Ear-worn devices (earables) are increasingly central to smart system interactions involving privacy-sensitive data, yet secure user authentication on these devices remains a challenge. Existing methods often depend on auxiliary sensors like in-ear microphones or accelerometers, which are absent in many commercial earables. This paper introduces a novel biometric approach leveraging natural head gestures. We observe that head gestures generate unique air friction patterns detectable by out-ear microphones, producing sonic signatures shaped by the head and neck's musculoskeletal dynamics. These signatures serve as a robust basis for earable authentication. We propose HMPrint, a system that captures air-friction-induced sonic effects (AFiSe) from head gestures via outear microphones for authentication. HMPrint incorporates advanced spectral analysis, synthetic data generation using variational autoencoders, and a contrastive continual learning framework to enhance robustness against inconsistent wearing postures, varied movement patterns, and environmental noise. A proof-of-concept prototype was tested with 30 participants and 15 commercial earable models across diverse conditions. Results show that HMPrint achieves high authentication accuracy (97.49% recall), a low FAR (2.34%), and strong resistance to spoofing (98% success rate).

Speaker diarization aims to identify "who spoke when" in multi-person conversational scenarios. State-of-the-art audio-only diarization methods divide the task into multi-stages of speech segmentation, neural speaker embedding and unsupervised clustering. Egocentric speaker diarization (i.e., diarization in egocentric videos) is characterized by natural conversational scenarios involving a variety of noisy backgrounds, changing sound levels and overlapping speech. These cause difficulty in predicting the number of speakers from audio input, and heavily influence the performance of audio-based clustering. Although audio-visual modeling has been studied recently to enhance speaker diarization, unreliable visual information in egocentric videos may even degrade the performance. In this work, we propose a unified audio-visual diarization framework by incorporating visual guidance into the audio-only diarization pipeline. In addition, we also propose an adaptive speech re-detection strategy to detect and assign speaker identity to the mistakenly undetected audio segments. Experiments on the Ego4D dataset show that our method achieves state-of-the-art diarization performance in challenging egocentric scenarios. The code and model weights are available at https://github.com/YellowRiver2001/EgoDiarization.

Generative agents, capable of simulating human behavior and collaborating on complex tasks, have the potential to revolutionize the investigation of traffic accidents. In this study, we designed TAA (Traffic Accident Agents), an advanced framework based on extended large language models (LLMs) to digitally model urban accident handling procedures and stakeholder interactions. TAA formalizes the roles, responsibilities, and interactions of all stakeholders through natural language encoding, utilizing this knowledge base to orchestrate its execution workflows. Facilitates trusted agent interactions, generates comprehensive reports, and employs memory mechanisms to plan and optimize subsequent actions. We evaluated TAA performance across multiple versions of ChatGPT, focusing on its capabilities to generate reliable interactions, make context-sensitive decisions, maintain extended dialogues, and produce accurate reports in streamlined accident resolution scenarios. Our analysis included evaluations of token consumption and economic costs to ensure scalability and practicality, with TAA achieving 87.9% effectiveness on the GPT 4omini benchmark. Experimental results demonstrate TAA’s successful execution of urban accident handling workflows with maintained informational consistency. The framework shows broad applicability to accident investigation, reconstruction, and archival documentation. This work pioneers the use of generative agents as collaborative human proxies, offering a transformative pathway to advance the future of traffic accident management and investigation.

Recent years, point-of-sale (POS) terminals are no longer limited to wired connections, with many relying on Wi-Fi for data transmission. Although Wi-Fi offers the convenience of wireless connectivity, it introduces significant security vulnerabilities. This work presents a non-intrusive method for eavesdropping POS passwords via Wi-Fi sensing, named${\mathsf {BeamThief}}$. Instead of conventional Wi-Fi Channel State Information (CSI) readings, our approach employs Wi-Fi Beamforming Feedback Information (BFI) for an eavesdropping attack. Compared to CSI, which can only be extracted through intruding into the Access Point (AP) or from a limited selection of commercial Wi-Fi cards (e.g., Intel-5300), BFI readings can be more readily obtained from a broad array of commercial Wi-Fi devices. A key technological contribution of${\mathsf {BeamThief}}$is the development of an analysis model for predicting finger motion trajectories. This model is based on the physical relationship between BFI readings and finger motion, thus eliminating the need for extensive labeled training data. Furthermore, we employ Maximum Ratio Combining (MRC) to enhance the BFI series, ensuring performance across various scenarios. We implement${\mathsf {BeamThief}}$using everyday commercial Wi-Fi devices and conduct a series of experiments to assess the impact of this attack. Experimental results demonstrate that${\mathsf {BeamThief}}$achieves an accuracy rate 79$\%$in inferring 6-digit POS passwords within the top-100 attempts.

The escalating threat of hidden GPS tracking devices poses significant risks to personal privacy and security.Featured by their miniaturization and misleading appearances, GPS devices can be easily disguised in their surroundings making their detection extremely challenging.In this paper, we propose a novel side-channel-driven detection system, GPSBuster, leveraging electromagnetic radiation (EMR) emitted by GPS trackers.Our feasibility studies and hardware analysis reveal that unique EMR patterns associated with the tracker's operation, stemming from the quartz oscillator, local oscillator, and mixer in the Mixed-Signal on Chip (MSoC) system.Nevertheless, as a side-channel leakage, EMRs can be extremely weak and suffer from the ambient noise interference, rendering the detection impractical.To address these challenges, we develop the signal processing techniques with noise removals and a dual-dimensional folding mechanism to accumulate the spectrum energy and protrude the EMR patterns with high Signal-to-Noise Ratios (SNR).Our detection prototype, built with a portable HackRF One device, allows users to perform a scan-to-detect manner and achieves an overall success rate of 98.4% on top-10 selling GPS trackers under various testing cases.The maximum detection range is 0.61m.

