Early dementia detection is a global healthcare priority in diverse populations. In this study, we propose a language-agnostic screening pipeline for dementia detection in the early stage. First, we use speaker diarization to isolate the speech of the target subject from a conversational recording. From the extracted speech segments, we derive a set of acoustic features (e.g., spectral centroid, pitch mean, mel-frequency cepstral coefficients) and linguistic features (e.g., normalized tone contrast, articulation clarity coefficient, articulatory effort coefficient). These features are used to train a ResNet-based binary classifier to distinguish between Healthy Controls (HC) and individuals with Mild Cognitive Impairment (MCI). We evaluated the trained model on a held-out test set comprising speakers of previously unseen languages, achieving an accuracy of 70%. This cross-lingual transfer performance highlights the potential of our approach for scalable, language-independent dementia screening.

Fingertip forces are important biomarkers for the detection and management of various conditions, including stroke and Parkinson's disease. This paper presents TouchWave, a non-contact sensing system designed to monitor fingertip forces during activities of daily living (ADL). TouchWave leverages under-cabinet millimeter-wave (mmWave) sensors to capture both macroscopic hand movements and subtle biomechanical cues associated with fingertip force production. A novel signal processing scheme is developed to suppress noise while preserving force-related information in the mmWave signals. Additionally, a hybrid deep neural network model is proposed to estimate highfidelity fingertip forces. A comprehensive evaluation involving 21 participants demonstrates the effectiveness of TouchWave in both controlled settings and ADL scenarios.

In order to determine the overall health of an individual, hand grip strength has emerged as a reliable and widely used indicator of muscular and functional health. However, the conventional devices for measuring grip strength, such as dynamometers, require direct interaction with a bulky external device. In this work, we propose a novel, cost-effective approach to estimate grip strength using bio-acoustic signals captured from the forearm via a compact armband equipped with low-power MEMS microphones. Our method performs well on grip strength classification with an accuracy of 93.33%, and as a proof of concept, demonstrates a promising direction for non-invasive grip strength estimation.

Monitoring human motion through inertial metrics is vital for healthcare, rehabilitation, and activity recognition. Traditional approaches rely on wearable inertial measurement units (IMUs), which, despite their accuracy, impose burdens due to their intrusive nature, limiting long-term usability. To mitigate this, recent advances explore device-free alternatives, such as pose-based inertial inference from video or mmWave sensing. However, inertial signals derived from pose tracking are prone to error amplification during differentiation. In this paper, we present mV-IMU, a novel mmWave-enabled Virtual Inertial Measurement Unit framework that bypasses pose estimation altogether to directly reconstruct body accelerations from raw mmWave signals. Our approach leverages a deep inertia reconstruction model trained on kinematics-informed features extracted from mmWave point clouds, integrated with a physicsguided optimization scheme for enhanced accuracy. Extensive evaluations show that mV-IMU achieves inertial measurement fidelity close to wearable IMUs, enabling practical, non-intrusive motion monitoring for smart healthcare and rehabilitation contexts.

The electrocardiogram (ECG) has been the gold standard for heart disease evaluation due to the rich information about the electrical activity of the heart contained in it. However, existing ECG monitoring devices either lack the capability for continuous monitoring or are unable to support multi-lead ECG recordings. To address the issues, we propose an approach for generating multi-lead ECG from photoplethysmogram (PPG), which can be passively monitored by wearable devices such as smartwatches. The PPG collected from wearable devices is first passed to a trained conditional diffusion model to generate the single-lead ECG, and then through a long short-term memory (LSTM) model to construct and predict the multi-lead ECG. The final outputs can be used to monitor and detect abnormal cardiac patterns in daily life. We evaluate the performance of our proposed approach with the dataset collected from dailylife scenarios involving 32 subjects. The results show that our approach can generate multi-lead ECGs accurately. In addition, a case study is conducted using data collected from the hospital, which demonstrates the effectiveness of our approach in detecting ST elevation.11ST elevation refers to an upward deviation of the ST segment on an electrocardiogram (ECG) from the baseline, indicating a potential heart attack or other cardiac issues. It is a crucial diagnostic finding in acute myocardial infarction (heart attack) and requires prompt medical attention. It is a key indicator of myocardial ischemia in practice.

