and their circumstances with an average detection accuracy of 93.8% with resource overheads comparable to existing audio- and GPS-based systems.
LiteNap: Downclocking LoRa Reception
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
Publisher: ACM
Date: 07-11-2017
DF-Sense: Multi-user Acoustic Sensing for Heartbeat Monitoring with Dualforming
Publisher: ACM
Date: 18-06-2023
ReLog: A systematic approach for supporting efficient reprogramming in wireless sensor networks
Publisher: Elsevier BV
Date: 04-2017
Compressive representation for device-free activity recognition with passive RFID signal strength
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Accurate and Generic Sender Selection for Bulk Data Dissemination in Low-Power Wireless Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
DiverSense
Publisher: Association for Computing Machinery (ACM)
Date: 04-07-2022
DOI: 10.1145/3536393
Abstract: The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal ersity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency ersity. To guarantee the ersity gain after signal combining, we also propose a time- ersity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in erse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters, which is a 3x improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g., DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors.
MultiResp: Robust Respiration Monitoring for Multiple Users using Acoustic Signal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
GazeRevealer: Inferring Password Using Smartphone Front Camera
Publisher: ACM
Date: 05-11-2018
HeartPrint: Exploring a Heartbeat-Based Multiuser Authentication With Single mmWave Radar
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-12-2022
A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
FTrack: Infrastructure-free floor localization via mobile phone sensing
Publisher: IEEE
Date: 03-2012
An Adaptive Low-Power Listening Protocol for Wireless Sensor Networks in Noisy Environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
AirContour
Publisher: Association for Computing Machinery (ACM)
Date: 17-10-2019
DOI: 10.1145/3343855
Abstract: Recognizing in-air hand gestures will benefit a wide range of applications such as sign-language recognition, remote control with hand gestures, and “writing” in the air as a new way of text input. This article presents AirContour, which focuses on in-air writing gesture recognition with a wrist-worn device. We propose a novel contour-based gesture model that converts human gestures to contours in 3D space and then recognizes the contours as characters. Different from 2D contours, the 3D contours may have the problems such as contour distortion caused by different viewing angles, contour difference caused by different writing directions, and the contour distribution across different planes. To address the above problem, we introduce Principal Component Analysis (PCA) to detect the principal/writing plane in 3D space, and then tune the projected 2D contour in the principal plane through reversing, rotating, and normalizing operations, to make the 2D contour in right orientation and normalized size under a uniform view. After that, we propose both an online approach, AC-Vec, and an offline approach, AC-CNN, for character recognition. The experimental results show that AC-Vec achieves an accuracy of 91.6% and AC-CNN achieves an accuracy of 94.3% for gesture/character recognition, both outperforming the existing approaches.
MindID:Person Identification from Brain Waves through Attention-based Recurrent Neural Network
Publisher: Association for Computing Machinery (ACM)
Date: 18-09-2018
DOI: 10.1145/3264959
Abstract: Person identification technology recognizes in iduals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person identification systems have been shown to be vulnerable, e.g., anti-surveillance prosthetic masks can thwart face recognition, contact lenses can trick iris recognition, vocoder can compromise voice identification and fingerprint films can deceive fingerprint sensors. EEG (Electroencephalography)-based identification, which utilizes the user's brainwave signals for identification and offers a more resilient solution, has recently drawn a lot of attention. However, the state-of-the-art systems cannot achieve similar accuracy as the aforementioned methods. We propose MindID, an EEG-based biometric identification approach, with the aim of achieving high accuracy and robust performance. At first, the EEG data patterns are analyzed and the results show that the Delta pattern contains the most distinctive information for user identification. Next, the decomposed Delta signals are fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural Networks) structure which assigns varying attention weights to different EEG channels based on their importance. The discriminative representations learned from the attention-based RNN are used to identify the user through a boosting classifier. The proposed approach is evaluated over 3 datasets (two local and one public). One local dataset (EID-M) is used for performance assessment and the results illustrate that our model achieves an accuracy of 0.982 and significantly outperforms the state-of-the-art and relevant baselines. The second local dataset (EID-S) and a public dataset (EEG-S) are utilized to demonstrate the robustness and adaptability, respectively. The results indicate that the proposed approach has the potential to be widely deployed in practical settings.
FullBreathe
Publisher: Association for Computing Machinery (ACM)
Date: 18-09-2018
DOI: 10.1145/3264958
Abstract: Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the litude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new " litude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.
