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.

Radio maps play a crucial role in optimizing wireless network performance and configuration, providing insights into the spatial distribution of radio frequency signal power. Existing solutions often face challenges in generalizing and adapting across various environments, frequency bands, and vertical dimensions. To overcome these limitations, we propose UniRM, a universal large model designed for constructing multiband 3D radio maps. UniRM leverages large-scale pre-training and prompt learning techniques to accurately generate radio maps across diverse environments, altitudes, and frequency bands. Specifically, UniRM employs a UNet-based encoder-decoder architecture during pre-training to extract universal latent representations that capture shared features across different environmental conditions. A prompt learning module further enhances this by transforming auxiliary inputs, such as environmental descriptions, frequency bands, and altitudes, into discriminative embeddings, thereby enabling effective cross-domain generalization and ensuring robustness in unseen scenarios. Extensive experiments using a large, diverse dataset covering numerous scenarios demonstrate that UniRM outperforms state-of-the-art baselines by over 10% in key metrics, including mean squared error, normalized mean squared error, root mean squared error, and peak signal-to-noise ratio. Notably, zero-shot evaluations highlight UniRM’s strong ability to generalize to new environments without retraining. The code for UniRM is available at: https://github.com/Shirleyue/UniRM.

In recent years, multivariate time series anomaly detection has become an important research topic in the field of anomaly detection. In Industrial Internet of Things (IIoT) systems, the collected data may be affected by internal failures, external disturbances, or other adverse factors. In such cases, appropriate anomaly detection methods are required to ensure the stable operation of the system. However, existing methods based on reconstruction, prediction, or hybrid approaches often suffer performance degradation when anomalies are present in large amounts of training data, as these anomalies can negatively impact the training process. To address this challenge, we propose a dynamic masking strategy in both temporal and spatial dimensions. We develop a time series imputation framework based on a diffusion model that integrates Graph Neural Network (GNN) and Transformer architectures. This framework, termed Spatial-Temporal Dynamic Mask Diffusion for Anomaly Detection (STDMD-AD), incorporates a dynamic masking mechanism: temporally, reconstruction errors are used to mask data by randomly concealing values with higher errors; spatially, attention is applied to mask channels that are more likely to contain anomalies during training. Experiments on five real-world datasets demonstrate that the proposed method outperforms existing benchmarks and achieves state-of-the-art anomaly detection performance.

With the rapid proliferation of IoT devices, the volume of generated data is growing at an unprecedented pace. Due to the limited resources of edge devices, a significant portion of this data must be transmitted to the cloud for in-depth processing, large-scale analysis, long-term storage, and archival purposes. Consequently, the performance has become a critical concern. While identifying prevailing challenges and research gaps in this domain requires a systematic review, such efforts remain largely absent from existing survey literature. This article addresses this gap by offering a structured review of recent optimization approaches. It begins by categorizing the literature into three main strategies: lossless transmission, lossy transmission, and hybrid approaches. In the context of lossless transmission, we analyze techniques such as data compression algorithms and incremental versus full synchronization mechanisms. For lossy strategies, we analyze approaches including lossy compression and predictive methods. In addition, we investigate hybrid strategies that integrate both lossless and lossy techniques to leverage their complementary advantages. Finally, we discuss the limitations of existing studies and highlight promising directions for future research in optimizing edge-to-cloud data transmission.

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.

Integrating Generative AI (Gen-AI) into vehicular computing can significantly enhance road safety and driving experience. The performance of Gen-AI inference in vehicular computing is highly sensitive to the number of inference steps, where even minor adjustments can disrupt the balance between delay and inference quality. This raises a critical question: how can inference steps be optimally determined for diverse tasks, given the inherent delay-quality trade-offs? Existing approaches fail to offer an optimal solution due to the lack of flexible inference services and efficient memory bandwidth allocation strategies. To address these challenges, we develop aCriticality-awareResource-efficientInference (CARIN) framework, where inference steps are dynamically adjusted to balance delay and quality for multi-criticality tasks. Leveraging an accurate inference model, CARIN fully exploits in-vehicle resources to accelerate both parameter loading and task computing during inference. The joint optimization problem of step control, memory bandwidth allocation, and compute resource scheduling is formulated as a mixed-integer nonlinear programming (MINLP) problem, and solved by a novel learning-to-optimize (L2O) algorithm efficiently. Experimental results demonstrate that, for high-criticality tasks, the proposed approach achieves$28.4\%$latency reduction and$36\%$quality improvement over baseline methods.

Detecting stowaways hidden in various transport vehicles, including cars, trucks, containers, and trailers, is crucial to border security inspection systems. Existing solutions mainly rely on contact-based sensors and manual inspection, which significantly compromise the efficiency of border control operations. Therefore, there is an urgent need for an automated, efficient and fast non-contact vehicle hidden person detection system. In this paper, we propose SiVe, a novel border inspection system that utilizes laser Doppler vibrometer (LDV) to detect hidden people in the vehicle. We extract signals associated with human activities (such as breathing, heartbeat, low-frequency body movements, etc.) from complex laser reflection data to detect the presence of hidden people. Specifically, we first employs the Empirical Mode Decomposition (EMD) algorithm to extract and reconstruct signals associated with human activities in complex and noisy environments. Then based on the characteristics of EMD outputs, we design a Time-series Variation Feature extraction Identification network (TVFI-net) model that accurately captures complex time-varying patterns for efficient and reliable detection of hidden people presence. Extensive real-world experiments validate the effectiveness of SiVe in various environments. The system achieves an average presence detection accuracy of 99.93$\%$for sedans and MPVs, 98.37$\%$for light trucks, 98.07$\%$for heavy trucks, and 95.23$\%$for trailers in non-contact detection of hidden people across twelve different vehicle types, under various indoor and outdoor environments.

The increasing proliferation of unmanned aerial vehicles (UAVs) poses significant challenges to airspace security, necessitating the development of effective detection technologies. Passive detection techniques, such as passive radar, offer key advantages including spectrum efficiency and covert operation. However, passive radars that rely on coherent integration are often computationally expensive and dependent on strong Doppler signatures, rendering them ineffective for detecting low-speed or hovering UAVs. To overcome these limitations, we explore the use of channel state information (CSI) time series to characterize UAV presence and propose a temporal variation network for detecting UAV states, including hovering conditions. Our method utilizes digital terrestrial multimedia broadcast (DTMB) signals, which have wide coverage and high transmission power. By capturing DTMB signals with a single receiver, we reduce the complexity of passive detection systems. First, we perform channel estimation on the received signal to obtain CSI, which is arranged in frame order to form a CSI time series. This enables the modeling of interference channels caused by UAVs. We then propose the Channel Estimation and Temporal Variation Network (CETVNet), which leverages an adaptive noise reduction module and a multi-period feature extraction module to process these series for passive UAV state detection. Finally, a real-world signal dataset is collected using a software-defined radio device to train and evaluate CETVNet. Experimental results demonstrate that CETVNet achieves superior performance compared to state-of-the-art methods.

The widespread adoption of wireless communication systems in both military and civilian applications has significantly advanced technological progress and social development across various industries. However, narrowband interference signals pose a significant challenge, severely disrupting the normal operation of wireless communication equipment. A major obstacle in existing narrowband interference detection lies in enhancing robustness under complex channel propagation conditions and diverse, dynamically changing types of interference. In view of those challenges, we propose a robustness-enhanced narrowband interference detection method by utilizing unlabeled data. The proposed detection network incorporates soft-shrink technology to isolate irrelevant signal features while adaptively extracting and fusing original and time-frequency features. The proposed method leverages the distribution characteristics of interference frequency bands to enhance model robustness in varying channel propagation environments. Additionally, we design a pseudo-label-based model tuning process to exploit the potential of unlabeled data, further enhancing the model’s robustness. Comparative experiments demonstrate the superiority of the proposed method against various baselines, as well as against configurations incorporating individual network modules.

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.

Traffic flow prediction is crucial for intelligent transportation and aids in route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices. We propose an online transfer learning (OTL) framework with a multi-layer perceptron (MLP)-assisted graph convolutional network (GCN), termed OTL-GM, which consists of two parts: transferring source-domain features to edge devices and using online learning to bridge domain gaps. Experiments on four data sets demonstrate OTL’s effectiveness; in a comparison with models not using OTL, the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.

Transformer-based methods have demonstrated potential capability in object detection of synthetic aperture radar (SAR) images. However, their reliance on global features and quadratic complexity hinders real-time and precise localization of small objects. To deal with these issues, in this work, a Mamba in Transformer DETR (MIT-DETR) framework is proposed. In MIT-DETR, the features are initially divided into visual sentences and processed by Mamba to extract global information with linear complexity. These visual sentences are then further divided into words, which are analyzed for local information through the transformer. In addition, a multiscale dilated attention (MSDA) module is designed with the purpose of obtaining rich multiscale information at a low cost. Numerical experiments show that MIT-DETR improves AP50by 5.01% and FPS by 5.1 on HRSID compared to the baseline. These results demonstrate the effectiveness and superiority of the proposed strategy. The code will be available athttps://github.com/CongLi-18/MIT-DETR.

Deep learning has demonstrated its potential capability in object detection of synthetic aperture radar (SAR) images. However, the low detection accuracy for small and dense objects remains a critical issue. To address this issue, in this work, a feature focus enhanced YOLO (FFE-YOLO) architecture is proposed. In FFE-YOLO, a channel feature enhanced (CFE) module is introduced to extract richer information and reduce time consumption by integrating it into the backbone. Additionally, a feature selection fusion network (FSFN) is designed to enhance the feature representation of small and dense objects by fully utilizing channel information. Numerical results demonstrate that FFE-YOLO outperforms baseline by 3.12% and 3.06% on datasets HRSID and LS-SSDD-v1.0, respectively, but with less inference time. These results demonstrate the effectiveness and superiority of the proposed strategy.

In building extraction, collecting pixel-level annotations required by fully supervised methods is extremely costly. Image-level weakly supervised methods based on class activation maps (CAMs) effectively reduce the cost and have shown promising progress. However, generating high-quality CAMs remains challenging due to the supervision gap between classification and segmentation tasks. Specifically, image-level supervision causes CAMs to activate only the most discriminative regions, which compromises the integrity of CAMs. Meanwhile, the absence of pixel-level supervision leads to a depletion of spatial information, resulting in imprecise boundaries. In this study, we propose a novel image-level weakly supervised building extraction method based on view consistency, named ViewCAM, to generate high-quality CAMs. The view transformation module is designed to apply view transformations to remote sensing images and the high-dimensional features. Additionally, a feature affinity enhancement module (FAEM) is proposed to capture positional relationships between pixels and low-level features, such as edges and textures, improving boundary fineness. We integrate these two modules into a classification network and incorporate pixel-level supervision using view-consistency constraints. The entire network is then trained in an end-to-end manner, leading to improved integrity and boundary fineness of the seeds generated from CAMs. To verify the effectiveness and robustness of ViewCAM, we conduct experiments on two representative datasets, and the results demonstrate that our proposed method achieves superior CAM integrity and boundary fineness, outperforming state-of-the-art methods.

Automatic Modulation Classification (AMC)-oriented Deep Neural Networks (ADNNs) have received much attention in recent years for their wide range of applications. However, they are vulnerable to attacks. Adversarial Examples (AEs) of modulation signals with added weak perturbations can easily fool ADNNs. The study of AEs on AMC, on one side, can enhance the security of wireless communication systems; on the other side, it can provide an effective defence against potential attacks. Nevertheless, most existing attack methods generate AEs with low transferability. In this paper, we propose a Multiscale Discriminative Attack Method (MDAM) for modulated signals. The method strives to alleviate such transferability issue by destroying discriminative features in multi-layer. Specifically, we utilize interpretable class activation maps to distinguish the discriminative regions, ignoring the noise and focusing on the interference of the discriminative features. Beyond that, we propose a multi-layer activation disruption loss to constrain activations in the middle layers. In so doing, the AEs do not erroneously retain deep features of the original signal. We conduct extensive experiments on RadioML datasets and the local area network (LAN) communication dataset we collected to evaluate the effectiveness of MDAM in both white-box and black-box attack scenarios. The results show that MDAM outperforms existing methods.

Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global information and the inability to extract effective features. In this paper, we propose a lightweight classification network Local and Global Graph ConvNeXt. This model has a hybrid architecture of convolutional neural network and Transformers. We build the Global NeXt Block and the Local NeXt Block to extract the local and global features of the structural magnetic resonance imaging (sMRI). These two blocks are optimized by adding global multilayer perceptron and locally grouped attention, respectively. Then, the features are fed into the pixel graph neural network to aggregate the valid pixel features using mask attention. In addition, we decoupled the loss by category to optimize the calculation of the loss. This method was tested on slices of the processed sMRI datasets from ADNI and achieved excellent performance. Our model achieves 95.81% accuracy with fewer parameters and floating point operations per second (FLOPS) than other classical efficient models in the diagnosis of AD.

As urbanization accelerates, the rise in private car usage has become a double-edged sword, symbolizing economic growth while exacerbating urban air pollution due to increased carbon emissions. This paper studies the problem of carbon emission prediction of private cars in urban environments, enabling effective carbon emissions reduction and energy conservation guidance. Existing methods struggle with costly carbon emission collection and rely on precise emission factors, inaccuracies in modeling spatial similarities across urban regions, and complexities in modeling global temporal variations. To solve these issues, the Multi-View CollAboRative graph network (MVCAR) framework is proposed for private car carbon emission prediction. MVCAR employs a trajectory-based method to estimate carbon emissions from private car mobility to represent the spatial-temporal carbon emissions effectively. To capture the geo-spatial and semantic regional similarities of the carbon emissions, MVCAR constructs multi-view graphs and utilizes multi-view graph convolution networks. Furthermore, MVCAR integrates collaborative gated recurrent networks to model the spatial-temporal correlations of carbon emissions. The collaborative gated recurrent networks include a multi-view gated recurrent unit (GRU) and a sequential GRU. The multi-view GRU models the multiple spatial-temporal correlations of carbon emissions. A learnable temporal module embeds various temporal features, and further feeds these features into sequential GRU to capture the temporal variations of carbon emissions. Finally, a collaborative strategy that synergistically combines multi-view and sequential GRUs through stacked training. Extensive experiments on real datasets demonstrate the superiority of the proposed MVCAR.

Carbon emissions caused by passenger cars in cities are essentially responsible for severe climate change and serious environmental problems. Exploring carbon emissions from passenger cars helps to control urban pollution and achieve urban sustainability. However, it is a challenging task to foresee the spatio-temporal distribution of carbon emission from passenger cars, as the following technical issues remain. i) Vehicle carbon emissions contain complex spatial interactions and temporal dynamics. How to collaboratively integrate such spatial-temporal correlations for carbon emission prediction is not yet resolved. ii) Given the mobility of passenger cars, the hidden dependencies inherent in traffic density are not properly addressed in predicting carbon emissions from passenger cars. To tackle these issues, we propose a Collaborative Spatial-temporal Network (CSTNet) for implementing carbon emissions prediction by using passenger car trajectory data. Within the proposed method, we devote to extract collaborative properties that stem from a multi-view graph structure together with parallel input of carbon emission and traffic density. Then, we design a spatial-temporal convolutional block for both carbon emission and traffic density, which constitutes of temporal gate convolution, spatial convolution and temporal attention mechanism. Following that, an interaction layer between carbon emission and traffic density is proposed to handle their internal dependencies, and further model spatial relationships between the features. Besides, we identify several global factors and embed them for final prediction with a collaborative fusion. Experimental results on the real-world passenger car trajectory dataset demonstrate that the proposed method outperforms the baselines with a roughly 7%-11% improvement.

RGBT tracking is a task that utilizes images from visible (RGB) and thermal infrared (TIR) modalities to continuously locate a target, which plays an important role in various fields including intelligent transportation systems. Most existing RGBT trackers do not achieve high precision and real-time tracking speed simultaneously. To address this challenge, we propose an innovative RGBT tracker, the Siamese Triple-stream Feature Aggregation Network (SiamTFA). Firstly, a triple-stream backbone is presented to implement multi-modal feature extraction and fusion, which contains two parallel Swin Transformer feature extraction streams, and one feature fusion stream composed of joint-complementary feature aggregation (JCFA) modules. Secondly, our proposed JCFA module utilizes a joint-complementary attention to guide the aggregation of multi-modal features. Specifically, the joint attention can focus on spatial location information and semantic information of the target by combining the features of two modalities. Considering the complementarity between RGB and TIR modalities, the complementary attention is introduced to enhance the information of beneficial modality and suppress the information of ineffective modality. Thirdly, in order to reduce the computational complexity of the joint-complementary attention, we propose a depthwise shared attention structure, which utilizes depthwise convolution and shared features to achieve lightweight attention. Finally, we conduct extensive experiments on four official RGBT test datasets and the experimental results demonstrate that our proposed tracker outperforms some state-of-the-art trackers and the tracking speed reaches 37 frames per second (FPS). The code is available athttps://github.com/zjjqinyu/SiamTFA.

Due to the presence of redundancy and interference, frame sampling is a promising but challenging solution to mitigate the expensive computation of video action recognition. Although the motion prior has shown great potential for frame selection, existing motion-based strategies suffer from limitations in terms of robustness and interpretive evaluation. In this paper, we devise a robust frame sampling strategy called positive motion guided sampler (PMGSampler). It consists of two procedures, local motion capture and global motion statistics. At the local level, we propose two concepts about inter-frame motion amplitude and motion continuity, which helps to perceive the movement of subjects and identify abnormal events that may generate negative pseudo-motion information. Then, through a global analysis of the obtained local motions, the sampler becomes more sensitive to informative frames and robust to outliers. The proposed sampler can be applied to most existing models for improving recognition accuracy. We conduct extensive experiments on four widely-used benchmarks to demonstrate the superiority of our PMGSampler over other methods of the same type. In addition, to analyse how sampled frames influence action recognition, we present a visual interpretation method for video models, termed as spatio-temporal class activation map (STCAM). By introducing spatial and temporal branches, our STCAM is able to visualise the salience of spatio-temporal features. With the help of STCAM, we can further intuitively evaluate the performance of different sampling strategies.

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

The 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.

In the emerging cloud-edge-end computing networks, edge servers possess more constrained resources and face greater task offloading pressure than centralized cloud servers due to the surge in mobile applications and data. Concurrently, the presence of multiple edge service providers introduces additional challenges, including competition among servers, disordered resource pricing, and a lack of coordination in edge and cloud resource allocation. To address these issues, we propose a novel approach aimed at optimizing task deployment, resource pricing, and system coordination. First, we develop a competitiveness model to facilitate efficient edge-side task allocation while addressing the challenges of resource pricing under competitive conditions. Second, we design a transformer-based task prediction model to enhance the accuracy of resource demand forecasting, thereby enabling more effective edge-cloud resource allocation. To achieve these objectives, the system's interaction is structured into two distinct stages. This division simplifies the problem-solving process and ensures that the long-term goal of maximizing benefits for all stakeholders—edge service providers, cloud providers, and end-users—is achieved. The proposed solution not only improves task offloading efficiency and resource utilization, but also promotes fair competition and pricing transparency across the system.

The surge in encrypted network traffic poses a significant challenge to existing cyberspace security measures. Classical traffic identification methods, such as those based on ports or statistical features, are ineffective against encrypted traffic. Deep learning methods offer new avenues for identifying encrypted traffic, but they are highly dependent on labeled data and struggle to adapt to new types of encrypted traffic. Pre-trained models possess a powerful generalization capability due to their training on large-scale unlabeled datasets, and they reduce the dependency on labeled data for specific downstream tasks. In this paper, we present an encrypted traffic classification framework based on pre-trained models and introduce a tokenization method tailored for encrypted traffic called FlowPiece. The experimental results demonstrate that our approach, along with the FlowPiece method, can reduce the parameter count by approximately 93% while maintaining nearly identical performance. This significant reduction in parameters enables the widespread application of encrypted traffic identification methods based on pretrained models.

Silent high-dynamic target recognition using single receiver has garnered significant attention due to its advantages in safety and stealth within military operations. This paper presents an in-depth exploration of a silent high-dynamic target recognition method based on frequency modulation (FM) signals. The proposed method processes the FM signals with a least squares filtering approach and further implements the calculation of Cross-Ambiguity Function (CAF) mapping to achieve target imaging within the CAF spectrum. By capturing FM signals with an antenna array tuned to various orientations, we conducted empirical analysis using actual aircraft in flight as the target for identification. The recognition scheme put forth by our research is capable of precisely determining the velocity of the target and the relative distance parameters between dual base stations, facilitating accurate tracking of targets.

The Specific Emitter Identification (SEI) technology has broad application prospects in the fields of the Internet of Things and cognitive communication and serves as an effective means for device authentication. Most existing SEI methods based on deep learning primarily operate in the real number domain. However, complex numbers naturally represent radio frequency signals, making complex-valued neural network(CVNN) a superior choice for signal representation. To address the limitations of traditional real-valued neural network methods, such as low recognition accuracy and the requirement for a large number of training samples, we propose an SEI method based on CVNN to learn the true RF fingerprint features. In the data preprocessing stage, we employ a signal slicing strategy with a random window step size to enhance data randomness and improve the model’s generalization performance. Given the complex-valued nature of signals, we employ a complex-valued convolutional neural network for feature extraction. Subsequently, we design a feature fusion module based on the complex-valued attention mechanism to eliminate redundant features while preserving radio frequency fingerprint features. Considering the high similarity between emitter signals, we employed a joint loss function based on metric learning that promotes intra-class aggregation and interclass separation. The experimental results demonstrate that the proposed method uncovers the authentic RF fingerprint with high accuracy and robustness under limited training sample conditions.

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

Real-time indoor crowd counting plays an important role in many applications such as crowd control, resource allocation and advertisement. Current research predominantly relies on camera-based methods. However, computer vision-based solutions raise severe privacy and ethical concerns. In this paper, we propose a privacy-preserving counting solution called BeamCount based on Wi-Fi sensing. Instead of using conventional Wi-Fi Channel State Information (CSI) readings, we utilize Wi-Fi Beamforming Feedback Information (BFI) for crowd counting estimation. Compared to CSI which can only be extracted from few commodity Wi-Fi cards (e.g., Intel 5300), BFI readings can be obtained from a large range of commodity Wi-Fi devices. We establish a mapping relationship between BFI and headcount and extract headcounts from BFI inputs through a carefully designed adversarial network. Owing to the adversarial network's cross-domain capability, the proposed counting system can achieve high accuracy across different environments, demonstrating its generalization capability. To mitigate the effect of BFI compression on sensing performance, we adopt a novel time series prediction model. Extensive real-world experiments validate the effectiveness of BeamCount in various environments, achieving an average counting accuracy of 93.6%.

With the advancement of Internet of Things technology, the need for sophisticated signal modulation classification has intensified, ensuring seamless communication and bolstering security among interconnected devices. In the contemporary complex channel environment, the difficult lies in dealing with a multitude of modulation schemes that exhibit subtle distinctions. Prior knowledge-guided and deep learning methods have complementary strengths in the current context of signal modulation classification. To synthesize the advantages of these two methods, we propose an integrated method of prior knowledge and contrast feature for signal modulation classification, called APFS. APFS integrates prior knowledge from the modulation task with feature information acquired through contrastive learning. Feature extraction guided by prior knowledge accurately captures the key patterns in modulated signals. Contrastive learning reveals the inherent distinctions among various modulation modes by comparing different samples. In the joint feature extraction approach for prior knowledge, each form of prior knowledge is first analyzed independently, and then jointed to extract information from its temporal sequence. The contrast features surpass the constraints of labeling and unearth deeper implicit information. In experiments, we systematically compared the performance of our method with various baselines, as well as combinations of prior knowledge and contrast feature. The results demonstrate the superior performance of our method.

Advances in communication technology have enabled signal modulation classification (SMC) to be widely used in noncooperative identification situations, such as spectrum detection, electronic countermeasures, and target identification. In the face of complex electromagnetic environments and various classification tasks, the class imbalance phenomenon in modulated signal data sets has become a problem that cannot be ignored. For the SMC based on machine learning, the unbalanced training data set will cause the actual decision boundary to shift, thereby reducing the prediction accuracy of minority signals. And for SMC based on deep learning, unbalanced data will lead to distortion of the feature space and affect the extraction of discriminative features. However, the existing modulation classification methods cannot effectively deal with the imbalance problem. This study introduces an oversampling method tailored for modulation signals. Our method balances the data set by synthesizing new samples according to the distribution of signal samples and the distance between samples, which will effectively reduce the impact of the imbalance problem on the classifier. For modulated signals, experimental results show that our method performs better than other oversampling methods. In addition to the SMC task, we test the performance of the proposed method for individual identification of radiation sources on the aircraft communications addressing and reporting system data set. Compared with other comparison methods, our method improves the classification performance the most.