Nowadays, point-of-sale (POS) terminals are no longer limited to wired connections, and many of them rely on Wi-Fi for data transmission. While Wi-Fi provides the convenience of wireless connectivity, it also introduces significant security risks. Previous research has explored Wi-Fi-based eavesdropping methods. However, these methods often rely on limited environmental robustness of Channel State Information (CSI) and require invasive Wi-Fi hardware, making them impractical in real-world scenarios. In this work, we present SThief, a practical Wi-Fi-based eavesdropping attack that leverages beamforming feedback information (BFI) exchanged between POS terminal and access points (APs) to keystroke inference on POS keypads. By capitalizing on the clear-text transmission characteristics of BFI, this attack demonstrates a more flexible and practical nature, surpassing traditional CSI-based methods. BFI is transmitted in the uplink, carrying downlink channel information that allows the AP to adjust beamforming angles. We exploit this channel information to keystroke inference. To enhance the BFI series, we use maximal ratio combining (MRC), ensuring efficiency across various scenarios. Additionally, we employ the Connectionist Temporal Classification method for keystroke inference, providing exceptional generalization and scalability. Extensive testing validates SThief’s effectiveness, achieving an impressive 81% accuracy rate in inferring 6-digit POS passwords within the top-100 attempts.

The Domain Name System (DNS) runs as a fundamental infrastructure of the mobile Internet. Various DNS protocols employed in the current network ecology can be predominantly classified as unencrypted DNS and encrypted DNS. However, existing research mainly focuses on assessing DNS performance within conventional internet structures, neglecting their evaluation in mobile contexts. In our pioneering study examining DNS within mobile networks, we developed an Android-based application to evaluate the efficiency and reliability of DNS protocols. The App issues nine domain name lookups to four cloud DNS providers supporting unencrypted and encrypted DNS protocols. Collaborating with volunteers from four countries, we collected about 52,000 test records. Our findings reveal substantial variability in the efficiency and reliability of all DNS protocols across different mobile scenarios. Overall, encrypted DNS protocols exhibit superior efficiency compared to plaintext DNS when oriented towards cloud DNS resolvers. In high-speed mobile scenarios, all DNS protocols demonstrate reduced efficiency, with encrypted DNS protocols showing relatively higher reliability. Our broad-scale measurement results indicate that the performance of DNS protocols varies across mobile contexts, but users are typically uninformed about these differences and do not realize how to break free. Intending to assist users in selecting an optimal DNS protocol, we propose a protocol choice model based on auto-encoding LSTM networks which leverages features of networking and protocols to predict the most suitable DNS protocol with reduced query time and enhanced reliability in the current scenario. Notably, we have achieved the prediction of the optimal DNS protocol for the future by foreseeing the network status ahead. Empirical results demonstrate an impressive 98.73% accuracy in prediction of DNS protocol selection.

Self-service libraries need self-service book collection and monitoring of book quality to improve user experience This article proposes a privacy-preserving alternative RFbook, a book classification and moisture sensing system formed from an array of passive commercial RFID tags. We have three key observations in designing RFbook for such benefits. The first observation is that when tags are in the vicinity, their interrogation currents can alter each other’s circuit properties, based on which unique phase and amplitude signatures can be obtained from the backscattered signal. The second observation is that books with different thicknesses and sizes of material will have different signal features. Finally, we found that changes in book humidity are reflected in the reader’s received signal strength (RSS). To turn the high-level idea into a practical system, we built a prototype of RFbook and conducted comprehensive experiments to evaluate the system’s performance. The experimental results show that RFbook can distinguish different types of books with an average accuracy rate higher than 96% and monitor the humidity change of the book.

Password, fingerprint and face recognition are the most popular authentication schemes on smartphones. However, these user authentication schemes are threatened by shoulder surfing attacks and spoof attacks. In response to these challenges, eye movements have been utilized to secure user authentication since their concealment and dynamics can reduce the risk of suffering those attacks. However, existing approaches based on eye movements often rely on additional hardware (such as high-resolution eye trackers) or involve a time-consuming authentication process, limiting their practicality for smartphones. This paper presents DEyeAuth, a novel dual-authentication system that overcomes these limitations by integrating eyelid patterns with eye gestures for secure and convenient user authentication on smartphones. DEyeAuth first leverages the unique characteristics of eyelid patterns extracted from the upper eyelid margins or creases to distinguish different users and then utilizes four eye gestures (i.e., looking up, down, left, and right) whose dynamism and randomness can counter threats from image and video spoofing to enhance system security. To the best of our knowledge, we are among the first to discover and prove that the upper eyelid margins and creases can be used as potential biometrics for user authentication. We have implemented the prototype of DEyeAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results indicate that DEyeAuth achieves a high authentication accuracy of 99.38% with a relatively short authentication time of 6.2 seconds, and is effective in resisting image presentation, video replaying, and mimic attacks.

Modern smartphones hold massive amounts of private and potentially sensitive user data (e.g., identity and messages). User authentication is the key measure to protect such sensitive data from adversaries. In this article, we explore a novel authentication mechanism, LipAuth, leveraging the unique spatial-temporal features (i.e., both static physiological and dynamic behavioral characteristics) of human lips biometrics for secure and convenient user authentication, without requiring any special sensors on smartphones. The key principle behind LipAuth is that the geometric structure of lips is unique across different users while consistent and stable for the same user, which is dependent on three types of static features, i.e., lip width, thicknesses, and the joint characteristic of the former two, and the dynamic features in smiling process, i.e., the bending processes of the boundary lines between the upper and lower lips. On that basis, LipAuth can accurately identify legal users by actively extracting the spatial-temporal features on the lips’ profile changes, while also remaining fast and easy to use. We have implemented the prototype of LipAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results show that LipAuth can achieve an overall 99.24% accuracy for user authentication and can resist potential intrusion from video replaying and mimic attacks.

Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).

The increasing misuse of commercial drones for illicit activities poses significant challenges in their detection and identification. Existing methods, such as acoustic-based, radio frequency-based, and computer vision approaches, face limitations due to factors like miniaturization, stealth, and background noise. In this paper, we propose E-Argus, a system that leverages the electromagnetic radiation (EMR) emitted by the memory of drones. It is a basic fact that, with all types of drones, the implementation of arbitrary behavior must be digested in the built-in memory, and electromagnetic radiation is thus generated. Specifically, the memory clock drives the switching regulator causing current fluctuations that generate EMR signals at the clock frequency. E-Argus combines the relationship between the flight pattern of the drone and the memory EMR signal, analyzes the unique side-channel signatures, and utilizes advanced neural network-based identification; E-Argus can accurately detect and identify various types of illegal drones. We designed a system prototype based on USRP B210 and conducted experiments in a wide range of scenarios. The evaluation shows that E-Argus has low latency, high accuracy, and robustness in real environments.

