As a significant biometric identification technology, face recognition (FR) is extensively utilized in identity verification and security surveillance systems. Current research predominantly relies on high-definition RGB camera-based methods. However, these methods are susceptible to various factors such as lighting conditions and disguises. This paper proposes a low-cost face recognition solution called Warm- Face based on thermal array sensors. By leveraging the thermal radiation of the face, we overcome the disturbances caused by lighting conditions and disguises, thereby achieving rapid and highly accurate face recognition. However, face recognition based on thermal array sensors still faces two major challenges. Firstly, in complex scenarios, thermal noise interference can lead to the thermal radiation characteristics of the target face becoming indistinguishable from the background. Secondly, due to their large network parameter sizes and high computational complexity, recognition models face challenges in simultaneously achieving low latency and high accuracy. WarmFace extracts facial regions through a semantic segmentation-based approach, effectively reducing the impact of background interference on recognition performance. Additionally, in the recognition model, we utilize a series of linear transformations instead of convolution operations to process the intrinsic features of images, which reduces redundancy in feature maps while preserving the essential information. Extensive real-world experiments validate the effectiveness of WarmFace in various environments, achieving an average recognition accuracy of 98.6%.

The evolution of sixth-generation (6G) wireless communication significantly accelerates the Internet of vehicles innovation, catalyzing advancements in autonomous driving systems. The collaborative model utilizing 6G is expected to break through the vehicle’s inherent field-of-view deficiencies and heterogeneous computational resource constraints, further improving the efficiency of the technology. This paper proposes a novel 6G NOMA air-ground integrated sensing-computing framework that achieves high-quality collaborative perception and low-latency 3D computing offloading to alleviate restrictions through cooperative networking with unmanned aerial vehicles (UAVs) and road-side units (RSUs). To balance latency and UAV energy consumption for efficient collaboration, we formulate it as a mixed integer nonlinear programming problem (MINLP). Considering the time sensitivity of the perceptual task, we introduce queuing and Lyapunov optimization theory to transform the optimization objective into a Lyapunov drift-penalty function and derive its upper bound, which we model as a Markov decision process (MDP) and optimize it with Large Language Models (LLMs) assisted temporal replay deep reinforcement learning (TR-DRL). For perception quality improvement, an elite-guided binary-weighted firefly algorithm is developed to solve combinatorial optimization in perception fusion. Experimental results demonstrate 4.88% and 8.26% improvements in latency and energy efficiency respectively, alongside enhanced perception fusion quality 7.41% compared with advanced counterparts.

In vehicular edge computing (VEC) networks, by implementing content caching and V2X connectivity, road side unit (RSU) and nearby vehicles can serve as platforms for rapid data retrieval to address mobile traffic explosion. However, due to the dynamic and multi-constrained environment consisting of heterogeneous vehicles and RSU, it is challenging to meticulously plan cooperative caching policies. Additionally, due to the diversity of contents and the mobility of vehicles, the caching policy space is massive, which can be fatal for vehicles with limited computing and energy. In this paper, we formulate cooperative content caching in VEC networks as Markov decision process (MDP), configuring caching policies for vehicles and RSU. Our aim is to minimize Lyapunov drift and long-term delay. To address the massive caching policies, we propose a two-stage deep reinforcement learning (TS-DRL) algorithm. In the first stage, an improved ant colony algorithm is used to generate unilateral suggestions and construct action space to avoid the curse of dimensionality. In the second stage, we combine the Noisy Net and Double Deep Q-Learning Network to avoid overestimating value and efficient exploration problem. Simulation results show that TS-DRL outperforms advanced algorithms in terms of delay and cache hit rate.

Person re-identification (Re-ID) can recognize users based on their clothing, body shape, and other information without the need for clear facial images, and is widely applied in the field of intelligent security. Traditional Re-ID systems mainly rely on high-definition RGB cameras, but the deployment of large-scale high-definition RGB cameras indoors has caused serious privacy and ethical concerns. Recently, wireless-based Re-ID systems (Wi-Fi, RFID, millimeter-wave radar, etc.) have shown promising prospects, but the limited sensing resolution hinders their practical deployment. In this paper, we propose WarmGait, a Re-ID system based on thermal array sensors, which can achieve high-precision Re-ID at low cost and minimize the invasion of user privacy. However, using thermal arrays for Re-ID still faces two major challenges. The first is the low and unclear texture resolution of images caused by low-cost infrared devices. The second is that existing gait recognition methods require maintaining the sequential constraint of gait images, which reduces the flexibility of gait recognition or Re-ID. To address these two challenges, we first designed an edge module inspired by Taylor Finite Difference (TFD) to aggregate image edge information to help improve the resolution of infrared devices. Then, we considered gait as a collection of gait profiles and extracted features from the frame level and collection level for recognition, breaking through the limitations of the number and order of input images. After extensive experimental evaluation, our model can achieve an average recognition accuracy of 87.3% in various scenarios, demonstrating the potential of WarmGait in Re-ID.