Seamless Internet of Things (IoT) connections expose many vulnerabilities in wireless networks, and IoT devices inevitably face many malicious active attacks. automatic modulation recognition (AMR) is an effective way to combat IoT physical layer threats. In the field of noncollaborative communication, feature representation learning for unlabeled signals is an important task of AMR. However, due to the unavailability of a priori knowledge and the influence of interference during signal transmission, the intercepted unlabeled signals are difficult to perform efficient feature representation. In this article, we propose cooperative contrast learning for unlabeled modulation signal Cooperative Contrast Learning for modulation Signals (CoCL-Sig). Specifically, the CoCL-Sig is trained using both sequence and constellation diagram modalities, and is divided into two parts: 1) modal-level feature representation and 2) instance-level auxiliary feature representation. In modal-level feature representation, two modal projections are matched in the same hyperplane space. To ensure the stability of the feature representation, a sequence auxiliary branch is added to form an instance-level feature representation of the sequence. In addition, the feature representations obtained by the CoCL-Sig can be applied to modulation signals for semi-supervised classification and clustering tasks. We have conducted extensive experiments on two widely used modulation signal data sets, RML2016.10A and RML2016.04C. The results demonstrate the effectiveness of our method in modulation signal feature representation and its superiority compared to other methods.

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

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

Accurate head movement tracking is crucial for virtual reality and Metaverse in ubiquitous human-computer interaction (HCI) applications. Existing works for head tracking with wearable VR kits and wireless signals require expensive devices and heavy algorithmic processing. To resolve this problem, we propose HeadTrack, a low-cost, high-precision head motion tracking system that uses commercially available wireless earphones to capture the user’s head motion in real-time. HeadTrack uses smartphones as ‘sound anchors’ and emits inaudible chirps picked up by the user’s wireless earphones. By measuring the time-of-flight of these signals from the smartphone to each microphone on the earphone, we can deduce the user’s face orientation and distance relative to the smartphone, enabling us to accurately track the user’s head movement. To realize HeadTrack, we use the cross-correlation method to optimize the Frequency Modulated Continuous Wave (FMCW) based acoustic ranging method, which solves the problem of insufficient wireless earphone bandwidth. Moreover, we solve the problems of asynchronous startup time between devices and the existence of sampling frequency offset. We conduct excessive experiments in real scenarios, and the results prove that HeadTrack can continuously track the direction of the user’s head, with an average error under 6.3° in pitch and 4.9° in yaw.

Transformer-based methods have demonstrated their potential capabilities in ship detection of synthetic aperture radar (SAR) images. However, their exclusive reliance on global context information hinders the precise localization of small ships, leading to suboptimal detection performance. In this work, a global-to-local detection transformer (GL-DETR) framework is proposed to enhance the detection accuracy of small ships in SAR images. In GL-DETR, the decoder is constituted of a global layer and a carefully designed local layer. Within the global layer, object queries fully interact with global context information. Furthermore, a local interaction attention (LIA) module is designed in the local layer, with the purpose of refining and enriching the object query features through local multiscale region of interest (ROI) information. Additionally, a multiscale information enhancement (MIE) module is introduced to enhance the high-frequency information of small ships through Gaussian filtering. Numerical results demonstrate that GL-DETR outperforms baseline by 3.71% and 4.89% on classical SAR datasets LS-SSDD and HRSID, respectively. These results demonstrate the effectiveness and superiority of the proposed strategy.

This letter proposes a closed-form design method for an infinite impulse response (IIR) digital approximation of the fractional-order operator$s^{\nu }$,$\nu \in (-1,1)$, with improved performance around a predefined low frequency$\beta$,$0 < \beta < 1$. The proposed method utilizes a modified indirect discretization strategy, where it generates a rational$s$-domain expression using the continued fraction expansion (CFE) of a scaled operator$(s/\beta)^{\nu }$to minimize the truncation error around$\beta$. The$s$-domain expression is then discretized using a well-matched$s$-to-$z$transformation and scaled back by$\beta ^{\nu }$to fit$s^{\nu }$. The new approximations are compared with recently-proposed models by simulation in terms of frequency response as well as QRS complex detection. The proposed method shows a great potential to attain low-order accurate models that can be immediately used for hardware realizations in a wide range of applications.

In this article, we explore intercity mobility in urban agglomerations by surveying people traveling across cities based on private car trajectory data. Specifically, we first adopt the statistical analysis method to mine the intercity mobility in terms of various metrics of travel trips, so as to gain a preliminary understanding of intercity mobility in urban agglomeration. Then, we utilize the tensor decomposition method to conduct in-depth study on the intercity mobility pattern from the perspectives of complexity and multidimensionality. We construct a 4-D tensor based on private car trajectory and point-of-interest (POI) datasets and define the functional similarity and geographic adjacency between regions. Finally, we design an alternating proximal gradient (APG)-based method to resolve the core tensor and factor matrix, leading to the fine-grained discovery of intercity mobility patterns on administrative divisions in the urban agglomeration. Extensive experiments are conducted to evaluate the analysis of intercity mobility, using a real-world dataset containing one-year private car trajectories from five cities in the selected urban agglomeration. The experiments show that the proposed method successfully captures 20 intercity mobility patterns, in which the factor matrices retrieve the patterns from different dimensions with core tensors characterizing correlations between patterns in factor matrices. Besides, the extracted intercity mobility patterns not only cover administrative areas with frequent intercity interactions, but also contain areas with less intercity interactions. It validates that the intercity mobility is consistent with the regional functions in urban agglomeration.

With the development of deep learning and the increase in the amount of data, general artificial intelligence models have become a popular research area nowadays. When facing a new application scenario, a pretraining general model can often show better performance than models trained with new data on its own. However, because of the specificity of the differences in hyperspectral image data bands, the current hyperspectral image classification (HSIC) field has not proposed a better general model training solution, and it is difficult to utilize the information of the existing hyperspectral datasets for model training in the face of a new scenario. In order to solve this problem, this article proposes a generalized hyperspectral classification model training method, which effectively completes the training of hyperspectral classification models across datasets by adaptive channel module and masked self-supervised pretraining method, and can pretrain and fine-tune hyperspectral classification models using multiple datasets. The adaptive channel module is able to solve the band difference problem of using hyperspectral datasets across datasets, and the masked self-supervised learning method solves the label difference and labeling difficulties of training models across datasets. Experimental results on multiple datasets show that the method proposed in this article can effectively use a large amount of data to complete the pretraining of hyperspectral classification models, and the fine-tuning results on downstream datasets have certain advantages relative to current advanced deep learning methods.

The continual progression and expanding applications of Hyperspectral Imaging (HSI) technology necessitate the development of lightweight HSI classification models that are capable of lifelong learning. However, the computationally-demanding task of training and updating HSI classification models, exacerbated by the substantial number of trainable parameters in feature extractors, remains a substantial challenge. This paper proposes an Attention-based Reservoir Computing (ARC) model to overcome these hurdles. The ARC model utilizes a cross-slicing operation to generate multi-directional inputs, treating the HSI dataset as spatial sequence for processing within a reservoir. For every target pixel, four spatial sequences from various directions are introduced into the reservoir, generating four corresponding outputs. A voting mechanism then evaluates these outputs to yield the final prediction. Additionally, we design an attention-based leaky function for reservoir computing to capture the spatial correlation inherent in HSI data accurately. The attention-based leaky function enables the reservoir state to weigh less on the pixels outside the region of interest (ROI) and have a longer memory for pixels inside the ROI. The ARC was tested on widely used HSI datasets: Indian Pines, PaviaU, and Salinas. It demonstrated competitive lightweight classification performance against state-of-the-art lightweight models by maintaining comparable training time while achieving superior accuracy. Furthermore, the model’s lifelong learning accuracy also showed superior performance compared to existing lifelong learning models, with a one thousand times reduction of parameter-to-be-updated. This work makes the ARC model an effective contender for HSI classification tasks, excelling in both lightweight classification and lifelong learning capacities. The source codes are available publicly at: https://github.com/Waterman-Ann/ARC.

The electromagnetic environments have become more complex with the development of wireless communication technology. Signal modulation classification has attracted extensive attention due to its application in electronic countermeasures and physical layer security threat prevention under complex electromagnetic environments. Excellent classification performance requirements challenge the adaptability of the method and the ability to extract modulation characteristics. This paper proposes an automated machine learning framework, AutoSMC, for signal modulation classification. An adaptive signal augmentation method is proposed to adapt to the network changes during the search process. In order to extract the modulation features effectively, an scalable convolutional random fourier feature block is proposed. Moreover, the initial search space of the framework is given. The Bayesian Optimization is used to drive hyperparameter optimization to achieve AutoSMC and obtain the optimal method state. Great experiments were carried out on RADIOML 2016.10A and RADIOML 2016.10B. Experimental evaluations on these datasets show that our approach AutoSMC achieves state-of-the-art results compared to the most relevant signal modulation classification methods.

Trajectory recovery aims to restore missing data for reconstructing high-quality human mobility trajectory, which benefits a wide range of intelligent transportation system applications ranging from urban planning to travel recommendation. Inspired by the inherent regularity of human mobility, existing approaches capture spatial-temporal transition regularities in historical trajectory for data recovery. Although promising, existing solutions suffer from two limitations.i)These methods fail to recover occasionally-visited points (OVP) due to the lack of semantic information when learning spatial-temporal transition regularities.ii)The information before and after missing data is not be fully utilized for trajectory recovery. To overcome the limitations, we propose a novel semantic-aware trajectory recovery framework. First, we leverage heterogeneous information network (HIN) to encode various semantic correlations for obtaining rich semantic embeddings, which are fused with temporal information to form spatial-temporal semantic context. Then, we develop a behavior attention mechanism to capture semantic behavior transition regularities for trajectory recovery based on the bidirectional spatial-temporal semantic context before and after missing data. Extensive experiments on four real-world datasets show that our proposed method outperforms the state-of-the-arts by 7%-11% in term of recall, F1-score and mean average precision.

Vessel trajectory clustering, which aims to find similar trajectory patterns, has been widely leveraged in maritime applications. Most traditional methods use predefined rules and thresholds to identify discrete vessel behaviors. They aim for high-quality clustering and conduct clustering on entire sequences, whether the original trajectory or its sub-trajectories, overlooking the behavioral significance and evolution characteristics. To resolve this problem, we propose a Predictive Clustering of Hierarchical Vessel Behavior (PC-HiV). PC-HiV first utilizes hierarchical representations to transform every trajectory into a behavioral sequence. It then predicts evolution at each timestamp of the sequence based on the representations. By applying predictive clustering and latent encoding, PC-HiV improves clustering and predictions simultaneously. Experiments conducted on real AIS datasets demonstrate that PC-HiV effectively captures behavioral evolution discrepancies between different vessel types (tramp vs. liner) and near emission control area boundaries. Additionally, the results show that PC-HiV outperforms NN-Kmeans and Robust DAA by 3.9% and 6.4% in terms of purity scores, thereby proving the superiority of the proposed PC-HiV over existing models.

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

Accurate perception of intelligent vehicles is critical for the safe operation of autonomous vehicles. However, current perception methods often struggle to effectively detect intelligent vehicles when obstacles block their field of view. Collaborative perception, although attracting considerable attention, presents challenges in terms of privacy and data trust. In this study, we present a novel design for Enhancing Intelligent Vehicle (), a cost-effective and comprehensive perception system for intelligent vehicles. We discovered that during the process of memory caching raw sensing data in the intelligent vehicle’s system-on-chip (SOC), continuous fluctuating currents inside the memory result in the emission of Electromagnetic Radiation (EMR). As a result, intelligent vehicles actively expose themselves on the electromagnetic spectrum. is based on a set of specially designed antenna arrays that scan the spectrum and utilize a joint Kalman filtering algorithm to enhance EMR signals. The micro-Doppler signature of each EMR signal is then analyzed to identify signals from intelligent vehicles and construct a vehicle database. A multi-antenna joint estimation algorithm is also designed to further estimate the position, distance, and direction of the target vehicle. Our experiments demonstrate that offers advantages in terms of timeliness, robustness, and accuracy.