Door lock is regarded as a critical line of defending the privacy and security of personal areas. However, for inner doors in environments like factories, existing locking mechanisms can be poor in user-friendliness and high in cost. For instance, mechanical locks require carrying keys that inevitably compromise user experiences, while smart locks always require non-trivial sensors. Therefore, inner doors urgently require a lightweight unlocking scheme that can properly balance user-friendliness, cost, and security. To this end, we propose HandKey as a keyless unlocking scheme to supplement existing lock systems. HandKey relies on two principles: the simplicity of hand knocking doors and the uniqueness of vibration triggered by the knocking force. In other words, a door and a hand knocking it jointly form a unique physical system that generates hand-dependent and user-specific vibration signatures uniquely representing a user identity. In designing HandKey, we first analyze the vibration mechanism behind it and the impacts of gestures and door materials on vibration signatures. Then we innovatively construct a signal processing and deep learning-based pipeline to extract signatures robust to variable knocking behaviors for representing user identity. Finally, we implement a HandKey prototype and use extensive evaluation to demonstrate its security and effectiveness.

User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short ofimplementabilitythanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices void of text entry interfaces. Biometrics may not scale well as they require both non-trivial sensors and cumbersome user involvement. Proximity-based methods exploiting shared ambient contexts are vulnerable to co-located malicious attacks. Therefore, a low-cost authentication scheme widely implementable on heterogeneous IoT devices is urgently demanded. To this end, we proposeMagSignthat leverages two fundamental capabilities owned by common IoT devices: the ubiquity of magnetic induction sensors and the power of screens to change magnetic field. Essentially, MagSign controls screen contents of an authorized device (possessed by a user) to generate specific currents in its electronic components that in turn induce a magnetic signature. This signature, sensed by a nearby device, allows the user to be authenticated and hence to unlock that device. In designing MagSign, we explore critical parameters employable to magnetic signature generation by analyzing electronic components’ workflow. Moreover, we innovatively encode binary sequences into magnetic intensity transitions, so that a sequence issued from a trusted server can be converted into a magnetic signature. Different from existing proximity-based approaches relying on shared static environment information, magnetic signature is directly derived from a server-issued sequence, allowing for dynamic signature generation that effectively thwarts potential attacks. The comprehensive experiments show MagSign has a false acceptance rate (FAR) of 0.38% and a false rejection rate (FRR) of 3.13%.

Head motion tracking is a promising research field with vast applications in ubiquitous human-computer interaction (HCI) scenarios. Unfortunately, solutions based on vision and wireless sensing have shortcomings in user privacy and tracking range, respectively. To address these issues, we propose IA-Track, a novel head motion tracking system that combines inertial measurement units (IMU) and acoustic sensing. Our wireless earphone-based method balances flexibility, computational complexity, and tracking accuracy, requiring only an earphone with an IMU and a smartphone. However, we still face two challenges. First, wireless earphones have limited hardware resources, making acoustic Doppler effect-based method unsuitable for acoustic tracking. Second, traditional Kalman filter-based trajectory restoration methods may introduce significant cumulative errors. To tackle these challenges, we rely on IMU sensor data to recover the trajectory and use smartphones to emit ”inaudible” acoustic signals that the earphone receives to adjust the IMU drift track. We conducted extensive experiments involving 50 volunteers in various potential IA-Track usage scenarios, demonstrating that our well-designed system achieves satisfactory head motion tracking performance.

Blink detection is essential for various human-computer interaction scenarios, such as virtual reality and driving state detection. It has gained significant attention from industry and academia alike in recent years. Existing non-contact detection systems (cameras, acoustics, etc.) have made significant progress, but various issues have prevented their widespread adoption, including privacy concerns, line-of-sight requirements, and cost issues. Therefore, there is a critical need for a simple and robust system that can detect eye blinks using common commercial equipment. In this paper, we propose BlinkRadar, which uses a low-cost customized impulse-radio ultra- wideband (IR-UWB) radar for non-contact and fine-grained blink detection. BlinkRadar can reliably detect driver blinks in driving conditions, making it possible to infer drowsy driving. To effectively extract the eye blink signal, we analyzed real experimental data to study the characteristics of the eye blink pattern and successfully used the multi-sequence variational mode decomposition (MS-VMD) algorithm to separate the blink signal from the noise signal. We conducted extensive experiments in two different environments (a quiet room and moving vehicles) and found that BlinkRadar had an average blink detection accuracy of over 96.2%. Our results demonstrate the feasibility of using UWB radar for non-contact eye blink detection.

Keyless entry systems (KES) have become popular due to their high user-friendliness, while fingerprint and digital password authentication are two of the most widely used unlocking ways. However, current KES are vulnerable to security threats, such as fingerprint films that deceive fingerprint sensors and stolen passcodes. To address these issues, this paper presents${\sf Fingerbeat}$, a two-factor authentication system to defend the security risks of the current widely deployed KES devices.${\sf Fingerbeat}$combines original credentials, such as fingerprints and passcodes, with unique and persistent finger-induced vibrations to create a two-factor secure authentication model, while ensuring user-friendliness.${\sf Fingerbeat}$leverages the fact that each person's finger structure is distinct and can be represented in distinct vibration patterns. During authentication, FIV is triggered and embodied in the mechanical vibration of the force-bearing body (i.e., KES panel), which can be captured by a low-cost accelerometer. We develop a proof-of-concept prototype of${\sf Fingerbeat}$, extracting FIV features from mixed vibration recordings and eliminating the impacts of variable behaviors and external disturbance. Finally, we conduct extensive experiments to demonstrate its security and effectiveness.

With the popularity of Mobility-on-Demand (MOD) vehicles, a new market called MOD-Vehicular-Crowdsensing (MOVE-CS) was introduced for drivers to earn more by collecting road data. Unfortunately, MOVE-CS failed after two years of operation. To identify the root cause, we survey 581 drivers and reveal its simple incentive model based on blindly competitive rewards. This model brings most drivers few yields, resulting in their withdrawals. In contrast, a similar market termed MOD-Human-Crowdsensing (MOMAN-CS) remains successful thanks to a complex model based on exclusively customized rewards. Hence, we wonder whether MOVE-CS can be resurrected by learning from MOMAN-CS. Despite considerable similarity, we can hardly apply the incentive model of MOMAN-CS to MOVE-CS, since MOD drivers are also concerned with passenger missions that dominate their earnings. To this end, we analyze a large-scale dataset of 12,493 MOD vehicles, finding that drivers have explicit preference for short-term, immediate gains as well as implicit rationality in pursuit of long-term, stable profits. Therefore, we design a novel driver-oriented incentive mechanism for MOVE-CS, calledLSTAloc, at the heart of which lies a spatial-temporal differentiation-aware task allocation scheme empowered by submodular optimization. Applied to the dataset, our design would essentially benefit both the drivers and platform to incentivize MOD vehicular crowdsensing efficiently, thus possessing the potential to resurrect MOVE-CS.