WiFi-based indoor positioning plays a crucial role in a variety of location-based services due to its widespread availability and cost-effectiveness. However, most existing indoor positioning systems predominantly utilize channel state information (CSI) to learn channel characteristics and apply fingerprinting for position estimation. Unfortunately, CSI can only be extracted from a limited set of commercial WiFi devices, hindering its widespread application in practice. In this work, we introduce BFMLoc, a novel indoor positioning framework that exploits the beamforming feedback matrix (BFM), which is readily available on commercial WiFi devices. Although BFM provides broader sensing coverage, it sacrifices detailed channel information due to the compression applied to reduce feedback overhead. To address this limitation, we explore the feasibility of using BFM derivatives for indoor positioning and propose a U-net model to reconstruct the angle-delay profiles (ADP) from the compressed BFM data, thereby enhancing positioning accuracy. A Vision Transformer (ViT) model is then developed to extract spatial features from the predicted ADP maps to perform localization. Extensive evaluation results demonstrate that our framework achieves high positioning accuracy and improved robustness compared to state-of-the-art methods.

Skin wounds are often covered with gauze to protect the injury and support the healing process. Accurate wound assessment is essential for monitoring healing progress and guiding treatment decisions. However, existing assessment methods typically require direct exposure of the wound, necessitating the removal of gauze when present. This process disrupts the healing environment and increases the risk of secondary infections. In this paper, we introduce mmSkin, an innovative over-gauze wound assessment system that utilizes millimeter-wave (mmWave) radar technology to evaluate wound characteristics without the need to remove the gauze. Central to this system is the principle that variations in skin moisture, a critical indicator of wound health, significantly influence mmWave signal strength. By analyzing these variations, mmSkin accurately identifies skin moisture levels, thereby enabling precise assessment of wound conditions. To achieve reliable sensing, mmSkin incorporates a denoised mmWave imaging algorithm designed to reduce motion noise and effectively distinguish between signals reflected from the target skin and those from surrounding environmental interference. Additionally, the system integrates a physics-based model to guide the training of its moisture derivation model. This integration ensures that mmSkin can accurately estimate moisture distribution across the wound area, making it a powerful tool for noninvasive wound assessment. Extensive experiments validate the system’s high accuracy in over-gauze wound moisture distribution estimation, achieving a mean moisture error of approximately 0.5% in both wound phantom and invivo tests. Additionally, the system demonstrates a structural similarity index measure (SSIM) of about 0.9 compared to groundtruth moisture distributions in both test scenarios. These results highlight mmSkin’s potential to revolutionize noninvasive wound assessment and improve patient outcomes.

The hand localization problem has been a longstanding focus due to its many applications. The task involves modeling the hand as a singular point and determining its position within a defined coordinate system. However, due to data modality limitations, existing hand localization technologies face several challenges. For example, vision-based localization raises privacy concerns, while wearable-based methods compromise user comfort. In this article, we introduce mmHand, a new device-free, privacy-preserving dynamic hand localization system with pixel-level accuracy, using a single commodity mmWave device. We first propose a mmImage generation tool to fully extract spatial information from raw mmWave data and introduce a novel 2-D image-format representation of mmWave data. Next, we design a framework that provides a new quality evaluation method and pixel space labeling for the mmWave data. Finally, we present a cross-modality spatial feature-enhanced model with high spatial feature extraction capabilities, which can accurately localize hand positions at the pixel level in the mmWave radar U-V pixel coordinate system. We evaluate the system with experiments on 12 subjects in three scenarios, and the results across four metrics demonstrate the effectiveness of our hand localization system.

Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced transceivers. Millimeter-wave (mmWave) communication emerges as a promising solution by offering substantial bandwidth and accommodating massive connectivities. Nevertheless, the inherent signaling directionality and susceptibility to blockages pose significant challenges for deploying multiple transceivers with narrow antenna beams. Consequently, beam management becomes imperative for practical network implementations to identify and track the optimal transceiver beam pairs, ensuring maximum received power and maintaining high-quality access service. In this context, we propose a Contextual Combinatorial Beam Management (CCBM) framework tailored for mmWave wireless networks. By leveraging advanced online probing techniques and integrating predicted contextual information, such as dynamic link qualities in spatial-temporal domain, CCBM aims to jointly optimize transceiver pairing and beam selection while balancing the network load. This approach not only facilitates multiple access effectively but also enhances bandwidth utilization and reduces computational overheads for real-time applications. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

Millimeter-wave (mmWave) communication, a cor-nerstone in the evolution of next-generation wireless networks, offers substantial bandwidth and plays a crucial role in advancing wireless connectivity capabilities. Nevertheless, the inherent directionality and susceptibility to blockages pose significant challenges for a cost-effective beam management in densely deployed networks. This paper presents a Contextual Combina-torial Beam Management (CCBM) framework, leveraging both location-aware link qualities and beam correlation to tackle the joint access point (AP) and beam selection problem in mmWave networks, with a specific focus on mitigating coordination overhead and balancing the load across APs. Built upon a formulated multi-armed bandit problem, CCBM significantly reduces the uncertainty during online probing process by employing early stopping and attention-based selection mechanisms. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

CCS CONCEPTS• Applied computing → Health informatics.

Finger gesture recognition is gaining great research interest for wearable device interactions such as smartwatches and AR/VR headsets. In this paper, we propose a hands-free fine-grained finger gesture recognition system AO-Finger based on acoustic-optic sensor fusing. Specifically, we design a wristband with a modified stethoscope microphone and two high-speed optic motion sensors to capture signals generated from finger movements. We propose a set of natural, inconspicuous and effortless micro finger gestures that can be reliably detected from the complementary signals from both sensors. We design a multi-modal CNN-Transformer model for fast gesture recognition (flick/pinch/tap), and a finger swipe contact detection model to enable fine-grained swipe gesture tracking. We built a prototype which achieves an overall accuracy of 94.83% in detecting fast gestures and enables fine-grained continuous swipe gestures tracking. AO-Finger is practical for use as a wearable device and ready to be integrated into existing wrist-worn devices such as smartwatches.

As the demand for precision medicine rapidly grows, companion diagnostics is proposed to monitor and evaluate therapeutic effects for adjusting medicine plans in time. Although a set of clinical companion diagnostics tools (e.g., polymerase chain reaction) have been investigated, they are expensive and only accessible in a lab environment, which hinders the promotion to broader patients. In light of this situation, we take the first steps towards developing a real-world companion diagnostic tool by leveraging mobile technology. In this paper, we present TherapyPal, a privacy-preserving medicine effectiveness computational framework by harnessing semantic hashing-based digital symptomatic phenotyping. Specifically, sensor data captured from daily-life activities is first transformed into spectrograms. Then, we develop a hashing learning network to extract privacy-masked symptomatic phenotypes on smartphones. Afterward, symptomatic hashes at different medicine states are fed to a contrastive learning network in the cloud for treatment effectiveness detection. To evaluate the performance, we conduct a clinical study among 65 Parkinson's disease (PD) patients under dopaminergic drug treatment. The results show that TherapyPal can achieve around 84.1% medicine effectiveness detection accuracy among patients and above 0.925 privacy-masked scores for protecting each private attribute, which validates the reliability and security of TherapyPal to be used as a real-world companion diagnostics tool.

With the increasing deployment of voice-controlled devices in homes and enterprises, there is an urgent demand for voice identification to prevent unauthorized access to sensitive information and property loss. However, due to the broadcast nature of sound wave, a voice-only system is vulnerable to adverse conditions and malicious attacks. We observe that the cooperation of millimeter waves (mmWave) and voice signals can significantly improve the effectiveness and security of user identification. Based on the properties, we propose a multi-modal user identification system (named WavoID) by fusing the uniqueness of mmWave-sensed vocal vibration and mic-recorded voice of users. To estimate fine-grained waveforms, WavoID splits signals and adaptively combines useful decomposed signals according to correlative contents in both mmWave and voice. An elaborated anti-spoofing module in WavoID comprising biometric bimodal information defend against attacks. WavoID produces and fuses the response maps of mmWave and voice to improve the representation power of fused features, benefiting accurate identification, even facing adverse circumstances. We evaluate WavoID using commercial sensors on extensive experiments. WavoID has significant performance on user identification with over 98% accuracy on 100 user datasets.

