The Internet of Vehicles (IoVs) integrates vehicles to the enormous realm of cyberspace which introduces some intelligence and convenience in the transportation sector. However, real-time traffic related information is exchanged over the open public internet among the vehicles and with other infrastructures. This exposes these networks to a myriad of security threats that can lead to accidents and congestions. Although many solutions have been developed over the recent past, most of them are inefficient while others are still susceptible to attacks. In this paper, we leverage on the k-valued modified bilinear inverse Diffie-Hellman problem and one-way hashing function to develop an efficient authentication protocol for IoVs. To demonstrate the robustness of its semantic security, we deploy the Real or Random (ROR) model. In addition, we execute extensive informal security anaysis to show that our scheme resists typical IoVs attacks such as forgery, privileged insider, and replay. Moreover, its performance evaluation shows that it incurs the lowest computation and communication overheads among its peers. Specifically, the proposed protocol reduces the transmission overheads by 8.5%, while increasing the supported security functionalities by 88.9%. It is therefor suitable for deployment in the IoV environment to mitigate the numerous security threats at relatively lower computation and energy costs.

Accurate and timely 90-day survival prediction for critically ill COVID-19 patients is vital to optimize scarce ICU resources, yet single-source models often miss important pathophysiological cues. Emerging studies show that combining complementary modalities can reveal richer prognostic signatures than any modality in isolation. Motivated by this, we present DQU-CLIP, an advanced multimodal deep learning framework designed to overcome this limitation. Utilizing the CoCross dataset (comprising 171 ICU patients), our model integrates chest Xrays (CXRs) via a pre-trained Contrastive Language-Image Pretraining (CLIP) encoder with key clinical features, including Age, Charlson Comorbidity Index (CCI), APACHE II, and SOFA scores, processed by a neural network. DQU-CLIP achieves a robust ROC-AUC of 0.85, significantly outperforming unimodal baselines (CXR-only: 0.78, Clinical-only: 0.72) and competing multimodal approaches. Extensive validation and ablation studies confirm the synergistic benefit of this fusion. Furthermore, interpretability analysis using Grad-CAM identified relevant lung regions in CXRs, while feature importance pinpointed SOFA and APACHE II scores as critical indicators of disease severity. By effectively unifying radiological and clinical evidence, DQU-CLIP provides a more reliable prognostic assessment.

Robotic autonomous ultrasound imaging is a challenging task as robots require strong analytical capabilities to make sound decisions in complex spatial relationships. In this paper, we integrate visual and tactile information into the ultrasound robotic system drawing inspiration from the process of human doctors conducting ultrasound scans, and explore the impact of different modalities of information on our task. The proposed multimodal deep reinforcement learning (DRL) framework can integrate real-time visual feedback and tactile perception, and directly output 6D pose decisions to control the ultrasound probe, thereby achieving fully autonomous ultrasound imaging of soft, movable, and unmarked targets. We demonstrate the feasibility of our method on a simulation platform and propose an effective model transfer learning method. Subsequently, we conducted further evaluations of the approach in a real-world environment. The results indicate that our approach effectively enhances the performance of autonomous ultrasound scanning and manual adjustments further optimize the outcomes.Note to Practitioners—This work is motivated by the increasing demand for intelligent human-machine interaction in medical applications. By improving the automation of traditional medical scanning procedures such as ultrasound scanning, the efficiency of medical scanning can be greatly improved. In this work, we propose a multi-modal autonomous ultrasound scanning system based on DRL, which can be applied to improve the efficiency of human-machine interaction in medical environments to execute daily health screening or used in emergency situations.

The widespread use of electronic devices has contributed to an increase in poor posture, particularly when it comes to the cervical spine, leading to various cervical vertebral pain disorders. In this article, we focus on accurately monitoring the motion status of the cervical spine using the accelerometers and gyroscope sensors embedded in earphones. Our aim is to gain a better understanding of cervical spine health. To address the individual differences among subjects, we introduce fuzzy rules to the Re-ISDA method, proposing a novel approach known as FuzRe-ISDA. Unlike traditional domain adaptation methods, the FuzRe-ISDA method offers flexibility in adjusting the contribution from different source domains. It takes into account the collective impact of multiple models on predicting new user behavior. Moreover, this method can quickly adapt to new users without requiring extensive datasets. Experimental results demonstrate that our FuzRe-ISDA approach outperforms popular domain adaptation methods in terms of accuracy when predicting cervical motion. This highlights the effectiveness of our approach in addressing individual differences and improving the reliability of cervical spine motion prediction.