Dynamic Adaptive Streaming over HTTP (DASH) stands as a leading streaming technology embraced by major video platforms and smart TV manufacturers worldwide. Despite its widespread use, the inherent diversity in both the video content and the client devices poses challenges, hindering DASH from consistently delivering top-notch playback quality for all users. This oversight often leads to network congestion, compromising the playback quality for users. To tackle these issues, we propose a Two-stage Stackelberg Game (TSBG) algorithm for personalized video streaming transmission in Edge Computing (EC) environments. The TSBG algorithm aims to optimize the Quality of Experience (QoE) of users by tailoring video streaming services between EC servers and clients. Initially, we establish the system model and define the video stream transmission problem as a multi-objective optimization problem, balancing server downlink resource scheduling and client adaptive bit rate. Subsequently, we design the TSBG algorithm, where an edge server allocation mechanism is adopted in the first stage to maximize overall user QoE, while users adjust their video bit rates based on the edge server's distribution plan to enhance their individual QoE in the second stage. We prove the existence and uniqueness of the equilibrium solution of the two-stage Starkelberg game and design an optimal pricing algorithm to maximize the benefits of edge servers. Extensive simulation experiments validate the effectiveness of the TSBG algorithm, showcasing its superiority in achieving enhanced QoE, network efficiency, and fairness compared to alternative approaches.

Android system has been widely deployed in energy-constrained IoT devices for many practical applications, such as smart phone, smart home, healthcare, fitness, and beacons. However, Android users oftentimes suffer from app crashes, which directly disrupt user experience and could lead to data loss. Till now, the community have limited understanding of their prevalence, characteristics, and root causes. In this article, we make an in-depth study of the crash events regarding ten very popular apps of different genres, based on fine-grained system-level traces crowd-sourced from 93 million Android devices. We find that app crashes occur prevalently on the various hardware models studied, and better hardware does not seem to essentially relieve the problem. Most importantly, we unravel multi-fold root causes of app crashes, and pinpoint that the most crashes stem from the subtle yet crucial inconsistency between app developers’ supposed memory/process management model and Android’s actual implementations. We design practical approaches to addressing the inconsistency; after large-scale deployment, they reduce 40.4% of the app crashes with negligible system overhead. In addition, we summarize important lessons learned from this study, and have released our measurement code/data to the community.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

The popularity of IoT devices has penetrated our daily life while posing new challenges in user authentication. Today’s solutions, e.g., passwords, fingerprints, and FaceIDs, primarily rely on specialized sensors or user interfaces to collect user’s identification information, which may not universally exist on heterogeneous IoT devices. In this paper, we propose a novel user authentication method that exploits the Electromagnetic (EM) emanations radiated from IoT devices. Our design is motivated by the observation that human touches on the IoT device can lead to time-varying coupling between these two. Consequently, it impacts the device’s EM emanations that can be picked up by its inertial ADC (analog-to-digital converter) interfaces. We ask the user to tap on the device rhythmically following a self-determined melody, such that the human-coupled EM emanations vary accordingly. We thus extract the rhythm pattern as the user’s secure password named EM-Rhythm . To examine its effectiveness, EM-Rhythm is implemented on a wide range of IoT devices. We show that our scheme achieves authentication accuracy as high as 98.67% with less than three login attempts. Besides, it is robust against various types of attacks and maintains stable performances under various settings. EM-Rhythm also exhibits satisfactory usability in terms of memorability and time consumption.

Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to their difficulty cooperating and the caregiver's lack of medical knowledge. To this end, we propose EarSonar, a novel acoustic-based MEE diagnostic system. The principle behind EarSonar is that the acoustic absorption effect exists in ear scenarios, and the volume of middle ear fluid can markedly affect the absorbed spectrum energy. By automatically eliminating the impact of potential interference factors and identifying the representative frequency range with the typical reaction of acoustic absorption, EarSonar captures fine-grained signal features on absorbed spectrum energy and models the intrinsic relationship between acoustic absorption and the volume of the filler fluid in the eardrum. On that basis, EarSonar extracts the features of the MEE signal segment and uses k-means clustering to classify middle ear effusion status. We conducted a test on 112 adolescents aged 4–6. We divided the degree of middle ear effusion into three grades. The final average detection accuracy rate exceeds 92%, which is 8 % higher than the previous method. We have implemented a proof-of-concept prototype of EarSonar by building upon earphones embedded with a microphone and speaker. Experimental results demonstrate a feasible and effective way to turn earphones into potential home-use MEE screening tools.

Voice assistants support contactless smart device control and thus act as a holy grail of human–computer interaction. However, recent studies reveal that an adversary can manipulate devices by vicious voice commands. This security risk is caused by only executing one-time liveness detection and lacking safeguard modules after service activation. Therefore, identifying speaker type (i.e., human articulators or loudspeakers) is critical in protecting voice-driven services during an entire interaction session. In this article, we propose a continuous voice liveness detection approach LiveProbe, leveraging unique energy response patterns in frequency bands induced by distinct voice generation mechanisms. The rationality behind LiveProbe is presented in two aspects: human articulator reshapes initial voices by exquisitely coordinated movements of vocal organs, which act as band-pass filters generating unique energy responses; nevertheless, the internal modules of loudspeakers are position fixed and cannot reproduce this response characteristic. To that end, we first work on voice generation mechanisms behind two-type speakers that cause spectrum differences. Then, we elaborately construct signal processing and deep-learning modules to extract liveness features. Especially, our approach does not interfere with normal voice interaction and need not to carry customized sensors. The experiment presents its effectiveness against potential attacks with a false acceptance rate of 0.51%.