A Novel Metric for Opportunistic Routing in Heterogenous Duty-Cycled Wireless Sensor Networks
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/ICNP.2015.10
Towards Accurate Corruption Estimation in ZigBee Under Cross-Technology Interference
Publisher: IEEE
Date: 06-2017
MDLdroidLite: A Release-and-Inhibit Control Approach to Resource-Efficient Deep Neural Networks on Mobile Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array
Publisher: ACM
Date: 2015
Season: Shelving interference and joint identification in large-scale RFID systems
Publisher: IEEE
Date: 04-2011
FinDroidHR
Publisher: Association for Computing Machinery (ACM)
Date: 26-03-2018
DOI: 10.1145/3191788
Abstract: We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.
Human respiration detection with commodity wifi devices
Publisher: ACM
Date: 12-09-2016
Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads
Publisher: Association for Computing Machinery (ACM)
Date: 21-06-2021
DOI: 10.1145/3442363
Abstract: Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.
Scalable floor localization using barometer on smartphone
Publisher: Wiley
Date: 04-07-2016
DOI: 10.1002/WCM.2706
A Novel Privacy Preserving Search Technique for Stego Data in Untrusted Cloud
Publisher: Hawaii International Conference on System Sciences
Date: 2019
Traveling Officer Problem: Managing Car Parking Violations Efficiently Using Sensor Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
City-Scale Localization with Telco Big Data
Publisher: ACM
Date: 24-10-2016
Towards energy-balanced data transmission for lifetime optimization in wireless sensor networks
Publisher: IEEE
Date: 05-2016
Embracing Corruption Burstiness: Fast Error Recovery for ZigBee under Wi-Fi Interference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Modeling link correlation in low-power wireless networks
Publisher: No publisher found
Date: 2015
AudioGuard: Omnidirectional Indoor Intrusion Detection Using Audio Device
Publisher: Association for Computing Machinery (ACM)
Date: 27-09-2023
DOI: 10.1145/3625305
SELoc: Collect Your Location Data Using Only a Barometer Sensor
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags
Publisher: ACM
Date: 2015
Making Sense of Doppler Effect for Multi-Modal Hand Motion Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Interpolating the missing values for multi-dimensional spatial-temporal sensor data
Publisher: No publisher found
Date: 2017
A hierarchical approach to real-time activity recognition in body sensor networks
Publisher: Elsevier BV
Date: 02-2012
An RFID and Computer Vision Fusion System for Book Inventory using Mobile Robot
Publisher: IEEE
Date: 02-05-2022
L-MAC: A wake-up time self-learning MAC protocol for wireless sensor networks
Publisher: Elsevier BV
Date: 08-2016
An Audio-based Hierarchical Smoking Behavior Detection System Based on A Smart Neckband Platform
Publisher: ACM
Date: 28-11-2016
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Publisher: IEEE
Date: 07-2018
A Pattern Mining Approach to Sensor-Based Human Activity Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition
Publisher: IEEE
Date: 03-2009
Spatial Multiplexing for Non-Line-of-Sight Light-to-Camera Communications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
FTrack: Parallel Decoding for LoRa Transmissions
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Context-aware middleware for pervasive elderly homecare
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2009
A Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns
Publisher: Springer International Publishing
Date: 2014
A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
LiteNap: Downclocking LoRa Reception
Publisher: IEEE
Date: 07-2020
Toward a Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Direction-Aware, Audio-Based Pedestrian Relative Positioning by Swing Induced Doppler Shift
Publisher: ACM
Date: 07-11-2017
FingerDraw
Publisher: Association for Computing Machinery (ACM)
Date: 18-03-2020
DOI: 10.1145/3380981
Abstract: This paper explores the possibility of tracking finger drawings in the air leveraging WiFi signals from commodity devices. Prior solutions typically require user to hold a wireless transmitter, or need proprietary wireless hardware. They can only recognize a small set of pre-defined hand gestures. This paper introduces FingerDraw, the first sub-wavelength level finger motion tracking system using commodity WiFi devices, without attaching any sensor to finger. FingerDraw can reconstruct finger drawing trajectory such as digits, alphabets, and symbols with the setting of one WiFi transmitter and two WiFi receivers. It uses a two-antenna receiver to sense the sub-wavelength scale displacement of finger motion in each direction. The theoretical underpinning of FingerDraw is our proposed CSI-quotient model, which uses the channel quotient between two antennas of the receiver to cancel out the noise in CSI litude and the random offsets in CSI phase, and quantifies the correlation between CSI value dynamics and object displacement. This channel quotient is sensitive to and enables us to detect small changes in In-phase and Quadrature parts of channel state information due to finger movement. Our experimental results show that the overall median tracking accuracy is 1.27 cm, and the recognition of drawing ten digits in the air achieves an average accuracy of over 93.0%.
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