Reconstructing keyboard input through side-channel attacks has posed significant threats to user security. While conventional keystroke eavesdropping attacks have demonstrated effectiveness using side channels such as acoustic signals, they are usually shorter in range and can be significantly affected by environmental noises. In this paper, we proposeLaserKey, a novel keystroke eavesdropping technique that leverages the long-range and noise-resistant nature of lasers to achieve a more stealthy side-channel attack. We utilize laser sensors to accurately capture the subtle vibrations induced on laptop screens by keystrokes, and innovatively design a laser-driven deep learning-based keystroke recognition model with the inputs being the Mel-frequency Cepstral Coefficien (MFCC), Time Difference of Arrival (TDoA), and amplitude features extracted from such vibration signals. Through systematic experiments, we demonstrate thatLaserKeyachieves a 92.2% single-key recognition accuracy. By combining multiple single-key recognition capabilities based on this, we then realize the end-to-end word-level recognition. Moreover, to mitigate the recognition errors caused by the changes in keystroke positions, we introduce a meta-learning based domain generalization approach for achieving robust laser position calibration. Results show thatLaserKeyachieves as low as 3% character error rate (CER) for word-level recognition, proving its effectiveness for long-range and high-accuracy keystroke eavesdropping, and highlighting the necessity for countermeasures in the future.

Material sensing holds significant potential in areas such as environmental awareness and security monitoring. While technologies like RFID, WIFI, and UWB offer potential solutions for portable, non-contact material identification, the need to place targets in fixed positions for identification has limited the flexibility of material sensing. In this article, we first innovatively apply the Range-Angle heatmap (RAheatmap) to effectively represent the distance, placement angle, and inherent material attributes to pave the way for precise material identification. Then propose an innovative system called Material-ID to utilize Commercial-Off-The-Shelf (COTS) millimeter wave (mmWave) radar for material sensing. Additionally, we endow the system with cross-domain adaptability to make it tailored to identify material reflection attributes and minimize the effects of variables such as distance and placement angle. The experiments prove the effectiveness of the proposed system.

Near-infrared spectroscopy (NIRS) has been widely applied to quality assessment for various products. The recent breakthrough in the miniaturization of NIR sensors allows users to scan samples onsite and get results in seconds, making the technology suitable for mobile sensing and IoT applications. However, external factors, such as temperature, humidity, and illumination, can affect the sensor response and the samples, leading to distorted spectra. This causes a domain shift problem in statistical learning algorithms where the spectra collected from one environment may have a different distribution from the spectra collected from another environment. As a result, the performance of the pretrained model can be severely degraded when the operating environment differs significantly from the training one. Existing works suggest fine-tuning the pretrained model using the spectra of reference samples collected from the target environment. Although the number of samples required for model fine-tuning is usually much smaller than that required for model pretraining, it is still impractical to ask users to always carry many reference samples in mobile sensing scenarios. This article presents the NIRWatchdog to address the cross-domain issue of NIRS-based mobile sensing tasks. The proposed approach provides much flexibility and practicality as the transfer data set can be automatically generated based on as few as one reference sample onsite. With only one reference sample, the area under the curve (AUC) of the NIRWatchdog is higher than 85% even when the target environment is considerably different from the training one. In comparison, the conventional approach needs more than 15 reference samples onsite to achieve a comparable performance under the same conditions.

This study presents a systematic literature review on blockchain-based authentication in smart environments that include smart city, smart home, smart grid, smart healthcare, smart farming and smart transportation. The review incorporated 39 articles presenting blockchain solutions for security and privacy issues through authentication mechanisms in these smart environments. Guided by three research questions to determine the main issues in smart environment, the availability of blockchain-based authentication solutions and identified research gaps and future research endeavors, this review used PRISMA method to provide insights on the use of blockchain-based authentication schemes. The research gap is that blockchain solutions are mostly at the proposal, and sometimes conceptual stage is in the reviewed articles. In addition, solutions presented in smart environments require exploration into blockchain. The findings show similar situational issues across different smart environments and the flexibility and adaptability of blockchain to provide solutions to the identified issues pertaining to security and privacy. More clearly, the authentication problem posed across different smart environments can be adapted to blockchain technology provided that it is combined with other technologies to increase efficiency, despite the existence of large-scale authentication mechanisms. Now, blockchain still has the unique, distributed feature of converting the current database into blockchain databases. This review guided future research directions which could further contribute to the sustainable management of smart environments.