Heart disease has now become a very common and impactful disease, which can actually be easily avoided if treatment is intervened at an early stage. Thus, daily monitoring of heart health has become increasingly important. Existing mobile heart monitoring systems are mainly based on seismocardiography (SCG) or photoplethysmography (PPG). However, these methods suffer from inconvenience and additional equipment requirements, preventing people from monitoring their hearts in any place at any time. Inspired by our observation of the correlation between pupil size and heart rate variability (HRV), we consider using the pupillary response when a user unlocks his/her phone using facial recognition to infer the user’s HRV during this time, thus enabling heart monitoring. To this end, we propose a computer vision-based mobile HRV monitoring framework-PupilHeart, designed with a mobile terminal and a server side. On the mobile terminal, PupilHeart collects pupil size change information from users when unlocking their phones through the front-facing camera. Then, the raw pupil size data is preprocessed on the server side. Specifically, PupilHeart uses a 1-D convolutional neural network (1-D CNN) to identify time series features associated with HRV. In addition, PupilHeart trains a recurrent neural network (RNN) with three hidden layers to model pupil and HRV. Employing this model, PupilHeart infers users’ HRV to obtain their heart condition each time they unlock their phones. We prototype PupilHeart and conduct both experiments and field studies to fully evaluate effectiveness of PupilHeart by recruiting 60 volunteers. The overall results show that PupilHeart can accurately predict the user’s HRV.

Authentication mechanisms deployed on access control systems undertake the responsibility of judging user identity to prevent unauthorized individuals from illegally approaching. In this article, we propose LIPAuth leveraging hand-dependent L ight I ntensity P attern to Auth enticate users. To be specific, lights released by a screen, are blocked and reflected by one hand above it; in this propagation process, hands exhibit user-specific ability in driving light absorption and attenuation due to owning unique structures, thereby outputting discriminative intensity patterns representing user identity. To implement LIPAuth , we first study the impact of screen contents on light intensity patterns, also explore the possibility of embedding hand structure biometrics into these patterns. We then design a customized dynamic stimulus-response mechanism for LIPAuth and make it resilient to the risks of potential registration profile leakage. Subsequently, we construct a joint pipeline consisting of signal processing and a learning-based generative adversarial network to overcome interference from variable user behaviors. More importantly, LIPAuth just utilizes common sensors to capture light signals, hence achieving low cost. We finally conduct extensive experiments in three scenarios to evaluate the authentication performance of LIPAuth prototype.

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

The eye-blink pattern is crucial for drowsy driving diagnostics, which has become an increasingly serious social issue. However, traditional methods (e.g., with EOG, camera, wearable, and acoustic sensors) are less applicable to real-life scenarios due to the disharmony between user-friendliness, monitoring accuracy, and privacy-preserving. In this work, we design and implement BlinkRadar as a low-cost and contact-free system to conduct fine-grained eye-blink monitoring in a driving situation using a customized impulse-radio ultra-wideband (IR-UWB) radar which has superior spatial resolution with the ultra-wide bandwidth. BlinkRadar leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. BlinkRadar aims to single out the eye-blink induced waveforms modulated by body movements and vehicle status. It solves the serious interference caused by the unique characteristics of blinking (i.e., subtle, sparse, and non-periodic) and from the human target itself and surrounding objects. We evaluate BlinkRadar in a laboratory environment and during actual road testing. Experimental results show that BlinkRadar can achieve a robust performance of drowsy driving with a median detection accuracy of 92.2% and eye blink detection of 95.5%.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context, and their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO, a novel user-Perception-based video Experience Optimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between device’s state of motion, BS factor, video bitrate and the resulting user-perceived quality. Specifically, by capturing the motion of on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.

Mobile crowdsensing systems typically operate centralized cloud storage management, and the environment data sensed by the participants are usually uploaded to certain central cloud servers. Instead, this article addresses the decentralized data storage problem in scenarios where cloud servers or network infrastructures do not work as expected and the sensing data have to be temporarily stored on the mobile devices carried by the participants. Considering that the sensing data are generally correlated, this article investigates a compressive distributed storage scheme for mobile crowdsensing. We notice a key observation: when a participant has a random walk in the target sensing area, his walking/sensing process can be considered as a random sampling for the entire area, although the activity of the participant may only have a local scope. We then propose an encoding algorithm based on compressive sensing theory. Each participant encodes the sensing data in their local trajectory, but the encoded CS measurement is capable of roughly reflecting the entire information of the whole area. While a participant stores a blurred global image of the target sensing area, the entire data can then be collaboratively stored by a certain number of participants. We further present a period-based data recovery algorithm to exploit the inter-period correlations, improving the recovery accuracy. Experimental results using real environmental data demonstrate the performance of the proposed compressive storage scheme. The test datasets and our source codes are available at https://github.com/siwangzhou/MCS-Storage .

Continuous user authentication is of great importance to maintain security for a mobile system and protect user's privacy throughout a login session. In this paper, we propose HandPass, a continuous user authentication system that employs the vibration responses of concealed hand biometrics, which are passively activated by the natural user-device interactions on the touchscreen. Hand vibration responses are instantly triggered and embodied in the mechanical vibration of the force-bearing body (i.e., the mobile device and the holding hand). Therefore, a built-in accelerometer can effectively capture the intrinsic features of hand vibration responses. The hand vibration response is determined by the trigger force and the complex hand structure, which is unique to each user and is difficult (if not impossible) to counterfeit. HandPass is a passive hand vibration response-based continuous user authentication system hosted on smartphones, with advantages of non-intrusiveness, high efficiency, and user-friendliness. We prototyped HandPass on Android smartphones and comprehensively evaluated its performance by recruiting 43 volunteers. Experiment results show that HandPass can achieve 97.3 % overall authentication accuracy and only 1.8 % false acceptance rate in diverse scenarios.

Modeling user preferences is a challenging problem in the wide application of recommendation services. Existing methods mainly exploit multiple activities irrelevant to user's inner feeling to build user preference model, which may raise model uncertainty and bring about prediction error. In this paper, we present PupilMeter - the first system that moves one step forward towards exploring the correlation between user preference and the instant pupillary response. Specifically, we conduct extensive experiments to dig into the generic physiological process of pupillary response while viewing specific content on smart devices, and further figure out six key time-series features relevant to users' preference degree by using Random Forest. However, the diversity of pupillary responses caused by inherent individual difference poses significant challenges to the generality of learned model. To solve this problem, we use Multilayer Perceptron to automatically train and adjust the importance of key features for each individual and then generate a personalized user preference model associated with user's pupillary response. We have prototyped PupilMeter and conducted both test experiments and in-the-wild studies to comprehensively evaluate the effectiveness of PupilMeter by recruiting 30 volunteers. Experimental results demonstrate that PupilMeter can accurately identify users' preference.

