Blockchain technology, renowned for its decentralized architecture and cryptographic immutability, faces fundamental scalability limitations due to its global consensus requirement where all nodes validate every transaction. Although sharding offers potential scalability via parallel transaction processing across subnetworks, its practical implementation confronts dual limitations: chronic load imbalance leading to suboptimal resource utilization and prohibitive cross-shard communication impairing system efficiency. Facing these challenges, this paper proposes a dynamic sharding-optimized cluster-driven greedy algorithm(DSCGA) scheme that employs account partitioning through cluster analysis of transactional patterns for dynamic node allocation. This scheme also analyzes historical transaction records to construct an account transaction network, ensuring strong intra-shard transaction affinity and weak inter-shard correlations. To validate the effectiveness of this approach, experimental validation with Ethereum data confirms the effectiveness of the scheme in minimizing cross-shard transactions and alleviating load imbalance.

With the rapid advancement of intelligent transportation systems and the continuous improvement of supporting infrastructure, the development of the Internet of Vehicles (IoV) has garnered increasing attention from researchers. The decentralized and immutable characteristics of blockchain can significantly enhance the security, data integrity, and transparency within the IoV, thereby driving rapid progress in blockchain-based solutions in the IoV ecosystem. However, low-latency decision-making applications face limitations due to performance bottlenecks and other challenges in blockchain technology. To address this issue, this paper proposes a blockchain grouping-based model that segments blockchain nodes into three interconnected sub-modules, each responsible for distinct functions. This model enables efficient decision-making during continuous system operations via asynchronous processes, ensuring decision accuracy through multi-layer verification. Furthermore, this paper introduces a Kademlia-based blockchain communication protocol, leveraging physical addresses in the IoV to enhance the speed of routing table construction between vehicle nodes. The performance of the proposed model was evaluated through experiments with the MCTS-Greedy algorithm. The results indicate that the blockchain-based decision model significantly improves decision-making efficiency and demonstrates robustness and stability under various conditions.

Existing 3D generation frameworks have demonstrated notable success, their continued evolution is impeded by issues including view inconsistency and insufficient detail, which significantly hinders further methodological refinements. To address these challenges, we propose MagicGS++, an innovative framework for Image-To-3D generation that efficiently generates high-quality 3D content from a single-view image. MagicGS++ adopts a Coarse-To-Fine framework consisting of two main stages. In the frst stage, a fixed-orthogonal view diffusion model with a further Super-Resolution model are employed to generate orthogonal view images and perform initial optimization on the 3D Gaussian distribution, yielding a rough shape. In the second stage, a fixed view image of the 3D Gaussian model is rendered alongside two neighboring-view images most relevant to the fixed view. By jointly optimizing the multi-MSE loss between the neighboring view images and the orthogonal view image, and the SDS loss between the fixed-view image and the 3D model. Extensive experiments demonstrate that MagicGS++ outperforms existing methods in both geometric detail and texture quality. Moreover, by integrating with a conditional diffusion model, our method supports multi-modal 3D generation tasks, showcasing remarkable adaptability.

In the fields of intelligent security and human-computer interaction, the rapid development of noncontact identity recognition technology based on Wi-Fi signals has shown promising application potential. To address the significant decrease in recognition accuracy in close-range scenarios, an close-range noncontact identity recognition method named WiDoor is proposed. During the data collection phase, the Fresnel propagation model is utilized by WiDoor to optimize the deployment layout of the receiving antennas. Gait information is reconstructed from the multiple antennas to enable the acquisition of more rich gait features. In the identity recognition stage, WiDoor employs a lightweight model that combines self-attention mechanisms with multiscale convolutional neural networks. This combination effectively enhances the model’s capability to capture key features while significantly reducing computational complexity and maintaining a high recognition accuracy. Experimental results show that WiDoor achieves a recognition accuracy of up to 99.3% on an expanded dataset that includes ten participants, with a distance of 1 m between the receiving and transmitting ends, and the parameter quantity of the built-in model is only 2% of the compared model with the same accuracy, offering a significant advantages over similar methods. Additionally, the model can achieve a high-precision recognition across different distances between the transmitter and the receiver using a limited number of samples, showing strong robustness of the model.

Previous research in active learning has primarily focused on selecting examples from closed-set data, which consists solely of unlabeled examples from the target classes. However, this approach overlooks the more prevalent scenario of open-set data in real-world applications. Open-set data encompasses examples from both target classes and non-target classes. To fill this gap, we propose a novel framework called JOSAL, which enhances the accuracy of the classifier by precisely selecting the target class examples from open-set data. The JOSAL framework introduces the concept of joint learning, where the Sampler and Classifier components perform sampling and classification tasks, respectively, by sharing example features extracted from a pre-trained Encoder. To maximize the classification accuracy of the Classifier, the framework adopts a novel joint learning strategy. This strategy initially prioritizes optimizing the Sampler and gradually shifts the optimization attention to the Classifier. The experimental results demonstrate that, compared to baselines, our approach exhibits stronger sampling precision and achieves higher classification accuracy. To the best of our knowledge, this is the first work to address the open-set active learning problem using the joint learning paradigm.