Internet of Vehicles (IoVs) has greatly improved safety and quality of services in Intelligent Transportation System (ITS). However, the deployed Dedicated Short-Range Communication (DSRC) protocol broadcasts messages after every 100-300ms. This presents some challenges in message validation within this short duration. As such, most of the current authentication schemes which incur heavy computation and communication overheads are not suitable in this environment. In this paper, an efficient authentication scheme is presented based on lightweight cryptographic primitives such as collision-resistant one-way hashing functions and exclusive OR (XOR) operations. In our protocol, two-factor authentication is attained using Physically Unclonable Function (PUF) generated identities and random nonces, as well as passwords. Extensive formal security verification using Real or Random (RoR) model shows that it is provably secure. In addition, elaborate semantic security analysis shows that it offers anonymity, untraceability and key secrecy as well as resilience against numerous IoV attack vectors. In terms of performance, comparative evaluations demonstrate that it reduces computation and energy consumptions by 42.31%. Moreover, it increases the supported security features by 26.67%.

Symmetric key generation methods are recently designed for wireless communication based on similar and unique observations of sensor measurements, such as wireless radio channels, inaudible sound channels, etc. Heartbeats, as unique biometrics, have been used for symmetric key generation. However, current solutions are designed for wearable devices with the integration of the same types of touchable heartbeat measurement equipment and fail because of the significant difference from different devices or the same devices with different deployment locations, which limits its large scale of deployment and application. To solve this problem, we propose a general heartbeat-based symmetric key generation solution by embracing observation variance from different devices, i.e., using an optical heart sensor on one finger and facing the camera of the second device to the user's face. We propose a novel data processing method to mitigate the significant difference and exploit key reconciliation to generate symmetric keys for paring devices and securing wireless communication. We have conducted extensive evaluations and shown our proposed method has good key matching rates up to 100% as well as good randomness. Security analysis has also been conducted to ensure the robustness of the proposed method.

Solar cells have been widely used for offering energy for Internet of Things (IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this article, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing-based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%. We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random.

Cracks are usually curve-like structures that are the focus of many computer-vision applications (e.g., road safety inspection and surface inspection of the industrial facilities). The existing pixel-based crack segmentation methods rely on time-consuming and costly pixel-level annotations. And the object-based crack detection methods exploit the horizontal box to detect the crack without considering crack orientation, resulting in scale variation and intra-class variation. Considering this, we provide a new perspective for crack detection that models the cracks as a series of sub-cracks with the corresponding orientation. However, the vanilla adaptation of the existing oriented object detection methods to the crack detection tasks will result in limited performance, due to the boundary discontinuity issue and the ambiguities in sub-crack orientation. In this paper, we propose a first-of-its-kind oriented sub-crack detector, dubbed as CrackDet, which is derived from a novel piecewise angle definition, to ease the boundary discontinuity problem. And then, we propose a multi-branch angle regression loss for learning sub-crack orientation and variance together. Since there are no related benchmarks, we construct three fully annotated datasets, namely, ORC, ONPP, and OCCSD, which involve various cracks in road pavement and industrial facilities. Experiments show that our approach outperforms state-of-the-art crack detectors.

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

Recently proposed split learning (SL) is a promising distributed machine learning paradigm that enables machine learning without accessing the raw data of the clients. SL can be viewed as one specific type of serial federation learning. However, deploying SL on resource-constrained Internet of Things (IoT) devices still has some limitations, including high communication costs and catastrophic forgetting problems caused by imbalanced data distribution of devices. In this article, we design and implement IoTSL, which is an efficient distributed learning framework for efficient cloud-edge collaboration in IoT systems. IoTSL combines generative adversarial networks (GANs) and differential privacy techniques to train local data-based generators on participating devices, and generate data with privacy protection. On the one hand, IoTSL pretrains the global model using the generative data, and then fine-tunes the model using the local data to lower the communication cost. On the other hand, the generated data is used to impute the missing classes of devices to alleviate the commonly seen catastrophic forgetting phenomenon. We use three common data sets to verify the proposed framework. Extensive experimental results show that compared to the conventional SL, IoTSL significantly reduces communication costs, and efficiently alleviates the catastrophic forgetting phenomenon.