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we presentVocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically,VocalPrintexploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the vocal tract and vocal source features and input them into an ensemble classifier for authentication.VocalPrintis practical as it allows the effortless transition to a smartphone while having sufficient usability due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show thatVocalPrintachieves over 96 percent authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

In this paper, we propose a transformative solution that uses a low-cost light sensor and commodity smartphone to support fast self-assessment of blood perfusion of ulcer regions in daily life. By harnessing the knowledge of light polarization, our system can "see-through" the skin to quantify the spatio-temporal properties of subdermal vasculature in terms of pulsation and hemoglobin. Our evaluation results show that our system can achieve 78.6% accuracy to detect poor and good blood perfusion.

Voice user interfaces (VUIs) have been adopted in many IoT and mobile devices in daily life. VUIs provide a good user experience with lower-cost hardware (i.e., microphone) and higher throughput (compared with keyboard and touchscreen). Currently, identity authentication and receiving commands are the two most common interactions through VUIs, leaving physiological information in the voice unexploited. Recognizing this untapped potential, we propose VocalHR to extend VUIs beyond voice commands to heart activity sensing without additional hardware. VocalHR is built upon the voice-heart modulation effect, which is rooted in the cardiac activities' impacts on the behavior of the vocal organ during voice production. VocalHR captures voice features of cardiac activity in multiple voice organs and proposes a deep learning pipeline to transform features into cardiac activities. As this is the first study exploring voice-based heart activity sensing, we conducted extensive experiments on 43 demographically diverse subjects to verify the intrinsic link between voice and heart activities. On average, VocalHR can achieve less than 11.1% normalized sensing error on the heart event timing. Our further evaluation shows VocalHR is robust to different microphone specifications and varying speech rates.

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger in Kanget al., 2003. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, ”voice of fingerprint”) from a user swiping his fingertip on everyday surfaces. Specifically, we developSonicPrintto leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification.SonicPrintis practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98 percent identification accuracy.

Temperature sensor is one of the most widespread technologies in the IoT era. Wireless temperature monitoring systems are convenient to deploy and can drive mass applications in the fields of smart home, transportation and logistics. Currently, wireless temperature monitoring products are based on microelectronic and semiconductor components, which are not cost-effective (e.g., a few dollars) and more importantly, generate electronic wastes. In this work, we present ThermoWave, a new paradigm of wireless temperature monitoring that is ecological, battery-less, and ultra-low cost. Specifically, ThermoWave is on the basis of the thermal scattering effect on millimeter-wave (mmWave) signals. Specifically, cholesteryl materials align their molecular patterns at different environmental temperatures, and this temperature-induced pattern change will be modulated and sensed by the scattered mmWave signals. There are three functional modules in the ThermoWave system. The ThermoTag is a cholesteryl material inked film or paper tag that can be conveniently attached to the object of interest to monitor temperature changes. Each ThermoTag costs less than 0.01 dollars. The temperature modulated mmWave scattering will be received by a mmWave-radar based ThermoScanner and demodulated by a software-based temperature decoder ThermoSense, which includes a model-based method (i.e., ThermoDot) for point temperature estimation and a data-driven method (i.e., ThermoNet) for thermal imaging. We prototype and evaluate the ThermoWave system performance in both controlled and real-world setups. Experimental results show that the ThermoWave achieves the precision of ±1.0°F in the range of 30°F to 120°F in a controlled setup. We also investigate the performance in real-world applications, and the ThermoWave can reach the ±3.0°F precision in the temperature estimation. We also test and discuss sustainability, durability, robustness, and cost-effectiveness of the ThermoWave in both design and experiments.