Vehicular edge computing (VEC) provides an effective task offloading paradigm by pushing cloud resources to the vehicular network edges, e.g., road side units (RSUs). However, overloaded RSUs are likely to occur especially in urban aggregation areas, possibly leading to greatly compromised offloading performance. Inspired by this, this article explores this situation by introducing an unmanned aerial vehicle (UAV) to address the VEC overload problem. Specifically, we formulate a novel online UAV-assisted vehicular task offloading problem to minimize vehicular task delay under the long-term UAV energy constraint. To solve the formulated problem, we first decouple the long-term energy constraint based on the Lyapunov optimization technique. In this way, the problem can be solved in a real-time manner without requiring future information. Then, we construct a Markov chain based on Markov approximation optimization to find out the close-to-optimal UAV-assisted offloading strategies. Furthermore, we derive a mathematical analysis to rigorously demonstrate the offloading performance of the proposed algorithm. Additionally, the simulation results show that the proposed method outperforms the baselines by significantly reducing the vehicular task delay constrained by the long-term UAV energy budget under various system parameters, such as the energy budget and computation workloads.

Next destination recommendation is a crucial research area for understanding human travel behavior. However, existing studies often overlook the problem of underfitting, which arises due to the limited regularity in users' travel patterns. To tackle this issue, we leverage diverse co-occurrence patterns (CoPs) to discover potential user preferences. These patterns capture intersections with similar spatial and temporal characteristics in users' travels. However, traditional graph neural network (GNN)-based approaches struggle to effectively handle complex spatial-temporal CoPs. To overcome these challenges, we propose a novel framework called DHIN (Dynamic Heterogeneous Information Network). Firstly, to address the problem of underfitting, DHIN constructs intricate CoPs by leveraging abundant features and connection relationships. Additionally, to solve the needs of cold-start users, DHIN generates potential connections by capturing dynamic urban hotspots based on global users' travel trajectories. Moreover, to model dynamic heterogeneous information, DHIN utilizes a hierarchical attention mechanism and integrates a dynamic encoder. The mechanism integrates multi-level attention to learn informative embeddings from heterogeneous attributes and structures, while the dynamic encoder processes dynamic temporal information for updating node representations. Finally, extensive experiments conducted on real-world trajectory data demonstrate the effectiveness of the proposed DHIN model.

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

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

Passenger counting is crucial for many applications such as vehicle scheduling and traffic capacity assessment. However, most of the existing solutions are either high-cost, privacy invasive or not suitable for passengers the vehicle scenarios. In this work, we propose thePa-Count, an effective real-timePassengerCounting system deployed inside the vehicle via using Wi-FiCSI(Channel State Information). Specifically, in Pa-Count, we design a set of combined filters to eliminate environmental interference and enhance CSI quality. In so doing, we can identify the fluctuation of weak CSI caused by passengers’ subtle movement, i.e., the fidgeting, and then obtain the distribution of fidgeting period and silent period. Following that, we describe the subtle movements of passengers via power law with exponential cutoff distribution and establish a counting model based on the queuing theory. A mathematical inference method with a priori probability is devised to calculate the number of real-time passengers through CSI. We evaluate the performance of the Pa-Count by conducting a set of experiments in real-world vehicle scenarios (including private car and subway). Experimental results show that Pa-Count can achieve robust performance with an average accuracy of over 92$\%$.

In connected autonomous vehicles (CAVs), the driving safety can be greatly deteriorated, in the presence of occlusions which are adverse to CAVs' perception of region-of-interest (RoI). Collaborative perception on the basis the information sharing of occlusions among CAVs, in a real-time and accurate manner, provides a means of the occlusion-free RoI perception for safe driving. In this paper, we propose a novel framework ofCollaborativeOcclusion-freePerception (COFP) in CAVs, to regain the real-time and accurate occlusion awareness. The innovative COFP targets two goals: well-balanced computation resource allocation, as well as fast and high-quality RoI information fusion. Specifically, the resource allocation problem, with the objective of minimizing CAVs' completion delay, is formulated as a multi-player continuous potential game and solved by a better response dynamics (BRD) algorithm. The RoI information fusion, with the objective of maximizing the overall object depiction quality, is formulated as a combinatorial optimization problem, and solved by a modified discrete salp swarm (MDSSA) algorithm. Experimental results show that the proposed COFP with 5GHz computing power can achieve full occlusion awareness for CAVs with 69.61% completion time reduction and 19.03% fusion quality improvement, compared to the existing methods.

Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7% and 39.3% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.

In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recognize object instances using only image-level labels, which greatly saves the labeling costs of RSIs. In this article, we propose a self-directed weakly supervised strategy (SD-WSS) to perform WSOL in RSIs. To specify, we fully exploit and enhance the spatial feature extraction capability of the RSIs' classification model to accurately localize the objects of interest. To alleviate the serious discriminative region problem exhibited by previous WSOL methods, the spatial location information implicit in the classification model is carefully extracted by GradCAM++ to guide the learning procedure. Furthermore, to eliminate the interference from complex backgrounds of RSIs, we design a novel self-directed loss to make the model optimize itself and explicitly tell it where to look. Finally, we review and annotate the existing remote sensing scene classification dataset and create two new WSOL benchmarks in RSIs, named C45V2 and PN2. We conduct extensive experiments to evaluate the proposed method and six mainstream WSOL methods with three backbones on C45V2 and PN2. The results demonstrate that our proposed method achieves better performance when compared with state-of-the-arts.

The task of hyperspectral image (HSI) classification has attracted extensive attention. The rich spectral information in HSIs not only provides more detailed information but also brings a lot of redundant information. Redundant information makes spectral curves of different categories have similar trends, which leads to poor category separability. In this article, we achieve better category separability from the perspective of increasing the difference between categories and reducing the variation within category, thus improving the classification accuracy. Specifically, we propose the template spectrum-based processing module from spectral perspective, which can effectively expose the unique characteristics of different categories and reduce the difficulty of model mining key features. Second, we design an adaptive dual attention network from spatial perspective, where the target pixel can adaptively aggregate high-level features by evaluating the confidence of effective information in different receptive fields. Compared with the single adjacency scheme, the adaptive dual attention mechanism makes the ability of target pixel to combine spatial information to reduce variation more stable. Finally, we designed a dispersion loss from the classifier's perspective. By supervising the learnable parameters of the final classification layer, the loss makes the category standard eigenvectors learned by the model more dispersed, which improves the category separability and reduces the rate of misclassification. Experiments on three common datasets show that our proposed method is superior to the comparison method.

Mobile edge computing emerges to serve mobile users with low-latency computation offloading in edge networks, which are resource-constrained with massive users and workloads. However, existing communication and computing resource allocation schemes for offloaded tasks aren't efficient enough, where finished tasks still occupy resources, wasting constrained resources. Besides, the multi-user offloading is usually for scenarios of one task per user, ignoring real-worldmulti-taskoffloading scenarios where each user has multiple tasks, lack generality and flexibility. Meanwhile, local computing resource allocation schemes in multi-task scenarios ignore resource readjustment, causing low resource utilization. To solve these problems, we propose ECO-GAME, an efficient multi-task offloading scheme, which dynamically allocates bandwidth and computing resources to unfinished tasks, resulting in high resource utilization. We initially formulate the multi-task offloading problem as the game minimizing each user's cost, which is NP-hard. Thus we re-formulate the game utilizing potential games to optimize user's objective either locally or globally, and prove the existence of its Nash equilibrium. We then design an efficient multi-task offloading algorithm to obtain an approximate solution in polynomial time, together with computational complexity analysis. We further conduct performance evaluation on ECO-GAME utilizing price of anarchy. Numerical results demonstrate the efficiency of ECO-GAME, and show ECO-GAME reduces 49.2% cost over the state-of-the-art work, and scales well with the increasing number of tasks and users.

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

Vehicle-to-vehicle (V2V) computation offloading has emerged as a promising solution to facilitate computing-intensive vehicular task processing, where task vehicles (i.e., TaVs) will be requested to offload computing-intensive tasks to server vehicles (i.e., SeVs) in order to keep task delay low. However, it is challenging for TaVs to obtain the optimal V2V computation offloading decisions (i.e., realizing the minimal task delay) due to the constraints, including: 1) incomplete offloading information; 2) degraded Quality-of-Service (QoS) of SeVs; and 3) privacy leakage risks. In this article, we develop a learning-based V2V computation offloading algorithm enhanced by SeV’s ability & trustfulness awareness to solve these problems. We emphasize that the proposed algorithm learns the offloading performance of candidate SeVs based on history offloading selections, without requiring the complete offloading information in advance. Additionally, both the QoS of SeVs and safe V2V computation offloading are enhanced in the proposed learning-based algorithm. Furthermore, we conduct extensive simulation experiments to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces the average task delay by 35% and 40%, and at the same time decreases the learning regret by 39% and 41%, compared to the algorithms without SeV’s ability and trustfulness awareness.

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.

Autonomous driving has so far received numerous attention from academia and industry. However, the inevitable occlusion is a great menace to safety and reliable driving. Existing works have primarily focused on improving the perception ability of a single autonomous vehicle (AV), but the safety problem brought by occlusions remains unanswered. In this paper, we propose a multi-tier perception task offloading framework with a collaborative computing approach, where an AV is able to achieve a comprehensive perception of the concerned region-of-interest (RoI) by leveraging collaborative computation with nearby AVs and road side units (RSUs). Besides, the collaborative computation provides offloading service for computationally intensive tasks so as to reduce processing delay. Specifically, we formulate a joint problem of perception task assignment, offloading and resource allocation, by fully considering the AV’s mobility, task dependency, and delay requirement. The collaborative offloading is modeled as a mixed-integer nonlinear programming (MINLP) problem. We design a two-layer binary intelligent firefly (TL-BIFA) algorithm to solve MINLP, with the goal of minimizing execution delay. The proposed TL-BIFA synthesizes the advantages of heuristic methods and deterministic methods. Through extensive simulations, the proposed collaborative offloading approach and the TL-BIFA show superiority in enhancing the autonomous driving system’s safety, efficiency and resource utilization.

Achieving reliable intervehicle positioning is one of the most fundamental elements for many vehicular applications, including collision avoidance and autonomous driving. Vehicle position is generally provided by a global navigation satellite system (GNSS), which unfortunately suffers from inaccuracy to varying degrees in challenging environments, for example, GNSS outages. In this article, a reliable fusion technique, called non-Gaussian Redheffer weighted least squares ( n GRWLSs), is proposed. This new approach highlights the intervehicle positioning estimation in multi-GNSS outage environments, such as complete, partial, and free GNSS pseudorange outages. The proposed method combines, on the one hand, the benefits of the Gaussian dynamical matrix principle and the Redheffer distribution function for the sparse property in complete GNSS pseudorange outages and, on the other hand, the use of the optimal window size to regulate the data flow generated by both the inertial navigation systems (INSs) and GNSS during a partial GNSS pseudorange outage. During the free GNSS pseudorange outage, the process ignores data from the INS, and instead, GNSS pseudorange information alone will be considered to compute the intervehicle positioning information. Consequently, weighted least squares is used as an intervehicle positioning estimator. To address the pseudorange uncommon and INS measurement noises, the generalized error distribution (GED) is used to estimate the non-Gaussian densities. Finally, road-test experiments are implemented to evaluate the consistency of the proposed approach. The experimental results show that the proposed n GRWLS can accurately estimate the intervehicle positioning under various conditions (free, partial, and complete GNSS pseudorange outages).

Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose a deep learning framework for a spatiotemporal attention network (STANet) with a neural algorithm logic unit (NALU), the so-called STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. Specifically: 1) we design an improved kernel density estimator (KDE) by defining a log-cosh loss function to calculate the spatial distribution of the aggregation effect with guaranteed robustness and 2) we utilize the stay time of private cars as a temporal feature to represent the nonlinear temporal correlation of the aggregation effect. Next, we propose a spatiotemporal attention module that separately captures the dynamic spatial correlation and nonlinear temporal correlation of the private car aggregation effect, and then we design a gate control unit to fuse spatiotemporal features adaptively. Further, we establish the STANet-NALU structure, which provides the model with numerical extrapolation ability to generate promising prediction results of the private car aggregation effect on weekends. We conduct extensive experiments based on real-world private car trajectories data. The results reveal that the proposed STANet-NALU outperforms the well-known existing methods in terms of various metrics, including the mean absolute error (MAE), root mean square error (RMSE), Kullback-Leibler divergence (KL), and R2.