The digital twin technology facilitates the application of Metaverse in autonomous driving. Particularly, this paper focuses on investigating an unmanned aerial vehicle (UAV)-aided vehicular Metaverse. In specific, the moving vehicles in physical world collect the real-time traffic data, which is synchronized through the UAV to the virtual world to help the autonomous vehicle (AV) simulation. For such a physical-virtual synchro-nization process, we define the age of incorrect information (AOII) to measure the traffic data freshness. Accounting for the randomness in the physical world, we jointly optimize the UAV trajectory, the vehicle scheduling and the semantic extraction of collected data under the Markov decision process (MDP) framework. Our objective is to minimize the expected long-term system AOII. Without the statistical knowledge of physical-world randomness, we propose to leverage a proximal policy optimization based deep reinforcement learning algorithm to solve the optimal control policy to the MDP formulation. We conduct numerical experiments to verify the accuracy of the theoretical analysis, and the results demonstrate the performance gains from our proposed algorithm.

The main idea of Multi-Object tracking is to accurately identify and track the position and motion status of multiple objects in a video sequence in real time. The current mainstream tracking algorithm is to associate the detection boxes with the trajectory in a comprehensive and violent way, as a way to accurately determine and maintain the unique identity mark of the moving object. However, implementing a unified association process for detection boxes with different confidence levels, this suffers from non-negligible low information utilization, long association time, and high resource consumption. To solve this problem, we propose a faster, simple and effective association algorithm ADAPT, dividing the queue of detection boxes and trajectories by confidence level to achieve adaptive association of detection boxes and trajectories in the queue, so as to realize tracking, shorten the association time, and improve tracking effect. When applied to six different trackers, our method achieved significant improvements in both MOTA and IDF1 scores. In order to improve the MOT performance, we designed a simple and powerful tracker, AdaptTrack, and evaluated it on 3090GPU for VisDrone-MOT datasets, achieving 41.3 MOTA, 53.6 IDF1, and 42.9 HOTA. AdaptTrack on UAVDT datasets also shows the same excellent results of 69.8 MOTA, 78.9 IDF1, 66.3 HOTA, and achieves a tracking speed of 18.2 FPS.

Recent advancements in 3D generation have garnered considerable interest due to their potential applications. Despite these advancements, the field faces persistent challenges in multi-conditional control, primarily due to the lack of paired datasets and the inherent complexity of 3D structures. To address these challenges, we introduce ImageBind3D, a novel framework for controllable 3D generation that integrates text, hand-drawn sketches, and depth maps to enhance user controllability. Our innovative contribution is adopting an inversion-align strategy, facilitating controllable 3D generation without requiring paired datasets. Firstly, utilizing GET3D as a baseline, our method innovates a 3D inversion technique that synchronizes 2D images with 3D shapes within the latent space of 3D GAN. Subsequently, we leverage images as intermediaries to facilitate pseudo-pairing between the shapes and various modalities. Moreover, our multi-modal diffusion model design strategically aligns external control signals with the generative model's latent knowledge, enabling precise and controllable 3D generation. Extensive experiments validate that ImageBind3D surpasses existing state-of-the-art methods in both fidelity and controllability. Additionally, our approach can offer composable guidance for any feed-forward 3D generative models, significantly enhancing their controllability.

Information sharing among vehicles plays a critical role in improving driving safety and traffic loads in Internet of Vehicles (IoV). However, due to the existence of malicious vehicles, information sharing among vehicles lacks a trustworthy environment. It is challenging for a vehicle to assess the credibility of the received information. Blockchain has attracted extensive attention because of the decentralization and tamper-proof characteristics. Nevertheless, the high mobility of vehicles leads to rapid network topology changes, which makes it difficult to reach a consensus. In this article, we propose a consortium blockchain-based trust-value management approach to build a trustworthy environment for information sharing among vehicles. A new incentive mechanism is designed to encourage vehicles to actively participate in blockchain maintenance. Furthermore, an Enhanced Proof of Work (EPoW) consensus algorithm is designed to reduce the amount of information that needs to be transmitted over the network to reach consensus quickly. Comprehensive analysis and simulation results verify that the proposed trust-value management approach and EPoW consensus algorithm realize secure and efficient information sharing in IoV.

With the advancement of intelligent transportation systems, location-based services (LBS) have been widely applied in vehicular ad hoc networks (VANETs). LBS utilizes mobile devices to gather vehicle location data, which is then processed using relevant technologies. By combining this data with additional information, LBS offers users personalized and intelligent services. However, providing LBS brings critical security issues related to the exposure of vehicle positions, as well as privacy-preserving problems during the process of collecting location information in VANETs. We propose a distributed trust-based k anonymity scheme to address the aforementioned issues. Our proposed scheme adopts a trust framework among vehicles for various types of LBS. This framework involves a multiparty evaluation and consideration of trust value fluctuations to enhance the efficiency of establishing a reliable k anonymous cloaking region. Furthermore, by leveraging the tamper-proof and decentralized nature of blockchain, we employ a lightweight consortium blockchain to maintain the security of the trustworthiness data throughout the entire model. Extensive security analysis and rigorous experiments have been conducted to demonstrate that the scheme exhibits a certain degree of resilience against attacks on various trust models. Additionally, it has the ability to construct anonymous regions with limited time delay, thereby preserving the privacy of vehicle locations. In comparison to other schemes, it exhibits lower computational complexity and enhanced security.