The capability of local smart sensing based on Internet of Things (IoT) devices is typically limited due to due to the inherent limitations of computational and storage capabilities. Recently, collaborative inference among multiple devices has been considered as an effective way to improve the sensing capabilities of individual IoT devices. However, the collaborative inference process still faces the challenges of data privacy leakage and inefficient collaboration. To alleviate the above issues, we design a blockchain-based collaborative inference system in this article, called CoBC, which allows each heterogeneous device node on the blockchain to customize a personalized local machine learning model according to its own hardware constraint and performance, thus improving the efficiency of resource utilization of the whole system. Meanwhile, each device node only needs to complete training locally, which significantly reduces the risk of privacy leakage due to the remote transmission of local data. CoBC improves the sensing capability of single device nodes by using collaborative inference that can obtain a more robust global inference. In addition, CoBC employs a Bayesian approximation training approach to evaluate the output uncertainty of each device node to further improve the efficiency of collaborative inference. To evaluate the performance, we deploy CoBC in a real environment and conduct a large number of simulations to evaluate the efficiency of CoBC. The simulation results demonstrate that CoBC exhibits good performance and good practicality in various criteria.

Recently we have witnessed the rise of Artificial Intelligence of Things (AIoT) and the shift of sensing paradigm from cloud-centric to the edge-centric, which effectively improves the sensing capability of intelligence transportation systems. To improve the real-time sensing performance, in this work we propose an ensemble sensing based scheme to solve the time-constraint synchronized inference problem and achieve robust inference with heterogeneous IoT devices in intelligence transportation systems. We design and implement Ensen, which incorporates various novel techniques such as customized DNN model design, KD-based model training, and dynamic deep ensemble management, etc., to achieve improved accuracy and maximize the computational resource usage of the whole sensing group. Extensive evaluations on different types of common IoT devices have shown that Ensen achieves a robust performance and can be easily extended to different types of convolutional neural networks.

As the recent rise of intelligent transportation systems (ITS), the sensing capability of vehicles has become crucial in realizing sophisticated intelligent transportation services. Collaborative sensing, an important approach to extend the sensing coverage of individual vehicles, has become an essential component of connected vehicle systems. However, due to challenges such as privacy concerns, frequent communication interruptions, customized models, and limited available data, the application of collaborative sensing in current ITS systems is still limited. In this paper, we propose BSL, a novel multi-exit split learning-based collaborative inference system. The key innovation of BSL is the introduction of multi-exit to the split network, enabling network training and collaborative inference between distributed device nodes and the cloud in a split manner. Specifically, BSL allows the device node to dynamically collaborate with the cloud by introducing the edge mode and collaboration mode, ensuring that intelligent services provided to the device will be sustained even if the communication is interrupted, which is crucial in ITS systems. We have implemented the system and evaluated it with public dataset on different embedded devices. The results demonstrate the promising performance of BSL.

Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss. In this paper, we propose the geometry-aware guided loss (GAGL) that enhances the discrimination ability and is only applied in the training stage without extra computation and memory during inference. The GAGL consists of the feature-based geometry-aware projected gradient descent method (FGA-PGD) that approximates the geometric distances of the features to the class boundaries, and the geometry-aware update rule that learns an anchor of each class as the approximation of the feature expected to have the largest geometric distance to the corresponding class boundary. Then the discriminative power can be enhanced by minimizing the distances between the features and their corresponding class anchors in the feature space. To address the limited availability of related benchmarks, we collect a fully annotated dataset, namely, NPP2021, which involves inconsistent cracks and noisy backgrounds in real-world nuclear power plants. Our proposed GAGL outperforms the state of the arts on various benchmark datasets including CRACK2019, SDNET2018, and our NPP2021.