Hyperspectral image classification (HSIC) has attracted widespread attention due to its important application in environment alterations and geophysical disaster monitoring. However, surface cultivation is not static as time passes, which leads to different hyperspectral images information collected from the same area at different time periods. Therefore, researchers are currently eager to construct a HSIC model that continuously acquires new classes of data. During the continuous learning process, the model is expected to not only effective in extracting unique spatial-spectral features of the hyperspectral image, but also ensures the ability to maintain the old classes knowledge while learning new data. To achieve this purpose, we propose a method which based on geometric spatial-spectral feature integration network with class incremental learning (GS2FIN-CIL) framework in continuous learning to make the model adaptable to new classes data and not overly forgetting the old classes knowledge during the training process. We conduct extensive experiments with the proposed GS2FIN-CIL method on widely-used hyperspectral datasets including Indian Pines, PaviaU and Salinas. The experimental results show that our GS2FIN-CIL method can achieve significantly improved results compared to current state-of-the-art class incremental learning methods, allowing for efficient adaptation and utilization of spatial-spectral features in processing new classes of hyperspectral images and alleviating the problem of catastrophic forgetting of learned old classes knowledge. The GS2FIN-CIL method could be successfully applied to the challenge of adding new classes data in HSIC task.

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

In enterprise management systems (EMS), augmented Intelligence of Things (AIoT) devices generate delay-sensitive and energy-intensive tasks for learning analytics, articulate clarifications, and immersive experiences. To guarantee effective task processing, in this work, we present a cloud-assisted fog computing framework with task offloading and service caching. In the framework, tasks make offloading decisions to determine local processing, fog processing, and cloud processing with the goal of minimal task delay and energy consumption, conditioned on dynamic service caching. To this end, we first propose a distributed task offloading algorithm based on noncooperative game theory. Then, we adopt the 0–1 knapsack method to realize dynamic service caching. At last, we adjust the offloading decisions for the tasks offloaded to the fog server but without caching service support. In addition, we conduct extensive experiments and the results validate the effectiveness of our proposed algorithms.

Urban region heat refers to the extent of which people congregate in various regions when they travel to and stay in a specified place. Predicting urban region heat facilitates broad applications ranging from location-based services to intelligent transportation management. The region heat is essentially characterized by the ‘arrive-stay-leave (ASL)’ behaviors, while it is a challenging task to well capture the spatial-temporal evolution of region heat since the following issues remain: i) ASL behaviors of private cars is usually heterogeneous resulting in a hierarchical distribution of region heat. ii) Urban region heat contains complex spatial-temporal correlations hidden in ASL behaviors and how to collaboratively integrate them is challenging. To address these challenges, we propose a Hierarchical Spatial-Temporal Network (HierSTNet) to forecast urban region heat, which contains two representations, namely, grid region from micro perspective and node region from macro perspective. For the grids, three-dimension spatial and temporal convolutional network (3D-STCNN) is proposed to model multi-scale properties in temporal dimension of ASL behaviors. For the nodes, multi-head graph attention networks are utilized to model the periodicity and spatial heterogeneity among macro region. Hierarchical structures are designed for multi-view modeling spatial-temporal distribution of ASL behaviors, by which they capture small-scale features in micro regions and embeds the global representation into graph propagation. Finally, we design an interaction decoder layer to integrate the external factors and aggregate spatial-temporal information across hierarchical structures. Extensive experiments based on real-world private car trajectory dataset demonstrate the superiority and effectiveness of proposed framework.

We consider the problem of task offloading and resource allocation in mobile edge computing (MEC). To maintain satisfactory quality of experience (QoE) of end-users, mobile devices (MDs) may offload their tasks to edge servers based on the allocated computation (e.g., CPU/GPU cycles and storage) and wireless resources (e.g., bandwidth). However, these resources could not be effectively utilized unless an encouraging resource allocation scheme can be proposed. What’s worse, task offloading incurs additional MEC energy consumption, which inevitably violate the long-term MEC energy budget. Considering these two challenges, we propose an online joint offloading and resource allocation (JORA) framework under the long-term MEC energy constraint, aiming at guaranteeing the end-users’ QoE. To achieve this, we leverage Lyapunov optimization to exploit the optimality of the long-term QoE maximization problem. By constructing an energy deficit queue to guide energy consumption, the problem can be solved in a real-time manner. On this basis, we propose online JORA methods in both centralized and distributed manners. Furthermore, we prove that our proposed methods enable the achievement of the close-to-optimal performance while satisfying the long-term MEC energy constraint. In addition, we conduct extensive simulations and the results show superiority in performance over other methods.

In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

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

We consider the problem of dependent task offloading in edge computing with unknown system-side information (e.g., edge transmission rate and computation resources). In this problem, tasks have complicated dependency relationships and have no prior knowledge of system-side information to assist offloading decision-making. Although existing learning-based approaches can help to address unknown system-side information, the impact of inherent task dependency on such approaches has not been formally explored. To bridge the gap, we first use a breadth-first-search (BFS) method to decouple task dependency, and then leverage the Lyapunov optimization technique to transfer the long-term offloading problem to an online optimization problem. Furthermore, we employ the multi-armed bandit (MAB) theory to develop theonlinelearning-baseddependenttaskoffloading algorithm, called OL-DTO. The algorithm can address the unknown system-side information and is augmented with task dependency awareness. We present a rigorous theoretical analysis to evaluate the performance of this algorithm in terms of application delay and UD energy consumption. Our extensive experimental results demonstrate that the OL-DTO algorithm significantly reduces application delay while satisfying the long-term energy budget constraint of the UD.

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.

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

In recent years, unmanned aerial vehicles (UAVs) have been widely used in various industries (e.g., search and rescue, express delivery, etc.) due to their high flexibility. In addition, the deployment of UAVs equipped with mobile-edge computing (MEC) servers to provide computing services at the edges of networks has become an emerging method. Under complex and limited resource constraints, increasing the total number of computation bits in the system becomes a challenging problem. Motivated by this, in this article, we propose an optimization framework to maximize the computation bits of the whole system by jointly optimizing the bandwidth allocation, the task offloading time allocation, and the trajectory of the UAV under the energy constraints of ground devices (GDs) and the maximal battery energy of the UAV. The formulated problem is a nonconvex and nonconcave problem that is very difficult to solve. To this end, we decompose the objective function into three suboptimization problems and adopt successive convex optimization techniques to solve them. Then, we utilize the block coordinate descent (BCD) algorithm to address the overall optimization problem. By doing so, the bandwidth allocation of GDs, task offloading time and local computing time allocation in each time slot, and the trajectory of the UAV are optimized alternately during each iteration. We conduct extensive simulations, and the results verify that the proposed solution achieves a better performance than those of other benchmark schemes.

With the rapid development of various applications in the Internet of Things (IoT), we have witnessed much progress with very wide differences in characteristics and requirements. In this article, we propose a novel dynamic channel assembling (DChA) strategy for channel access of heterogeneous secondary user (SU) flows in IoT-oriented cognitive radio networks (CRNs), making use of the priority queues based on fine-grained flow classification. Specifically, three categories of SU flows are considered, so-called the real-time SU (RSU) flows, the elastic large SU flows, and the elastic small SU flows. On top of this, channel access opportunities are distributed to the SU flows in three specially designed queues performing the channel access algorithm. The highlight of our main idea is that the RSU flows with higher priority are only supposed to assemble as few channels as possible, so long as their minimum requirements are fulfilled, thereby minimizing the impact on elastic SU (ESU) traffic. For the sake of performance evaluation, we utilize the continuous-time Markov chain to model our proposed strategy and conduct theoretical analyses. With the detailed theoretical analyses and extensive simulations, the proposed DChA strategy is demonstrated to be able to fulfill the deadline of SU flows, while significantly reducing the blocking probability as well as the completion time of the ESU flows.

In this paper, we investigate the precoder design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system with one-bit digital-to-analog converters (DACs). In order to form the dual-functional beam-pattern, we formulate the precoding problem as a weighted optimization problem with the constant modulus constraint, which aims at minimizing the average error power and guaranteeing radar waveform similarity. The problem is divided into three sub-problems corresponding to the multiple variables, i.e., the precoding factor, transmit signal matrix, and radar waveform matrix. Due to the discrete and non-convex properties of the optimization problem, we propose a multi-variable alternating minimization (MVAM) framework to achieve the near-optimal solutions. The precoding factor and radar waveform can be solved in closed-forms. For the transmit signal matrix, we devise a binary particle swarm optimization-simulated annealing (BPSO-SA) algorithm to obtain it under the MVAM framework. Extensive simulations validate the effectiveness of the proposed approach under various scenarios, including the case without perfect channel state information. The simulation results show that, compared with existing non-linear precoders, the proposed approach achieves 7dB SNR gain at the bit error rate of 10−4in the 8-antenna system, and the gain of SNR is 0.2dB in the massive MIMO system with 128 antennas.

Convolutional neural networks (CNNs) have achieved remarkable performance in driver drowsiness detection based on the extraction of deep features of drivers' faces. However, the performance of driver drowsiness detection methods decreases sharply when complications, such as illumination changes in the cab, occlusions and shadows on the driver's face, and variations in the driver's head pose, occur. In addition, current driver drowsiness detection methods are not capable of distinguishing between driver states, such as talking versus yawning or blinking versus closing eyes. Therefore, technical challenges remain in driver drowsiness detection. In this article, we propose a novel and robust two-stream spatial-temporal graph convolutional network (2s-STGCN) for driver drowsiness detection to solve the above-mentioned challenges. To take advantage of the spatial and temporal features of the input data, we use a facial landmark detection method to extract the driver's facial landmarks from real-time videos and then obtain the driver drowsiness detection result by 2s-STGCN. Unlike existing methods, our proposed method uses videos rather than consecutive video frames as processing units. This is the first effort to exploit these processing units in the field of driver drowsiness detection. Moreover, the two-stream framework not only models both the spatial and temporal features but also models both the first-order and second-order information simultaneously, thereby notably improving driver drowsiness detection. Extensive experiments have been performed on the yawn detection dataset (YawDD) and the National TsingHua University drowsy driver detection (NTHU-DDD) dataset. The experimental results validate the feasibility of the proposed method. This method achieves an average accuracy of 93.4% on the YawDD dataset and an average accuracy of 92.7% on the evaluation set of the NTHU-DDD dataset.

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred in the amount of data obtained by advanced remote sensors. Inevitably, new data classes and refined categories appear continuously, and such data are limited in terms of the timeliness of application. These characteristics motivate us to build an HSI classification model that learns new classifying capability rapidly within a few shots while maintaining good performance on the original classes. To achieve this goal, we propose a linear programming incremental learning classifier (LPILC) that can enable existing deep learning classification models to adapt to new datasets. Specifically, the LPILC learns the new ability by taking advantage of the well-trained classification model within one shot of the new class without any original class data. The entire process requires minimal new class data, computational resources, and time, thereby making LPILC a suitable tool for some time-sensitive applications. Moreover, we utilize the proposed LPILC to implement fine-grained classification via the well-trained original coarse-grained classification model. We demonstrate the success of LPILC with extensive experiments based on three widely used hyperspectral datasets, namely, PaviaU, Indian Pines, and Salinas. The experimental results reveal that the proposed LPILC outperforms state-of-the-art methods under the same data access and computational resource. The LPILC can be integrated into any sophisticated classification model, thereby bringing new insights into incremental learning applied in HSI classification.

Hyperspectral images (HSIs) have gained high spectral resolution due to recent advances in spectral imaging technologies. This incurs problems, such as an increased data scale and an increased number of bands for HSIs, which results in a complex correlation between different bands. In the applications of remote sensing and earth observation, ground objects represented by each HSI pixel are composed of physical and chemical non-Euclidean structures, and HSI classification (HIC) is becoming a more challenging task. To solve the above problems, we propose a framework based on a deep attention graph convolutional network (DAGCN). Specifically, we first integrate an attention mechanism into the spectral similarity measurement to aggregate similar spectra. Therefore, we propose a new similarity measurement method, i.e., the mixed measurement of a kernel spectral angle mapper and spectral information divergence (KSAM-SID), to aggregate similar spectra. Considering the non-Euclidean structural characteristics of HSIs, we design deep graph convolutional networks (DeepGCNs) as a feature extraction method to extract deep abstract features and explore the internal relationship between HSI data. Finally, we dynamically update the attention graph adjacency matrix to adapt to the changes in each feature graph. Experiments on three standard HSI data sets, namely, the Indian Pines, Pavia University, and Salinas data sets, demonstrate that the DAGCN outperforms the baselines in terms of various evaluation criteria. For example, on the Indian Pines data set, the overall accuracy of the proposed method achieves 98.61% when the training sample is 10%.