Vehicular edge computing (VEC) emerges as a promising paradigm for processing computing-intensive parallel vehicular tasks, where vehicular tasks can be offloaded to the edge nodes [e.g., roadside units (RSUs)] to seek less computing delay. Considering the impact of computation services on offloading efficiency, there are several works that jointly study the decision making of task offloading and service caching. However, the existing works fail to consider the time-varying service requests and ignore the time-slots correlation of the computation services. To bridge the gap, this work designs a service-aware parallel task offloading approach, which is the first work to jointly explore time-varying computation services and task offloading based on real-world vehicular trajectory data in VEC networks. Specifically, we first propose a computation service prediction algorithm using the real-world vehicular trajectory data. Guided by this, RSUs flexibly precache computation services. Then, we propose a learning-based parallel task offloading algorithm, which allows vehicles to make offloading decisions based on the history of the edge selections. Furthermore, we conduct simulations to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces task delay by 45%, 58%, and 55% compared to the algorithms without service-aware computation offloading under various CPU cycles, task numbers, and time slots.

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, aVehicle-assistedCollaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.

With the development of Wi-Fi sensing, wireless-based gait recognition has become increasingly important as it supports a wide range of applications (person identification, disease diagnosis, etc.). However, two serious challenges limit the universal deployment of such Wi-Fi vision schemes: i) the limited bandwidth of Wi-Fi severely restricts the granularity of gait recognition, and ii) users non-gait behaviors (e.g., stopping and turning) interfere with the extraction of gait-related features. In this paper, we propose CSID, which can achieve robust gait recognition under the limited bandwidth conditions of commercial Wi-Fi devices. Specifically, we use a neural network to generate super-resolution spectrograms of channel state information (CSI), overcoming the limitation of insufficient Wi-Fi bandwidth. To overcome the challenge of non-gait behavior interference, considering the human-incomprehensible nature of Wi-Fi spectrograms, we adopt cross-domain adversarial training and further extract gait features that are independent of the interference behaviors by learning domain-independent representations. We conducted a large number of experiments in different indoor environments, and the average person identification rate of the CSID system reached 91.6%. These results demonstrate that the CSID system is promising and could be used as a complement to visual person identification systems in the future.

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this article, we propose a continuous user authentication mechanism, namely, HandPass, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the nonreplicability of hand structure, it is difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making HandPass comprehensively understand vibration response variation. We prototype HandPass on smartphones, and extensive experiments demonstrate that HandPass can achieve satisfactory authentication accuracy.

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.

With deepening urbanization and Internet of Vehicles (IoV) applications, the number of private cars has been increasing in recent years. However, because the surging number of private cars is not compatible with limited road resources, private car users have had unsatisfactory commute experiences during their daily travel. In this work, we focus on improving private car users’ commute experience based on an analysis of IoV trajectory data in a privacy-preserving way. Our idea is based on the following observations: 1) the commute experience of private car users is closely related to the departure time and the travel cost and 2) most travel costs are spent on urban hot zones. Motivated by these findings, we propose a novel blockchain-enabled model named Deep Improving Commute Experience (DeepICE) to improve private car users’ commute experience by predicting when to depart and when to arrive. In this model, a blockchain with a consensus mechanism is developed to address private car user privacy concerns. In addition, we propose a multitask learning-enabled graph convolution network (GCN) method to capture the highly complex features and relations between two tasks, i.e., the departure time and travel cost, and then develop the model to predict these two tasks. The experimental results demonstrate the superior performance of our proposed model compared to existing approaches. Our model can be applied to efficiently enhance private car users’ commute experience.

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.

Automotive cyber-physical systems need to be rigorously checked and tested under various physical conditions. Automakers aim to improve development efficiency of the automotive cyber-physical systems in the fierce market competition. However, the actual development process suffers from the challenges of long development cycle and poor scalability. To tackle these challenges, this article develops a digital twinning based adaptive development environment for automotive cyber-physical systems, which addresses two critical problems: each physical entity (i.e., electronic control unit, component, test source, etc.) needs to clone a corresponding digital twin; digital twins and the physical entities need to interact closely. The first problem is addressed through proposing an integrated digital twinning clone flow. The second problem is addressed through developing a smart digital twinning board. Our case study with the automotive body control system demonstrates that the adaptive development environment achieves a high adaptability with short development cycle, low complexity, low cost, high scalability, and high flexibility, which meet various automotive cyber-physical design requirements during the development process.

Co-verification of reliability and confidentiality is a necessary process for safety- and security-critical applications. While these two objectives are conflicting, preassignment has emerged as an effective and efficient verification solution. In this article, we propose two preassignment-based co-verification techniques, namely, Blocks-based Vulnerability Preassignment (BVP) and Reversed Blocks-based Time Preassignment (RBTP) for a parallel application in distributed CAN FD systems. BVP can significantly improve reliability under a vulnerability bound, while RBTP can reduce vulnerability over a reliability goal. Real case study with the parallel automotive application and parallelism study with two structures of high-parallelism and low-parallelism applications are demonstrated; the proposed BVP and RBTP can improve the verification acceptance ratio by 19 and 10 percent compared to the state-of-the-art Average Vulnerability Preassignment (AVP) and Average Time Preassignment (ATP) techniques, respectively.

Model-based design using the Simulink modeling formalism and associated toolchain has gained popularity in the development of real-time embedded systems. However, the current research on software synthesis for Simulink models has a critical gap for providing a deterministic, semantics-preserving implementation on multicore architectures with partitioned fixed-priority scheduling. In this paper, we consider a semantics-preservation mechanism that combines (1) the RT blocks from Simulink, and (2) task offset assignment to separate the time windows to access shared buffers by communicating tasks. We study the software synthesis problem that optimizes control performance by judiciously assigning task offsets, task priorities, and task communication mechanisms. We develop a problem-specific exact algorithm that uses an abstraction layer to hide the complexity of timing analysis. Experimental results show that it may run a few orders of magnitude faster than a direct formulation in integer linear programming.