Networked devices, such as wearable devices, laptops, smart home appliances, etc., are ubiquitous nowadays. To secure communication among those devices, symmetric keys are widely used because of their feasibility in resource-constrained networked devices. The ob-servations of sensors from independent devices have been adopted for symmetric key generation. The identical biometrics information or environment interference has been observed by sensors, and their corresponding patterns are used for key generation. Pop-ular signals from networked devices are inertial measurements, sound, wireless signals, etc. The existing sensor-based key gen-eration solutions use the same type of sensors for both devices. Different from the existing solutions, we are the first to propose a cross-sensor symmetric key generation system i2Key, where two devices collect inertial measurements from a motion sensor and inaudible sound from a microphone, respectively. A new coding framework is designed for general key generation. We also pro-pose an efficient and accurate time synchronisation method for key generation. Additionally, a multi-tier key reconciliation method is suggested to improve key generation performance. By using the proposed architecture, the key generation rate is improved by up to approximately 40% compared with the situation without using it. We also perform security analysis and randomness analysis over the proposed method.

Sign language (SL) is used by hearing impaired and deaf people. The WiFi-based sign language recognition (SLR) technology has attracted much attention due to its contactless nature and wide applications. Most previous SLR works are designed to recognize a single isolated sign word in data samples. However, such assumption is not realistic in practical applications, such as, in daily communication, deaf people usually express their mind through a phrase (a.k.a. a group of words) rather than an isolated word. Compared with the previous works, the sign words in a phrase have variety of length, sequential patterns, and combinations in realistic communications between deaf people. Therefore, it is challenging in exploiting the WiFi signals to accurately capture the unique patterns of SL in the previous natural setting. In this article, we propose Wi-Phrase, a multigesture context-awareness SLR system. Wi-Phrase exploits WiFi signals to translate SL to the English phrase. To achieve this, Wi-Phrase employs principal component analysis (PCA) projection to filter out the noise and convert cleaned WiFi signals to spectrogram. Then, we propose a novel Residual-MultiHead model that exploit residual learn structure to obtain local patterns of phrases and adopt multihead block to capture the global context information of phrase. To prove the advanced nature of our model, we design a WiFi-based SL phrase data set of 40 categories for experiments. Our comprehensive evaluation shows that Wi-Phrase achieves an accurate phrase recognition accuracy of 95.03%. In future, we envision Wi-Phrase could be widely used as the phrase command control system for deaf people in IoT devices.

6D pose estimation of objects is an important part of robot grasping. The latest research trend on 6D pose estimation is to train a deep neural network to directly predict the 2D projection position of the 3D key points from the image, establish the corresponding relationship, and finally use Pespective-n-Point (PnP) algorithm performs pose estimation. The current challenge of pose estimation is that when the object texture-less, occluded and scene clutter, the detection accuracy will be reduced, and most of the existing algorithm models are large and cannot take the real-time requirements. In this paper, we introduce a Multi-directional Feature Pyramid Network, MFPN, which can efficiently integrate and utilize features. We combined the Cross Stage Partial Network (CSPNet) with MFPN to design a new network for 6D pose estimation, MFPN-6D. At the same time, we propose a new confidence calculation method for object pose estimation, which can fully consider spatial information and plane information. At last, we tested our method on the LINEMOD and Occluded-LINEMOD datasets. The experimental results demonstrate that our algorithm is robust to textureless materials and occlusion, while running more efficiently compared to other methods.

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

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual's limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual's activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

Human activity recognition (HAR) is an important component in context awareness IoT applications such smart home, smart building etc. With the proliferation of WiFi-integrated devices, researchers exploit WiFi signals to recognize various human activities. In this work, we introduce a HAR as a Service (HARaaS) model for activity recognition services applied in IoT areas. HARaaS proposes a novel edge computing model in the concept of the Sensing as a Service (S2aaS) architecture to offer accurate and real-time activities recognition services with good energy efficiency. HARaaS distributes the resource-hungry computing workload i.e. training recognition model to edge terminals, and exploits the built-in intelligence of IoT devices. A WiFi-based activity recognition service is designed following the HARaaS architecture, and the lightweight machine learning and deep learning model are incorporated in the service for accurate activity recognition. Experiments are conducted and demonstrate the service achieves an activity recognition accuracy of 95% with extremely low latency and high energy efficiency.

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

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

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

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

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