In the field of hyperspectral image (HSI) classification, deep learning has helped achieve great successes. However, most of these achievements are made with very large amounts of labeled training data. Manual annotation of HSIs is labor intensive and time consuming. In practical HSI classification, there may only be a few labeled samples available. To perform HSI classification with a small number of labeled samples, a new few-shot classification model based on adaptive subspaces and featurewise transformation is proposed in this article. First, we design a 3-D local channel attention residual network to obtain the spatial–spectral features of HSIs. Then, a featurewise transformation strategy is introduced to enhance feature diversity to avoid model overfitting problems and to mitigate the impact of cross-domain problems. Finally, a subspace classifier is implemented to construct different subspace categories based on the embedded features of the limited labeled samples. Classification of an HSI sample is performed using spatial projection and a distance metric. The proposed model is trained using the metalearning mechanism to perform few-shot classification of HSIs. Four public datasets are utilized to construct a sufficient few-shot classification task named episodes for training. The other three public datasets are used to test the proposed model. Experiments show that our proposed method can outperform mainstream small sample HSI classification methods.

Recently, optical remote-sensing images have been steadily growing in size, as they contain massive data and complex backgrounds. This trend presents several problems for object detection, for example, increased computation time and memory consumption and more false positives due to the complex backgrounds of large-scale images. Inspired by deep neural networks combined with time-frequency analysis, we propose a time-frequency analysis-based object detection method for large-scale remote-sensing images with complex backgrounds. We utilize wavelet decomposition to carry out a time-frequency transform and then integrate it with deep learning in feature optimization. To effectively capture the time-frequency features, we propose a feature optimization method based on deep reinforcement learning to select the dominant time-frequency channels. Furthermore, we design a discrete wavelet multiscale attention mechanism (DW-MAM), enabling the detector to concentrate on the object area rather than the background. Extensive experiments show that the proposed method of learning from time-frequency channels not only solves the challenges of large-scale and complex backgrounds, but also improves the performance compared to the original state-of-the-art object detection methods. In addition, the proposed method can be used with almost all object detection neural networks, regardless of whether they are anchor-based or anchor-free detectors, horizontal or rotation detectors.

The graph-based hyperspectral image classification (HSIC) method has attracted wide attention because it can extract information with a non-Euclidean structure. Many graph-based HSIC works have achieved good results, but unresolved technical issues remain. For example, many graph nodes lead to high computational costs, and the mining of non-Euclidean structures is not sufficient. To solve these problems, we propose a graph attention network with an adaptive graph structure mining (GAT-AGSM) approach. Specifically, we first propose an HSIC framework with a superpixel feature subdivision (SFS) mechanism. In this framework, the number of nodes in the graph structure is reduced by using superpixel segmentation algorithms, and the SFS mechanism is designed to generate finer classification results. Second, we design the spatial–spectral attention layer with an adaptive graph structure mining (AGSM) mechanism for the graph attention network. The spatial–spectral attention layer can filter information in both spatial and spectral dimensions. The AGSM mechanism requires less manual intervention to dynamically generate non-Euclidean graph structures that better aggregate information. We conduct excessive experiments to compare the proposed GAT-AGSM with seven nongraph methods and three graph-based methods on widely used datasets. On the Indian Pines, Pavia University, and Salinas datasets, compared to the comparison method, the overall accuracy of GAT-AGSM is improved by at least 4.26%, 2.59%, and 1.41%, respectively. Experimental results show that GAT-AGSM has the best performance compared to the baselines in terms of various metrics.

Deep learning has become a mainstream method of hyperspectral image (HSI) classification. Many DL-based methods exploit spatial-spectral features to achieve better classification results. However, due to the complex backgrounds in HSIs, existing methods usually show unsatisfactory performance for the class pixels located on the land-cover category boundary area. In large part, this is because the network is susceptible to interference by the irrelevant information around the target pixel in the training stage, resulting in inaccurate feature extraction. In this paper, a new multibranch transformer architecture (SST-M) that assembles spatial attention and extracts spectral features is proposed to address this problem. The transformer model has a global receptive field and thus can integrate global spatial position information in the HSI cube. Meanwhile, we design a spatial sequence attention model to enhance the useful spatial location features and weaken invalid information. Considering that HSIs contain considerable spectral information, a spectral feature extraction model is designed to extract discriminative spectral features, replacing the widely used PCA method and obtaining better classification results than it. Finally, inspired by semantic segmentation, a mask prediction model is designed to classify all of the pixels in the HSI cube; this guides the neural network to learn precise pixel characteristics and spatial distributions. To verify the effectiveness of our algorithm (SST-M), quantitative experiments were conducted in three well-known datasets, namely, IP, PU, and KSC. The experimental results demonstrate that the proposed model achieves better performance than the other state-of-the-art methods.

In recent years, hyperspectral image classification (HIC) algorithm based on deep learning has been widely studied, and has achieved much better results than traditional algorithms. HIC using small samples has gradually become a research hotspot, and the generative adversarial networks (GANs) have become a brilliant application in this field. However, the HIC results based on GAN methods are poor and volatile, since a single loss function cannot accurately measure the distance between the generated samples and the real samples in different hyperspectral images. To resolve this problem, we propose a novel immune evolutionary generative adversarial network (HIEGAN) via leveraging the evolutionary strategy and immune strategy. Specifically, we enhance the performance of the generator in two ways: 1) HIEGAN uses multiple loss functions for calculation and backpropagation, so as to endow the generator with different parameter values and select the best one as the evolution result each time to enter the next iteration and 2) in the training process, we preserve the optimal generator as memory cells to avoid the performance degradation of the generator. Through these changes, HIEGAN overcame the defects of GAN, improved the stability of GAN, and finally improved classification efficiency. At the same time, in order to alleviate the overfitting problem of depth network under small samples, we change convolution and deconvolution into ghost module to reduce the network parameters. Experiments on three classical datasets validate that HIEGAN has encouraging performance in HIC under small samples.

Unsupervised signal modulation clustering is becoming increasingly important due to its application in the dynamic spectrum access process of 5G wireless communication and threat detection at the physical layer of Internet of Things. The need for better clustering results makes it a challenge to avoid feature drift and improve feature separability. This article proposes a novel separable loss function to address the issue. Besides, the high-level semantic properties of modulation types make it difficult for networks to extract their features. An autoencoder structure based on the random Fourier feature (RffAe) is proposed to simulate the demodulation process of unknown signals. Combined with the separable loss of RffAe (RffAe-S), it has excellent feature extraction ability. Great experiments were carried out on RADIOML 2016.10 A and RADIOML 2016.10 B. Experimental evaluations on these datasets show that our approach RffAe-S achieves state-of-the-art results compared to classical and the most relevant deep clustering methods.

The explosive growth of road vehicles especially the private cars has brought unprecedented pressure to a series of problems in urban transportation systems, such as traffic congestion and environmental pollution. Private cars trajectory data and perceiving their information provide a promising solution to these problems. However, the collection of large-scale trajectory data for private cars with high accuracy and reliability is still delicate tasks in urban environments. In this paper, we propose a low-cost and user-friendly implementation method for achieving large-scale private cars trajectory data acquisition via designing lightweight GPS module and On Board Diagnostics (OBD) reader. To ensure reliable trajectory data acquisition via GPS/OBD integration, we propose an ensemble learning based Gauss Process Regression (GPR) method so as to cope with the non-linearity, non-stationarity and incremental training problems during trajectory collection. We design a classification-type loss (CTL) function and build a regression to classification (R2C) method with Learn++ for realizing ensemble learning. The proposed approach implements incremental learning when new trajectory data arrives and is able to resolve the concept drifting problem. Experiments in real-world urban environment have demonstrated the effectiveness and reliability of the proposed method, it achieves better trajectory prediction performance than the comparative methods under various road conditions in GPS-denied areas.

With the fast development of urbanization and motorization, an increasing number of people choose to buy private cars to fulfill their daily travel needs. In particular, many people from various positions of the city drive their cars to specified areas, and then they will stop and stay for a certain period of time, leading to a spatiotemporal evolution of urban area attractiveness (AA). In this paper, we aim at understanding urban AA based on analyses of private car trajectory datasets. Specifically, by extracting point-of-stop (PoS) data from the private car trajectories, we design the variational Bayesian Gaussian mixture models (VBGMM) to deduce the probability density distribution of PoSs and connect it to the variation of AA. We establish a deep learning model based on long short-term memory (LSTM) to capture the evolution of the AA. Furthermore, we integrate dropout in the LSTM method to address challenging issues such as overfitting and time-consuming training of complex neural networks in the AA prediction. We conduct experiments by using real-world private car trajectory data to evaluate the performance of the proposed method. The results validate that our proposed method outperforms existing ones in terms of various metrics. To the authors’ knowledge, our work is the first one to utilize private car trajectory data to study urban area attractiveness, thereby facilitating a new perspective regarding an understanding of human travel behavior and the evolution of urban mobility.

Vehicle trajectory collection usually faces challenges such as inaccurate and incomplete trajectory data, mainly due to missing trajectories caused by Global Navigation Satellite System (GNSS) outages. In this paper, a novel ensemble transfer regression framework is proposed for urban environments with transfer learning as the primary solution for constructing a fine-grained trajectory dataset during GNSS outages. First, GNSS and motion information are fused for the training process. Then, a regression-to-classification (R2C) process is employed to implement incremental training to adapt to dynamically changing environments. Third, to account for GNSS outages, transfer learning is integrated to construct a data filtering strategy that minimizes negative sample weights during the current scenario. Finally, a more accurate classification-type loss function for ensemble learning is designed to obtain the ensemble transfer regression model. We utilize real-world datasets to verify the accuracy of the comparative methods and the proposed framework in trajectory interpolation prediction. The experimental results show that our framework is significantly superior to the comparative methods.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability.

Hyperspectral image has a large amount of data and many feature channels. If the hyperspectral image classification model is too complex, it is easy to cause low efficiency. In order to reduce the amount of network calculation and model parameters, improve the operation efficiency, and make full use of the characteristics of hyperspectral data, this paper uses the Ghost module to reduce the complexity of the model, and combined with the extended morphological profile (EMP) features, proposes a hyperspectral image classification method based on extended morphological profile features and Ghost module (GhostEMP). The experimental results show that the proposed method can improve the efficiency of operation while ensuring the operation efficiency of the network model.

This letter explores how to apply a generative adversarial network (GAN) to the classification of hyperspectral images (HSIs) to obtain a smooth training process and better classification results. To this end, the ideas of the progressive growing GAN (PG-GAN) and Wasserstein generative adversarial network gradient penalty (WGAN-GP) are combined to propose a new method for HSI classification. PG-GAN is optimized from the training process of generating adversarial networks. It gradually increases the depth of the network and the size of the input image, making the training smoother. WGAN-GP is optimized in terms of the loss function. The gradient penalty method is used to solve the problems of vanishing gradient and exploding gradient, making the training more stable. Based on the combination of the two methods, a classifier is added to the model so that it can complete the HSI classification task. The proposed method is evaluated over two publicly available hyperspectral data sets, the Indian Pines and University of Pavia data sets. The results show that the proposed method can achieve good training results with only a small amount of labeled training data.

Destination prediction plays an important role as the basis for a variety of location-based services (LBSs). However, it poses many threats to users’ location privacy. Most related work ignores privacy preservation in destination prediction. Few studies focus on specific kinds of privacy-preserving destination prediction algorithms and thus are not applicable to other prediction methods. Furthermore, the third party involved in these studies is a potential privacy threat. Additionally, another line of related work regarding LBSs neither guarantees the utility of the predicted results nor provides quantifiable privacy preservation. To this end, in this paper, we propose a general framework that can provide quantifiable privacy preservation and obtain a trade-off between the privacy and the utility of the predicted results by utilizing differential privacy and a neural network model. Specifically, it first adopts a specially designed differential privacy to construct a data-driven privacy-preserving model that formulates the relationship between injected noise and privacy preservation. Then, it combines a Recurrent Neural Network and Multi-hill Climbing to add fine-grained noise to obtain the trade-off between the privacy preservation and the utility of the predicted results. Our extensive experiments on real-world datasets validate that the proposed framework can be applied to different prediction methods, provide quantifiable location privacy preservation, and guarantee the utility of the predicted results simultaneously.