Drug effectiveness management is a complicated and challenging task in chronic diseases, like Parkinson's Disease (PD). Drug effectiveness control is not only linked to personal out-of-pocket cost but also affecting the quality of life among patients with chronic symptoms. In the current practice, although that health and medical professionals still play a key role in the personalized treatment plan, the critical decision on drug selection falls upon the individual report when patients call in or visit the clinics. Unfortunately, most of the patients with chronic diseases either fail to report their day-to-day symptoms or have a limited access to medical resources due to economic constraints. In this paper, we present PDLens, a first smartphone-based system to detect drug effectiveness among Parkinson's in daily life. Specifically, PDLens can extract digital behavioral markers related to PD drug responses from everyday activities, including phone calls, standing, and walking. PDLens models the PD symptom severity on drug treatment and detects the change of severity scores before and after drug intake. A ranking-based multi-view deep neural network is developed to decide the drug effectiveness upon the symptom severity changes. To validate the performance of PDLens, we conduct a pilot study with 81 PD patients and monitor their smartphone activities and severity changes over 33693 drug intake events across six (6) months. Compared with the standard clinical drug effectiveness test developed by Motor Disorder Society, results reveal that PDLens is a promising tool to facilitate drug effectiveness detection among PD patients in their daily lives.

The advent of smart devices has caused unprecedented security and privacy concerns to its users. Although the fingerprint technology is a go-to biometric solution in high-impact applications (e.g., smart-phone security, monetary transactions and international-border verification), the existing fingerprint scanners are vulnerable to spoofing attacks via fake-finger and cannot be employed across smart devices (e.g., wearables) due to hardware constraints. We propose SonicPrint that extends fingerprint identification beyond smartphones to any smart device without the need for traditional fingerprint scanners. SonicPrint builds on the fingerprint-induced sonic effect (FiSe) caused by a user swiping his fingertip on smart devices and the resulting property, i.e., different users' fingerprint would result in distinct FiSe. As the first exploratory study, extensive experiments verify the above property with 31 participants over four different swipe actions on five different types of smart devices with even partial fingerprints. SonicPrint achieves up to a 98% identification accuracy on smartphone and an equal-error-rate (EER) less than 3% for smartwatch and headphones. We also examine and demonstrate the resilience of SonicPrint against fingerprint phantoms and replay attacks. A key advantage of SonicPrint is that it leverages the already existing microphones in smart devices, requiring no hardware modifications. Compared with other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacy-oriented and secure approach to identify users across smart devices of unique form-factors.

Core body temperature is an important indicator of medical treatment. Sudden changes in core body temperature can be a precursor to neurodegenerative diseases such as Parkinson's disease. These diseases have the potential to strike at any time, therefore, long-term monitoring of core body temperature and alerting to sudden changes in temperature become important. In this paper, we designed an in-ear thermometer to monitor the core body temperature with the help of smartphone.

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present VocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically, VocalPrint exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations during communication. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the text-independent vocal tract and vocal source features and input them to an ensemble classifier for user authentication. VocalPrint is practical as it leverages a low-cost, portable, and energy-efficient hardware allowing effortless transition to a smartphone while having sufficient usability as typical voice authentication systems due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that VocalPrint can achieve over 96% authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

Parkinson's disease (PD) is a chronic neurodegenerative disorder resulting from the progressive loss of dopaminergic nerve cells. People with PD usually demonstrate deficits in performing basic daily activities, and the relevant annual social cost can reach about $25 billion in the United States. Early detection of PD plays an important role in symptom relief and improvement in the performance of activities in daily life (ADL), which eventually reduces societal and economic burden. However, conventional PD detection methods are inconvenient in daily life (e.g., requiring users to wear sensors). To overcome this challenge, we propose and identify the non-speech body sounds as the new PD biomarker, and utilize the data in smartphone usage to realize the passive PD detection in daily life without interrupting the user. Specifically, we present PDVocal, an end-to-end smartphone-based privacy-preserving system towards early PD detection. PDVocal can passively recognize the PD digital biomarkers in the voice data during daily phone conversation. At the user end, PDVocal filters the audio stream and only extracts the non-speech body sounds (e.g., breathing, clearing throat and swallowing) which contain no privacy-sensitive content. At the cloud end, PDVocal analyzes the body sounds of interest and assesses the health condition using a customized residual network. For the sake of reliability in real-world PD detection, we investigate the method of the performance optimizer including an opportunistic learning knob and a long-term tracking protocol. We evaluate our proposed PDVocal on a collected dataset from 890 participants and real-life conversations from publicly available data sources. Results indicate that non-speech body sounds are a promising digital biomarker for privacy-preserving PD detection in daily life.