Enabling contactless and device-free hands tracking on mobile device leads to new user interaction experiences. In this article, we propose CTrack, a device-free and collaborative hands motion tracking solution for above-device interaction by using acoustic signals. CTrack does not require instrumenting hands with sensors. We achieve this by transforming the device into an active sonar system that transmits inaudible sound signals and tracks the echoes of the hand at its microphones. To guarantee subcentimeter-level tracking accuracies, we present an adaptive approach that uses the chirp’s time of flight to accurately measure the distance from the hand to an in-built speaker array. Then, the hand, speaker array, and microphone array yield a set of different ellipses. The hand position can be pinpointed exactly by solving and optimizing the intersection of these ellipses. Our evaluation shows that CTrack can achieve 2-D motion tracking with an average accuracy of 14 mm using the in-built microphones and speakers of a Nexus 6P.

Existing studies on indoor navigation often require such a pre-deployment as floor map, localization system and/or additional (customized) hardwares, or human motion traces, making them prohibitive when the situation deviates from these requirements (e.g., navigating a crowd of panicking people where no localization system or motion traces are available). The main observation inspiring our work without reliance on such pre-deployment is that when there are sufficient participants (e.g., a crowd of panicking people), the WiFi signatures collected by participants can serve as the fingerprints (referred to as location fingerprints) of their unknown locations. By computing relative positions of these location fingerprints we can connect them to form a global map. Such a map reflects the topology of the underlying walkable space and thus holds the potential of offering a navigation path for any intended users. Based on this observation, we design Fly-Navi, a crowdsourcing based indoor navigation system via on-the-fly map generation, and primarily designed for indoor environments with rectilinear and narrow corridors. Specifically, each participant uploads sensory data, and the server then generates a global map (on-the-fly map) through a series of operations such as local map generation, local map stitch and edge computation. On top of the global map, Fly-Navi computes a navigation path to the given destination and tracks the progress. We implement the prototype of Fly-Navi and our experiments show that Fly-Navi can quickly generate a correct global map with the 80-percentile of between-fingerprint distance error less than 3 meters, which is important for computing turning points of the map and hereon offering turn-by-turn instructions, and correctly navigate the intended users to their destinations.

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload's size is large, the concurrent broadcast performance is far from efficient due to the frequently unsatisfied capture effect. Intuitively, senders can send a short packet containing partial flooding payload to keep concurrent broadcast efficiency. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover the entire flooding payload from several received packets as soon as possible. Moreover, considering different channel states of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization is not easy. In this paper, we propose Chase++ a Fountain-code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of a certain part of the flooding payload's continuous loss. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, further encoded into many encoded payload blocks by Fountain-code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, the concurrent broadcast layer continuously transmits these packets. Receivers can recover the original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on Local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

The emerging of Generative Adversarial Networks (GANs) gives rise to a significant improvement in image generation. However, a controllable way of synthesizing images with specific characteristics still is a challenging issue. Many existing methods are not efficient enough that require additional information and pre-designed attributes, and are with much more human intervention. In this paper, we propose GAGAN, an extension method to the Generative Adversarial Network, that is the first work to generate specific images from a trained GAN model. To control the characteristics of images, a DNA pool of the trained GAN model is introduced and evolved by a genetic algorithm (GA). Then, with the DNA pool, GAGAN can generate the corresponding latent vector (DNA) of target images. Furthermore, GAGAN can synthesize images containing a single specific characteristic or multiple specific attributes (including AND and OR relation). Moreover, several fitness evaluation strategies are also proposed to make GAGAN flexible to control the target characteristics. Experiments on CelebA and MNIST are conducted, and results show that the proposed method is feasible and effective in specific image generation problem.

Vehicle state including location and motion information plays an important role in various applications such as Internet of Vehicles (IoV), autonomous cars, and driving safety monitoring. Achieving accurate vehicle state is a challenging task in those applications due to the noise disturbances. Recent studies suggest that noise is not generally Gaussian distributed and many physical environments can be handled more accurately as non-Gaussian rather than Gaussian model. Inspired by this observation, we strive to improve the vehicle state estimation by investigating the effects of that assumption when process and measurement noises are non-Gaussian distributed. Here, process noise represents the noise during the state information processing. To that end, we exploit the generalized error distribution (GED) to compute the non-Gaussian probability density during the vehicle state estimation. We then derive extensive theoretical analysis targeting to estimate the parameters such as the mean and the variance (or covariance matrix) related to both process and measurement noises and reduce the computational burden of the distribution. Further, we propose a non-Gaussian particle filter for vehicle state estimation (nGPF-VSE) algorithm wherein we utilize the genetic operator resampling (GOR) technique to enhance the efficiency of particle filter (PF) relying on the selection of the importance sampling distribution. To evaluate the performance of the proposed approach, we conduct numerical simulations on the popular system of state-space equations and a real experiment for estimating the vehicle state. The results from the numerical simulations, experimental data and the statistical evaluation confirm that nGPF-VSE outperforms existing methods in terms of vehicle state accuracy.

The energy efficiency and delivery robustness are two critical issues for low duty-cycled wireless sensor networks. The asynchronous receiver-initiated duty-cycling media access control (MAC) protocols have shown their effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this article, we present CD-MAC, an energy-efficient and robust contention-detectable mechanism for addressing the collision-catching problem in receiver-initiated MACs. By exploring the temporal diversity of the acknowledgments, a receiver recognizes the potential senders and subsequently polls individual senders one by one. On that basis, CD-MAC can successfully avoid packet collision even though multiple senders have data packets to transmit to the same receiver. We implement CD-MAC in TinyOS and evaluate its performance on an indoor testbed with single-hop and multi-hop network scenarios. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

Due to limited energy supply of Internet of Things (Zhao et al. 2018) (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Since the sleep schedules of nodes are unsynchronized, a sender has to repeatedly send frames to coordinate with its receiver or keep sleeping until the receiver’s wake-up time will come according to receiver’s sleep-wake schedule. In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource for duty-cycled IoT networks. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in opportunistic forwarding, potentially enhancing the overall performance of duty-cycled multi-hop networks. However, since the current channel contention mechanisms mainly focus on collision avoidance, it is too conservative to exploit concurrency. To address this problem, in this article, we propose COF to fully exploit the potential Concurrency for Opportunistic Forwarding in duty-cycled IoT networks. COF achieves concurrent transmission by: (i) measuring conditional link quality under the interference of on-going transmissions, and then (ii) further modeling the benefit of potential concurrency opportunities. According to the expected benefit of concurrency, COF decides whether or not to transmit in concurrent way. COF also adopts concurrency flag and signal features to avoid data collision caused by disordered concurrent transmissions and enhance the accuracy of conditional link quality estimation. COF can be easily integrated into the conventional unsynchronized and duty-cycled protocols. We have implemented COF and evaluated its performance on a 40-node testbed. The results show that COF can effectively exploit potential concurrency in opportunistic forwarding and COF outperforms the state-of-art protocols under diverse traffic load and network density.