Benefited from the rapid development of deep learning, object detection in natural image has made great improvements. However, since the size of the optical remote sensing video is very large while the size of airplane is very small, airplane detection in optical remote sensing video still faces a lot of challenges. In this article, we aim at a novel approach for airplane detection in optical remote sensing video. The proposed approach utilizes spatial features from structured forests edge detection and temporal features from neighboring frames. It is capable of circumventing existing challenges and running at a high speed for practical applications. To realize this goal, edge detection results of optical remote sensing video frames are obtained from structured forests edge detection method. Afterwards, improved frames differencing method is utilized to extract temporal features. Finally, airplane detection result is generated by deep neural networks with extracted spatial and temporal features. Our experiments demonstrate that our method has a great breakthrough on the precision and recall of airplane detection in optical remote sensing video.

The number of vehicles has increased year by year, especially individual vehicles. In addition to meeting basic transportation needs, vehicles are expected to serve varied location-based services and applications for humans. However, it can constitute severe risks for privacy. In this article, we concentrate on one of the most sensitive information, namely, social relationships, that can be inferred from the vehicle mobility data. We propose a social relationship inference model, which provides a new perspective for privacy preservation in human mobility data. In particular, we extract discriminative features from both the spatial and temporal dimensions. Then, the heterogeneous features are being merged with a fusion model to improve the performance of inference. Extensive experiments on the real-world data set validate the effectiveness of the extracted features in estimating social connections and demonstrate that our method significantly outperforms the baseline models.

Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of a large amount of computations in the emerging vehicular applications. In this article, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here, the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hot zones. Specifically, a deep learning-based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a noncooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters, such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end users' quality-of-experience guaranteed.

With the ubiquitous demand for wireless communications, researchers have studied heterogeneous networks (HetNets) for years. Often the HetNets include a macrocell base station (MBS), several sets of macrocell users (MUs), a large number of femtocell base stations (FBSs), and femtocell users (secondary users), where the femtocells help the macrocell system relay the uplink or downlink traffic between the MUs and the MBS. In this article, we propose a novel joint information and energy cooperation method, with the aim of enhancing the spectrum and energy efficiency (EE) for cognitive HetNets. Specifically, the MUs and the femtocells harvest wireless energy from the radio frequency signal transmitted by MBS. By using the harvested energy, femtocells obtain the transmission opportunity to forward the signals of their serving users. We theoretically derive the theoretical expressions of the outage probabilities of the primary link as well as the secondary link. Then, we focus on investigating how to maximize EE by jointly considering time allocation and power control. Furthermore, we formulate the EE maximization problem, which contains the fractional form objective function and the linear inequality constraints and hence is nonconvex. To resolve this, we integrate the Dinkelbach method with convex optimization to derive the tractable and optimal solution. The numerical results demonstrate the simulations well match our theoretical analysis. Moreover, the results validate the feasibility of the proposed method for high-quality transmission without incurring extra energy consumption.

For years, vehicle trajectory data have increasingly been important for a wide range of applications, from driver behavior investigation/classification, travel time/distance estimation, and routing in vehicular networks, to vehicle energy/emission evaluation. This article presents TrajData, the first systematic solution to reliable vehicle trajectory data collection, with only reliance on commercial-off-the-shelf (COTS) onboard unit (OBU) devices that utilize lightweight GPS modules and low-cost onboard diagnostics (OBD) readers. In the practical use of trajectory collection, GPS outages inevitably occur in urban environments thereby leading to large trajectory errors as well as missing vehicle location data. To resolve this, we propose a novel data-fusion-enabled deep learning approach with the purpose of achieving reliable vehicle trajectory collection in various urban road conditions. Specifically, we leverage motion information retrieved from OBD readers in TrajData to help reconstruct the trajectory data during GPS outages. By investigating the changes of direction angle from the OBD readings, we can identify different types of road sections. Furthermore, we integrate the neural arithmetic logic units (NALUs) into our trajectory reconstruction model to tame the challenges when GPS outages take place in various road sections. Experimental results from realistic data have demonstrated the effectiveness and reliability of the proposed method. In the road test, TrajData achieves an average position error below 15-m around a 60-s GPS outage, even in complex road sections, i.e., continuous turns and driving with accelerations/decelerations resulting in frequent changes of direction and speed.

In order to provide a robust estimate of vehicle position in all environments, especially, in challenging urban areas where GPS signals are blocked, a fusion framework based on sparse Gaussian-Wigner prediction (SG-WP) is proposed. This new approach combines the advantages of both the random matrix theory and the sparse property to provide enhanced vehicle localization capabilities. In this method, measurement noises are assumed to be non-Gaussian distributed, and a generalized error distribution is adopted as an approximation to non-Gaussian densities. To ensure the robustness and the stability of the proposed approach, road-test experiments in various scenarios, including free, partial, and complete GPS outages, were performed based on the geometric dilution of precision metric. During complete outages, the SG-WP fuses all available INS measurements to improve the vehicle position prediction, whereas in free outages, only GPS information is processed. Besides, information from both GPS and INS are taken as inputs during partial outages, and the slide window is then introduced to regulate the flow data. The experimental comparison with the existing prediction methods reveals that the proposed method can achieve accurate and reliable positioning for land vehicles in all considered environments when the measurement noises are Gaussian or non-Gaussian distributed.

Understanding the travel behavior of private cars will generate promising solutions on addressing urban problems such as alleviating traffic congestion and improving transport services. In this paper, we focus on investigating the individual travel patterns of private car users based on a large-scale private car trajectory dataset. To achieve this goal, we first analyze the stop-and-wait information from the private car trajectory data and utilize DBSCAN method to implement clustering with the aim at identifying the frequently-visit places (FVPs). After that, we leverage Markov chain to study the spatial-temporal transition characteristics when private cars travel among their FVPs. Finally yet importantly, we design the concept of spatial-temporal entropy rate and conduct a quantitative study for measuring the regularity of each individual private car's mobility. We validate the proposed methodology based on a real-world dataset including 25,564 private cars driving during one month in China. Extensive experiments demonstrate that the proposed method outperforms the existing methods in terms of the accuracy on measuring the mobility behavior. Moreover, we observe that, on one side, the travel pattern is easier to mine from the private car users with fewer FVPs, on the other side, there are also a small number of users whose FVPs are large, while their mobility are relatively regular. Our work is the first effort to explore individual travel patterns of private car users via studying private car trajectory big data, thereby being able to provide new insight into the research of human travel activities, traffic management and urban planning.

In traditional ground cellular systems, ground terminals (GTs) in marginal areas are often faced with performance bottlenecks due to they are too far from the macro base station (MBS). For some temporary and unexpected communication service requirements, it is uneconomical to deploy femtocell access points (FAPs) on the edge of cell. In this paper, we investigate a cellular system that utilizes a unmanned aerial vehicles (UAVs) with base station module unit as an airborne mobile base station to provide communication channel for the cell-edge users offloaded by the MBS. Compared with FAPs, UAV is cheaper and flexible which can provide better communication quality for the cell-edge GTs with better channel condition. Based on the theoretical model, we proposed plausible optimal algorithm to maximize the energy-efficiency (EE) of the UAV by jointly optimizing the spectrum allocation, flying speed and user partition between the UAV and MBS. Numerical results indicate that our design could achieve relatively higher energy-efficiency by exploiting optimal flight strategy and spectrum allocation strategy.

Trajectory clustering aims at grouping similar trajectories into one cluster. It is an efficient way of finding the representative path or common trend shared by different moving objects, and also provides a foundation for movement pattern mining, anomaly detection and other applications. Existing trajectory clustering studies mainly rely on feature selection and similarity measurement based on their geographical and spatial properties. However, one obstacle hindering their wide usage is the problem of clustering accuracy in the presence of noisy or incomplete sensing data, due to limited sensory device quantity, communication errors, sensor failures, and sensor vacancy. This paper proposes an error-tolerant trajectory clustering approach by incorporating denoising methods.We propose the Robust Deep Attention Auto-encoders model (called Robust DAA) to learn the representations of low-dimensional denoising trajectories with three novel features. First, we present the deep attention auto-encoders by integrating the attention mechanism into the classical deep auto-encoder, which is capable of enhancing feature propagation and feature selection. Second, we train the deep attention auto-encoder by applying proximal method, back propagation and the Alternating Direction of Method of Multipliers (ADMM). As a result, our Robust DAA can reduce the negative influence of the noise on trajectory data. Finally, we perform clustering over the low-dimensional denoising representations using traditional clustering algorithms and demonstrates the quality of the clustering results by comparing our approach with existing representative methods. Extensive experiments are conducted on both synthetic datasets and real datasets. The results show that our approach outperforms the existing models in terms of accuracy, precision, recall and f1-score.

Short-term traffic flow prediction plays a crucial component in transportation management and deployment. In this paper, a novel regression framework for short-term traffic flow prediction with automatic parameter tuning is proposed, with the SVR being the primary regression model for traffic flow prediction and the Bayesian Optimization being the major method for parameters selection. First, the preprocessing of raw traffic flow is carried out by seasonal difference to eliminate the non-stationary of the data. Then, Support Vector Regression model is trained by the pre-processed data. In order to optimize the model parameters, the generalization performance of SVR is modeled as a sample from a Gaussian process (GP). Bayesian optimization determines the parameters configuration of the regression model by optimizing the acquisition function over the GP. Finally, the optimal short-term traffic flow regression model is constructed through repeated GP update and iteratively multiple training of the model. Experiment results show that the accuracy of proposed method is superior to methods of classical SARIMA, MLP-NN, ERT and Adaboost.

The communication infrastructures are likely to fail, in the case of disasters like earthquakes and debris flow, resulting in blind areas and the inconvenience of residents' communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely, an energy-aware device-to-device communication scheme (NEED). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads (CHs) before determining final CHs. Based on location and residual energy, the final CHs are selected in the candidate CHs, and dual CHs in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show the effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

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

Private cars, a class of small motor vehicles usually registered by an individual for personal use, constitute the vast majority of city automobiles and hence significantly affect urban traffic. In particular, private cars tend to stop-and-wait (SAW) in specific regions during daily driving. This SAW behavior produces a spatiotemporal aggregation effect, which facilitates the formation of urban hot zones. In this paper, we investigate the SAW behavior and aggregation effect based on large-scale private car trajectory data. Specifically, motivated by the first law of geography, we leverage the kernel density estimation (KDE) method and extend it to three dimensions to capture the density distribution of the SAW data. Furthermore, according to the inherent relationship between the present SAW density and future SAW aggregation, we propose a 3D-KDE-based prediction model to characterize the dynamic spatiotemporal aggregation effect. In addition, we design a modified inertia weight particle swarm optimization (MIW-PSO) algorithm to determine the optimal weight coefficients and to avoid local optima during SAW prediction. Extensive experiments based on real-world private car SAW data validate the effectiveness of our method for discovering dynamic aggregation effects, therein outperforming the current methods in terms of the Kullback-Leibler (KL) divergence, mean absolute error (MAE), and root mean square error (RMSE). To the best of the authors’ knowledge, our work is the first to utilize private car trajectory data to study the aggregation effect in urban environments, thereby being able to provide new insight into the study of traffic management and the evolution of urban traffic.

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. In order to resolve difficulties introduced by low sampling rate trajectory data, we concentrate sample points around intersections by utilizing the turning characteristics from the large-scale trajectory data to ensure the accuracy of the detection of intersections and road segments. In front of complex road networks including many complex intersections, such as the overpasses and underpasses, we first layer intersections into major and minor one, and then propose a simplified representation of intersections and corresponding computable model based on the features of roads, which can significantly improve the accuracy of detected road networks, especially for the complex intersections. In order to construct fine-grained road networks, we distinguish various types of intersections using direction information and detected turning limit. To the best of our knowledge, our road network building method is the first time to give fine-grained road networks based on low-sampling rate private car trajectory data, especially able to infer the location of complex intersections and its connections to other intersections. Last but not the least, we propose an effective parameter selection process for the Density-Based Spatial Clustering of Applications with Noise based clustering algorithm, which is used to implement the reliable intersection detection. Extensive evaluations are conducted based on a real-world trajectory dataset from 1,345 private cars in Futian district, Shenzhen city of China. The results demonstrate the effectiveness of the proposed method. The constructed road network matches close to the one from a public editing map OpenStreetMap, especially the location of the road intersections and road segments, which achieves 92.2% intersections within 20m and 91.6% road segments within 8m.

Due to the popularity of mobile computing and mobile sensing, users' traces can now be readily collected to enhance applications' performance. However, users' location privacy may be disclosed to the untrusted data aggregator that collects users' traces. Cloaking users' traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose W3-tess that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of W3-tess is to credibly imitate the temporal, spatial, and social behavior of users' mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, W3-tess can provide “differential privacy” on location privacy preservation. In addition, compared to the existing work, W3-tess offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of W3-tess.