Voice-user interface (VUI) has become an integral component in modern personal devices (\textite.g., smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low-quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial-of-service (DoS) of user requests. Therefore, it is of paramount importance to enable noise-resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio-frequency signals, such as millimeter wave (mmWave) for sensing the noise-resistant voice of an individual. We first perform an in-depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end-to-end noise-resistant speech sensing system.WaveEar comprises a low-cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near-throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real-world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise-resistant voice and enable a pervasive VUI in modern electronic devices.

Inventory management is pivotal in the supply chain to supervise the non-capitalized products and stock items. Item counting, indexing and identification are the major jobs of inventory management. Currently, the most adopted inventory technologies in product counting/identification are using either the laser-scannable barcode or the radio-frequency identification (RFID). However, the laser-scannable barcode is entangled by an alignment issue (i.e., the laser reader must align with one barcode in line-of-sight), and the RFID is economically and environmentally unfriendly (i.e., high-cost and not naturally disposable). To this end, we propose FerroTag which is a paper-based mmWave-scannable tagging infrastructure for the next generation inventory management system, featuring ultra-low cost, environment-friendly, battery-free and in-situ (i.e., multiple tags can be simultaneously processed outside the line-of-sight). FerroTag is developed on top of the FerroRF effects. Specifically, the magnetic nanoparticles within the ferrofluidic ink reply to probing mmWave with classifiable features (i.e., the FerroRF response). By designating the ink pattern and hence the location of particles, the related FerroRF response can be modified. Thus, a specifically designated ferrofluidic ink printed pattern, which is associated with a unique FerroRF response, is a remotely retrievable (a.k.a., mmWave-scannable) identity. Furthermore, we augment FerroTag by designing a high capacity pattern system and a fine-grained identification protocol such that the capacity and robustness of FerroTag can be systematically improved in mass product management in inventory. Last but not least, we evaluate the performance of FerroTag with 201 different tag design patterns. Results show that FerroTag can identify tags with an accuracy of more than 99% in a controlled lab setup. Moreover, we examine the reliability, robustness and performance of FerroTag under various real-world circumstances, where FerroTag maintains the accuracy over 97%. Therefore, FerroTag is a promising tagging infrastructure for the applications in inventory management systems.

Electricity price forecasting is a significant part of smart grid because it makes smart grid cost efficient. Nevertheless, existing methods for price forecasting may be difficult to handle with huge price data in the grid, since the redundancy from feature selection cannot be averted and an integrated infrastructure is also lacked for coordinating the procedures in electricity price forecasting. To solve such a problem, a novel electricity price forecasting model is developed. Specifically, three modules are integrated in the proposed model. First, by merging of Random Forest (RF) and Relief-F algorithm, we propose a hybrid feature selector based on Grey Correlation Analysis (GCA) to eliminate the feature redundancy. Second, an integration of Kernel function and Principle Component Analysis (KPCA) is used in feature extraction process to realize the dimensionality reduction. Finally, to forecast price classification, we put forward a differential evolution (DE) based Support Vector Machine (SVM) classifier. Our proposed electricity price forecasting model is realized via these three parts. Numerical results show that our proposal has superior performance than other methods.

The emergence of edge computing has witnessed a fast-growing volume of data on edge devices belonging to different stakeholders which, however, cannot be shared among them due to the lack of the trust. By exploiting blockchain's non-repudiation and non-tampering properties that enable trust, we develop a blockchain-based big data sharing framework to support various applications across resource-limited edges. In particular, we devise a number of novel resource-efficient techniques for the framework: (1) the PoC (Proof-of-Collaboration) based consensus mechanism with low computation complexity which is especially beneficial to the edge devices with low computation capacity, (2) the blockchain transaction filtering and offloading scheme that can significantly reduce the storage overhead, and (3) new types of blockchain transaction (i.e., Express Transaction) and block (i.e., Hollow Block) to enhance the communication efficiency. Extensive experiments are conducted and the results demonstrate the superior performance of our proposal.