Convolutional Neural Networks (CNNs) is capable of automatically learning feature representations, CNN-based recognition algorithm has been an alternative method for human activity recognition. Even though general convolution operation followed by pooling could expand the receptive fields for extracting features, it will bring about information loss in feature representation. Due to that dilated convolutions not only could expand receptive field exponentially without changing the size of field map or pooling, but it also will not cause information loss, hence, we propose D2CL, a novel deep learning framework for human activity recognition using multi-model wearable sensors. This framework consists of dilated convolutional neural networks and recurrent neural networks. At first, learning from previous works, we add a general convolutional layer to map inputs into a hidden space for improving the capability of nonlinear representations. Subsequently, a stacked dilated convolutional networks automatically learn feature representations for inter-sensors and intra-sensors from hidden space. Then, given these learned features, two RNNs are applied to model their latent temporal dependencies. Finally, a softmax classifier at the topmost layer is utilized to recognize activities. To evaluate the performance of D2CL on activity recognition, we select two open datasets OPPORTUNITY and PAMAP2 for training and testing. Results show that our proposed model achieves a higher classification performance than the state-of-the-art DeepConvLSTM.

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to quickly disseminate system parameters to adapt diverse network requirements. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the length of flooding payload is long, due to frequently unsatisfied capture effect construction, the performance of concurrent broadcast is far from efficient. Intuitively, senders can send short packet that contains partial flooding payload to keep the efficiency of concurrent broadcast. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover entire flooding payload from several received packets as soon as possible. Moreover, considering diverse channel state of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization in a light-weight way is not easy. In this paper, we propose Chase++ a Fountain code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of the continuous loss of a certain part of flooding payload. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, which are further encoded into many encoded payload blocks by Fountain code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, concurrent broadcast layer continuously transmits these packets. Receivers can recover original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

Crowdsourced mobile network access (CMNA), in which mobile users can share their Internet access with others, is a promising paradigm for addressing users' increasing needs for ubiquitous connectivity and alleviating cellular network congestion. In this paper, we study the operator-assisted CMNA model, in which a mobile virtual network operator (MVNO) incentivizes its subscribers to operate as mobile WiFi hotspots (hosts) through reimbursement and gets revenue from the relayed traffic. Despite of the promising performance, practical strategies for MVNO and hosts have not been studied yet. Existing works usually assume both MVNO and hosts can obtain complete information, and ignore the accompanied overhead in backhaul and privacy threats to users. Such assumptions are unrealistic in practice. To address this issue, we first systematically characterize the revenue loss for both MVNO and hosts with incomplete market information. Based on the analysis, we propose a novel partial cooperation strategy (PCS) to enable appropriate information exchange between MVNO and hosts with little overhead. With adaptive reimbursement and subtle information control, our PCS efficiently improves MVNO's revenue at equilibrium, and also satisfies the hosts' rationality. Through extensive evaluation on data from the real world, we demonstrate our PCS can improve MVNO's revenue by 23% at equilibrium, compared with the results without PCS.

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose Coco, a protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco addresses the key challenges in achieving collision tolerance, such as precise sender alignment and the control of transmission concurrency. We implement Coco in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at least 20 percent in general cases, compared with state-of-the-arts protocols.

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal time and strength requirements (e.g., Capture Effect) for concurrent broadcast while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a lightweight signal pattern recognition-based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS with TelosB nodes and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Link estimation is a fundamental component of forwarding protocols in wireless sensor networks. In low power forwarding, however, the asynchronous nature of widely adopted duty-cycled radio control brings new challenges to achieve accurate and real-time estimation. First, the repeatedly transmitted frames (called wake-up frame) increase the complexity of accurate statistic, especially with bursty channel contention and coexistent interference. Second, frequent update of every link status will soon exhaust the limited energy supply. In this paper, we propose meter, which is a distributed wake-up frame counter. Meter takes the opportunities of link overhearing to update link status in real time. Furthermore, meter does not only depend on counting the successfully decoded wake-up frames, but also counts the corrupted ones by exploiting the feasibility of ZigBee identification based on short-term sequence of the received signal strength. We implement meter in TinyOS and further evaluate the performance through extensive experiments on indoor and outdoor test beds. The results demonstrate that meter can significantly improve the performance of the state-of-the-art link estimation scheme.

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal timing and strength requirements (e.g., Capture Effect) for concurrent transmission while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a light-weight signal pattern recognition based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS and TelosB platform and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Despite very significant efforts on smartphonebased indoor localization, highly accurate and practical method remains an open problem. To guarantee accuracy, robustness, and practicality, in this paper, we propose SITE, a novel scheme uses acoustic Signal and phone Images to achieve accurate and robust indoor posiTion systEm. Our key observation is that if the simultaneously computed locations according to different sets of acoustic sources vary small, the positioning result is close to the true physical location. Based on the pre-deployed acoustic sources, SITE first tracks the direction of smartphone relative to an individual acoustic source according to proactively generated doppler effect in rough horizontal plane. Given m (m >= 3) acoustic sources, SITE can compute the relative coordinate of the phone in floor plan. By respectively selecting different sets of acoustic sources to compute the related coordinates, SITE can make sure whether these positioning results satisfy the requirement of positioning accuracy. If not, using images captured by phone camera, SITE exploits the synergy between its acoustic-based localization (coarse-grained) and the relative positions in reconstructed 3D point cloud by VisualSFM technique to refine the positioning result using acoustic signals. We have built a prototype of the SITE system and conducted evaluations in real testbed. Experimental results show that SITE is excellent in accuracy, robust and valuable in practical application.