The advances in mobile sensing and vehicle cloud service techniques have generated massive spatial-temporal trajectory data, which has caused the crises of storage and communication. In this paper, we propose a novel Opportunistic Compression and Transmission approach, namely OCT, with aims of reducing trajectory transmission overhead and storage cost. We first present the design of trajectory collection terminal based on GPS & OBD wherein the main process of OCT can be implemented rather than the cloud server. Within the proposed OCT, we devise a map-matching method based on MIV-matching and calibrating trajectory, which significantly reduces sampling errors of raw trajectories. Then we divide trajectory data into two parts, i.e. spatial and temporal parts, and realize compression operation separately. By using a prediction model based on historical trajectory velocity, we make use of opportunistic transmission of trajectory data from the GPS & OBD terminal to vehicle cloud server and thus dramatically decrease the transmission overhead. The proposed OCT not only realizes real-time trajectory preprocessing and compressing, but also ensures high trajectory compression ratio. To validate the performance of the OCT, we collect a large-scale private car trajectory data from real urban environments. Extensive experiments verify the effectiveness and superiority of the proposed method.

Estimating communication latency is a challenging task in the applications of industrial Internet of things (IIoT). Mobile convergecast, as a many-to-one communication pattern, has been recently explored in mobile sensor networks for IIoT, where sensor nodes are usually in mobile status, and report the sensed data regularly or randomly to one or more stationary sinks through the multihop routing path. As convergecast becomes increasingly relevant for industrial sensing and monitoring, a critical part of empowering information aggregation is to maintain consistent transmission. Path duration is one important component of end-to-end delay for communications along the path. In this paper, a probabilistic model for mobile convergecast has been proposed and evaluated to capture path duration times, by considering parameters including network models, sensor network scope, and mobility patterns of network elements. Through experiments, it has been verified that the proposed model can provide a feasible analysis of end-to-end delays in industrial networks implementing convergecast.

Heterogeneous small cell networks (HSCN), as a promising paradigm to increase end-user data rates and improve the overall capacity, is expected to be a key cellular architecture in 5G wireless networks. However, energy consumed in HSCN is considerable due to the massive use of small cells. In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cell base stations (SBSs) deploying in the HSCN. We first propose power consumption models so as to characterize the active state and the idle state of SBSs, respectively. Then two sleep modes for SBSs tier, i.e. random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the SBS operation probability. Through the analysis on activeness of SBSs, we define the operation probability for the SBS applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability for coverage, which is used to decide whether an active user can connect to a SBS successfully, are derived for the proposed sleep modes. Energy consumption minimizations are presented for the two proposed sleep modes under the success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the energy saving strategy. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

Mobility management and handover, in the presence of the macro-small wireless cellular networks, are of the critical challenging issues, owing to the large- scale small cells deploying and frequent mobility of user equipment (UE). Existing literatures include variously studied the inbound handover, namely the UE's movement from a macrocell to a small cell. The outbound handover (OHO), which refers to the UE's movement from a small cell to a macrocell, is also of importance. In this paper, instead of the concentric circles based handover regions, we propose a novel model for the OHO regions by utilizing the principle of the circle of Apollonius. Based on the mathematical analysis for the boundaries of OHO regions, we derive the closed-form expression to characterize the relationship between the OHO hysteresis margin and UE's mobility in terms of the velocity and the moving direction. Then, we develop the UE's movement based regulatory algorithm to adaptively adjust hysteresis for achieving the proper OHO. Simulation results show the effectiveness of the proposed OHO model. The proposed hysteresis adjusting algorithm outperforms the existing schemes in terms of the decrease for radio link failure and pingpong effect during OHO process.

In this paper, we investigate the hierarchical clustering for dense small cells and devise non-cooperative game-theoretic scheme, with aim at increasing the network throughput and minimizing both cross- and co-tier interference. By studying the distances between the small cells and the requirements of cluster formation, we propose a novel hierarchical clustering scheme for the densely deployed small cells, which consists of two phases, i) an absorption mechanism is designed to form the small cells into clusters; ii) to balance cluster populations, a segmentation algorithm is proposed for the clusters that contains excessive small cells. Within each small cell cluster, the spectrum is split into central frequency and marginal frequency which can be reused in a FFR manner. Then, we formulate the spectrum allocation and interference mitigation issues as a non-cooperative game, in which a game theoretical strategy optimization algorithm based on regret-matching is proposed to reach the correlated equilibrium. Numerical results reveal that our approach can achieve the correlated equilibrium with fast convergence and it is effective in offloading traffic and increasing the system throughput in dense small cell networks.

Land vehicle localization and navigation mainly relies on the Global Positioning System (GPS)/Inertial Navigation System (INS) integration. In this paper, we propose a unified incremental regression framework that enables vehicle localization with high accuracy in urban environment. Within the framework, we propose a nonlinear Gauss Process Regression (GPR) approach to perform vehicle position prediction during GPS outages. By mapping nonlinear data into high-dimensional space by kernel function, the proposed GPR based approach is able to deal with the nonlinearity issue in GPS denied environment. We design a Particle Swarm Optimization (PSO) based algorithm to optimize GPR hyper-parameters, which are tuned with high time efficiency for vehicular position prediction. By real-world road experiments, the proposed method is evaluated against Artificial Neural Network (ANN), Support Vector Machine Regression (SVR) and Partial Least Squares Regression (PLSR). The results reveal that the proposed outperforms the others by 22.8%-65.2% improvement in the positional accuracy.

Short-term traffic flow forecasting has been a crucial component in the area of intelligent transportation systems (ITS), which plays a significant role in operating traffic management systems and dynamic traffic assignment effectively as well as proactively. In this paper, a novel short-term traffic flow prediction method called Ensemble Real-time Sequential Extreme Learning Machine (ERS-ELM) with simplified single layer feed- forward networks (SLFN) structure under freeway peak traffic condition and non-stationary condition is proposed. By quickly training historical data and incrementally updating model with new arrived data, ERE-ELM has the characteristics of less training time consumption and high prediction accuracy. Ensemble mechanism is also used to improve stability and robustness. Experiment results show that average mean absolute percentage error (MAPE), test root mean square error (RMSE) as well as training time consumption of proposed method is superior to classical Wave-NN, MLP-NN and ELM methods.

Abstract In this paper, we investigate the inbound handover confusion in the two‐tier macrocell‐small cell networks with help of mobility prediction. Instead of studying the mobile user's (MU) movement, we propose an analytical model for the activity status of small cells, which is to exploit the statistical property of inbound handover events that would happen in small cells. We design the cell status prediction algorithm to obtain the optimal prediction outcome of the next status for the small cells. On avoiding the handover confusion, we develop a dynamic allocation approach of physical cell identifier according to the prediction results. We design (i) the cell status prediction‐based strategy, by which the dedicated PCIs will be assigned to the small cells with busy activity status while the other small cells share the public PCIs, and (ii) an integrated strategy in order to fully exploit the usage of PCI. We formulate the preference relation for small cells via reference signal received quality relation integrated with status prediction information using Bayesian average method. Simulation results reveal that the proposed algorithms yield higher accuracy than the conventional methods; in the meantime, handover confusions can be reduced significantly during the inbound handover. Copyright © 2016 John Wiley & Sons, Ltd.

In severe non-line-of-sight environment, the three-dimensional localization methods face a major challenge due to the undetectable directed path and the unavailable arrival angles. In this paper, we proposed a novel three-dimensional localization scheme by exploiting the distance and departure angle of single-bounce path, which is capable in non-line-of-sight localization even with lacking information of spatial arrival angles. With the help of height information of the scattering plane, we study the three-dimensional spatial location relationship among the reference station, unknown node and scatterers and devise a new optimization algorithm which can apply in severe non-line-of-sight environment. Furthermore, the performance of the algorithm is studied by using root mean-square error analysis and Cramer-Rao lower bound. The simulation results show that the derived analytical results closely match the simulated results. The numerical results of cumulative distribution function in location error demonstrate that utilizing sufficient single-bounce path signals can yield to acceptable three-dimensional positioning accuracy in severe non-line-of-sight multipath environments.

Vehicle position prediction has become more and more critical for most applications in intelligent transportation systems (ITS). Prediction based INS/GPS integration provides continuous and reliable navigation solution when compared to standalone Inertial Navigation System (INS) or Global Positioning System (GPS). Although there have been several research works for fusing INS and GPS data to bridge navigation during GPS outages, most of them are offline methods and do not consider sensors data fluctuation due to traffic incident, inclement weather conditions or rush hour. This paper proposes a supervised statistical learning technique called Online Support Vector Machine for Regression (OL-SVR) for the prediction of vehicle position. During GPS availability, the OL-SVR models INS errors by fusing the INS and GPS data; meanwhile during outages, the trained OL-SVR method is utilized to predict accurate vehicle position. The proposed method is compared with two well-known prediction techniques including Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN). Experiments conducted at rush hour on real urban roads and simulation results prove that OL-SVR is more efficient and accurate in position prediction than PLSR and ANN, achieving an accuracy improvement of 20.3%-64.8%.

In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cells deploying in the heterogeneous networks. We first propose two power consumption models so as to characterize the active state and the idle state of small cells respectively. Then two sleep modes for small cells tier, random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the small cell operation probability. Through the analysis on activeness of small cell base stations (SBSs), we define the operation probability for the small cell applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability, which is used to decide whether an active user can connect to a small cell successfully, are derived for the proposed two sleep modes. Energy consumption minimizations are presented for each of the proposed sleep modes with condition on success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the concrete sleep mode. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

In this study, the authors develop a novel solution for hybrid global navigation satellite systems, differential navigation satellite systems and time of arrival cooperative positioning (CP) based on iterative finite difference particle filter (PF) in GNSS‐terrestrial navigation and challenging environments. A variant of finite difference filters called divided difference filter (DDF) was used as an importance density for particle generation. Various proposal distributions have been proposed to improve the performance of PF, but practical situations have encouraged the researchers to design better candidate for proposal distributions in order to gain better performance especially for hybrid CP system. The author's proposed method named hybrid cooperative particle‐based DDF solves the problem of linearisation of non‐linear functions that are based on Jacobian matrices which often cannot be applied in practical applications of non‐linear estimation techniques. An iterative reweighted information filter based on the extended Kalman filter (KF) was integrated during the measurement update phase to smooth the output of the DDF used for particles update. Simulation results based on a realistic outdoor scenario show that the proposed solution outperforms some well‐known state‐of‐the‐art in hybrid CP systems, such as hybrid cooperative unscented KF in terms of accuracy and availability and provides good performance even in challenging conditions.

In long‐term evoluation (LTE) femtocell networks, hysteresis is one of the main parameters which affects the performance of handover with a number of unnecessary handovers, including ping‐pong, early, late and incorrect handovers. In this study, the authors propose a hybrid algorithm that aims to obtain the optimised unique hysteresis for an individual mobile user moving at various speeds during the inbound handover process. This algorithm is proposed for two‐tier scenarios with macro and femto. The centralised function in this study evaluates the overall handover performance indicator. Then, the handover aggregate performance indicator (HAPI) is used to determine an optimal configuration. Based on the received reference signal‐to‐interference‐plus‐noise ratio, the distributed function residing on the user equipment (UE) is able to obtain an optimal unique hysteresis for the individual UE. Theoretical analysis with three indication boundaries is provided to evaluate the proposed algorithm. A system‐level simulation is presented, and the proposed algorithm outperformed the existing approaches in terms of handover failure, call‐drop and redundancy handover ratios and also achieved better overall system performance.

Femtocell has been considered as a promising technology in wireless communications to extend indoor service coverage and enhance overall network capacity. Two-tier networks, where the current cellular networks, i.e., macrocells, overlapped with a large number of randomly distributed femtocells, can potentially bring significant benefits. If femtocell access points (FAPs) operate within the same frequency band as macrocells, the cross-tier interference (CTI) creates a distinct impact on the system performance. This paper studies the uplink (UL) interference at FAP caused by approaching macrocell users (MUEs). It is noted that the CTI is more significant to the closed subscriber group (CSG) femtocells. We propose an incentive mechanism (IM) for CSG femtocells to alleviate the UL interfference from approaching MUEs, thus preventing the femtocell users performance from degrading and protecting the neighbor FAPs that might also suffer from the UL interference. Meanwhile, the macrocell also benefits from the IM, in terms of energy saving and UL spectral efficiency. Simulation results show that close to the ideal performance when no CTI presents can be achieved with IM, demonstrating that the proposed scheme is very effective in dealing with uplink CTI.