With the emergence of edge computing in various applications domains, end users are now surrounded by a fast growing volume of data from edge devices belonging to different stakeholders. However, these edge devices cannot cooperate to share big data because of the distrust among them. In this paper, the blockchain is deployed in collaborative edges by exploiting the non-repudiation and non-tampering properties to enable trust. First, we develop a blockchain based big data sharing framework in collaborative edges for adapting to the limited computational and storage resources in edge devices. Then, a consensus mechanism called Proof-of-Collaboration (PoC) is proposed for computational resources reduction in our proposed framework, where edge devices offer their credits of PoC to compete for the block generation. Moreover, we put forward a futile transaction filter algorithm for transaction offloading, greatly reducing the storage resources occupied by the blockchain in edges. Extensive experiments are performed to demonstrate the superior performance of our proposal.

Demand side management (DSM) is a key mechanism to make smart grids cost efficient using electricity price forecasting issue. Price forecasting method takes the big price data into account, and gives estimates of the future electricity price. However, most of existing price forecasting methods cannot avoid redundancy at feature selection and lack of an integrated framework that coordinates the steps in forecasting. To address this issue, we first propose a new electricity price forecasting framework. It is significant to design a system tool chain based on big data analytics for ensuring that the users can make appropriate decisions. To this end, three algorithms are proposed integratedly. First, feature redundancy elimination is implemented by the fusion of Grey Correlation Analysis (GCA) and ReliefF algorithm. Second, a combination of Kernel function and Principle Component Analysis (KPCA) is designed to achieve dimensionality reduction. Finally, Support Vector Machine (SVM) optimization algorithm based on differential evolution (DE) is proposed to forecast price classification. These three modules jointly power the price forecasting system. Simulation results show the superiority of our proposed framework.

The advances of network technology and mobile communication technology are making eHealth possible. In eHealth systems, physiological data and relevant context-aware data are acquired continuously and in real time. At the same time, such large-scale data results in huge challenges in the aspect of real-time big data processing since eHealth data appears in the form of data stream. Therefore, we propose a novel incremental learning algorithm, namely α-SVMSGD, which improves the SVMSGD (Support Vector Machine-Stochastic Gradient Descent) algorithm by updating the training data with the continuous data stream. Besides, this α-SVMSGD may handle the problem that original SVMSGD cannot further mine the useful information in unclassified data. In α-SVMSGD, the process of training data updating is completed by introducing the concept of forgetting mechanism, in which the forgetting factor α is introduced to weed out useless training data. α-SVMSGD is applied into ambient assisted living communications, and further incorporated into the data filtering layer of a local data processing architecture (LDPA) to reduce data redundancy. Simulation results confirm that the proposed algorithm is a promising data redundancy solution for classification without loss of accuracy in the case of real-time data stream.

In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than ever before, and Internet traffic is rapidly increasing, not only in volume but also in heterogeneity. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive papers have been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this article, a hybrid-stream model is proposed to solve these problems for video analysis. Functionality of this model covers Data Preprocessing, Data Classification, and Data-Load-Reduction Processing. Specifically, an improved Convolutional Neural Networks (CNN) classification algorithm is designed to evaluate the importance of each video frame and video clip to enhance classification precision. Then, a reliable keyframe extraction mechanism will recognize the importance of each frame or clip, and decide whether to abandon it automatically by a series of correlation operations. The model will reduce data load to a dynamic threshold changed by σ, control the input size of the video in mobile Internet, and thus reduce network overload. Through experimental simulations, we find that the size of processed video has been effectively reduced and the quality of experience (QoE) has not been lowered due to a suitably selected parameter η. The simulation also shows that the model has a steady performance and is powerful enough for continuously growing multimedia big data.