Link estimation is a fundamental component of forwarding protocols in wireless sensor networks. In low power forwarding, however, the asynchronous nature of widely adopted duty-cycled radio control brings new challenges to achieve accurate and real- time estimation. First, the repeatedly transmitted frames (called wake-up frame) increase the complexity of accurate statistic, especially with bursty channel contention and coexistent interference. Second, frequent update of every link status exhausts the limited energy supply due to long duration of beacon broadcast. In this paper, we propose meter (Distributed Frame Counter), which takes the opportunities of link overhearing to update link status in real time. Furthermore, meter does not only depend on counting the successfully decoded wake-up frames, but also counts the corrupted ones by exploiting the feasibility of ZigBee identification based on short-term sequence of the received signal strength. We implement meter in TinyOS and further evaluate the performance through extensive experiments on indoor and outdoor testbeds. The results demonstrate that meter can significantly improve the performance of the state-of-the-art link estimation schemes.

Offloading network traffic from cellular to WiFi is widely used to reduce energy consumption since WiFi is assumed to have lower power consumption than cellular. However, we find that WiFi link quality may vary significantly under user mobility. Consequently, the energy efficiency of WiFi varies and sometimes becomes even worse than that of cellular. Therefore, widely used WiFi offloading may not be beneficial or even incurs more energy consumption. To address this issue, we propose Furion, an energy efficient WiFi offloading scheme that exploits beneficial WiFi links on smartphones. Towards such a goal, we investigate the relationship between energy efficiency and link quality. Accordingly, we propose a practical probabilistic model to predict WiFi energy efficiency based on the dynamics of link quality. We further extend the method to different environments by exploiting contextual factors in the prediction model to improve the accuracy. Based on the model, we design an adaptive offloading scheme to optimize the energy efficiency of WiFi offloading, while also guaranteeing user experience. We have implemented Furion on the Android platform and conduct extensive real-world experiments. The results demonstrate that Furion achieves 34.13% improvement in energy efficiency compared with the state-of-the- arts.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g., overhearing or coordination based approaches, either cannot scale up with the system size, or suffer high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate-free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgment scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor testbed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.

On-air access of individual sensor node (called remote control) is an indispensable function in operational wireless sensor networks, for purposes like network management and real-time information delivery. To realize reliable and efficient remote control in a wireless sensor network (WSN), however, is extremely challenging, due to the stringent resource constraints and intrinsically unrealizable wireless communication. In this paper, we propose TeleAdjusting, a ready-to-use protocol to remotely control any individual node in a WSN. We develop a coding scheme for addressing on the cost-optimal reverse routing tree. In the address of each node, all its upstream relaying nodes are implicitly encoded. Then through a distributed prefix matching process between the local address and the destination address, a packet used for remote control is forwarded along a cost-optimal path. Moreover, TeleAdjusting incorporates opportunistic forwarding into the addressing process, so as to improve the network performance in terms of reliability and energy efficiency. We implement TeleAdjusting with TinyOS and evaluate its performance through extensive simulations and experiments. The results demonstrate that compared to the existing protocols, TeleAdjusting can provide high performance of remote control, which is as reliable as network-wide flooding and much more efficient than remote control through a pre-determined path.

Due to the constraint of energy resource, the radio of sensor nodes usually works in a duty-cycled mode. Since the sleep schedules of nodes are unsynchronized, a sender has to send preambles to coordinate with its receiver(s). In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in concurrent transmissions, potentially enhancing end-to-end network performance. However, the current channel contention mechanism based on collision avoidance is too conservative to exploit concurrency. To address this problem, we propose COF, a practical protocol to exploit the potential Concurrency for low power Opportunistic Forwarding. COF determines whether a node should concurrently transmit or not, by incorporating: (1) a distributed and light-weight link quality measurement scheme for concurrent transmission and (2) a synthetic method to estimate the benefit of potential concurrency opportunity. COF can be easily integrated into the conventional unsynchronized sender-initiated protocols. We evaluate COF on a 40-node testbed. The results show that COF can reduce the end-to-end delay by up to 41% and energy consumption by 18.9%, compared with the state-of-the-art opportunistic forwarding protocol.

In distributed networks such as wireless ad hoc networks, local and lossy logs are often available on individual nodes. We propose REFILL, which analyzes lossy and unsynchronized logs collected from individual nodes and reconstructs the network behaviors. We design an inference engine based on protocol semantics to abstract states on each node. Further we leverage inherent and implicit event correlations in and between nodes to connect interference engines and analyze logs from different nodes. Based on unsynchronized and incomplete logs, REFILL can reconstruct network behavior, recover the network scenario and understand what has happened in the network. We show that the result of REFILL can be used to guide protocol design, network management, diagnosis, etc. We implement REFILL and apply it to a large-scale wireless sensor network project. REFILL provides a detailed per-packet tracing information based on event flows. We show that REFILL can reveal and verify fundamental issues, like locating packet loss positions and root causes. Further, we present implications and demonstrate how to leverage REFILL to enhance network performance.

The energy efficiency and delivery robustness are two critical issues for low duty cycled wireless sensor networks. The asynchronous receiver-initiated duty cycling media access control (MAC) protocols have shown the effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this paper, we present Contention Detectable MAC (CD-MAC), an energy efficient and robust duty-cycled MAC for general wireless sensor network applications. By exploring the temporal diversity of the acknowledgements, a receiver recognizes the potential senders and subsequently polls individual senders one by one. We further design efficient algorithm to avoid the possible acknowledgement collision. We implement CD-MAC in TinyOS and evaluate the performance on an indoor testbed with single-hop and multi-hop networks. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

In large scale wireless sensor networks, retransmission strategies are widely adopted to guarantee the reliability of multi-hop forwarding. However, keeping retransmission over a bursty link may fail consecutively. Moreover, the retransmission will also be useless over those back-up links which are spatial correlated with the failed link. Thus, it is necessary to design an unified retransmission strategy, which considers both temporal and spacial link properties, to further improve network reliability and efficiency. In this paper, we propose RxLayer, a practical and general supporting layer of data retransmission. Without inducing noticeable overhead, RxLayer captures the temporal and spatial link properties by conditional probability models. A sender will retransmit data over the candidate link with the highest delivery probability while failures occur. RxLayer can be transparently integrated with most of the existing forwarding protocols. We implement RxLayer and evaluate it on both indoor and outdoor testbeds. The results show that RxLayer improves networks reliability and energy efficiency in various scenarios. The network reliability is improved by up to 7.82%, and the total number of transmissions is reduced by up to 36.3%.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g. overhearing or coordination based approaches, either cannot scale up with the system size, or suffers high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgement scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor test-bed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.