Previous works have proven the superior performance of ensemble-based black-box attacks on transferability. However, existing methods require significant difference in architecture among the source models to ensure gradient diversity. In this paper, we propose a Diverse Gradient Method (DGM), verifying that knowledge distillation is able to generate diverse gradients from unchangeable model architecture for boosting transferability. The core idea behind our DGM is to obtain transferable adversarial perturbations by fusing diverse gradients provided by a single source model and its distilled versions through an ensemble strategy. Experimental results show that DGM successfully crafts adversarial examples with higher transferability, only requiring extremely low training cost. Furthermore, our proposed method could be used as a flexible module to improve transferability of most of existing black-box attacks.

Mobile-edge computing (MEC) brings the potential to address the ever increasing computation demands from the mobile users (MUs). In addition to local processing, the resource-constrained MUs in an MEC system can also offload computation to the nearby servers for remote execution. With the explosive growth of mobile devices, computation offloading faces the challenge of spectrum congestion, which, in turn, deteriorates the overall quality of computation experience. This article, hence, investigates computation task scheduling in a heterogeneous cellular and WiFi MEC system. Such a system provides both licensed and unlicensed spectrum opportunities. Due to the sharing of communication and computation resources as well as the uncertainties, we formulate the problem of computation task scheduling among the competing MUs in a stationary heterogeneous edge computing system as a noncooperative stochastic game. We propose an approximation-based multiagent Markov decision process without the global system state observations, under which a multiagent proximal policy optimization (PPO) algorithm is derived to solve the corresponding Nash equilibrium. When expanding to a nonstationary heterogeneous edge computing system, the obtained algorithm suffers from the slow convergence due to constrained adaptability. Accordingly, we explore meta-learning and propose a multiagent meta-PPO algorithm, which rapidly adapts the control policy learning to the nonstationarity. Numerical experiments demonstrate performance gains from our proposed algorithms.

In point cloud video streaming systems, the field of view (FoV) prediction is critical for selecting the tiles, the objective of which is to optimize the expected long-term quality-of-experience (QoE) from the perspective of a user. On one hand, a satisfactory QoE accounts for not only the playback quality but also the playback smoothness. On the other hand, the large data volume of a selected tile requires the transmission to be adaptive to the system uncertainties. This paper applies a Markov decision process to formulate the problem of tile selection across the infinite discrete time horizon. In particular, a system state includes the FoV information, which is predicted from the Transformer. To alleviate the dependence on system uncertainty statistics, a deep reinforcement learning approach is derived for solving the optimal control policy. Under different settings, we conduct experiments based on the real throughput and head-mounted display data. The results show that compared to the existing baselines, our proposed prediction-control approach achieves a higher FoV prediction accuracy, better playback quality as well as smoothness, and hence a better average QoE for the user.

Traffic congestion is a severe challenge for intelligent transportation system (ITS). So far, if traffic congestion is perceived in a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestions should be tackled with rerouting, since the extra overhead (e.g., extra travel time, extra fuel consumption, and extra CO2 emission) caused by certain congestions might be lower than that of re-routing. Against this backdrop, an intelligent route guidance strategy is proposed, in which vehicles will trade off the extra overhead of re-routing and that of waiting in congestion. Firstly, the perceived congestion is divided into four basic types. Then, the prediction method about the duration of each congestion type is formulated. Finally, the intelligent route planning mechanism is developed. Simulations demonstrate that the proposed strategy can reduce the travel time, fuel consumption, and CO2 emission for vehicles.

The applications of Internet of Things have emerged in every aspect of people’s life. The volume of data gathered can be enormous. Enterprises and personal consumers are increasingly reliant on cloud storage services instead of local storage. While they enjoy the convenience of cloud storage services, they also worry about the integrity of the cloud-stored data since they do not physically own the data. To enable public integrity auditing, third-party auditors as trusted ones verify data integrity on behalf of the data owner. However, the vulnerability of auditors should also be considered. We propose a consortium blockchain-based public integrity verification system (CBPIV). In CBPIV, the auditor behaviors are recorded in the consortium blockchain so that authorized parties can audit the auditor to see if the verification results are correct. A smart contract is deployed to check the behavior of the auditor automatically, which can trigger alerts for unusual behaviors. The evaluation on both security and performance shows that our proposed scheme is secure and alleviates the burden on data owners of limited computation capability.

Mobile edge computing (MEC) is a promising paradigm for alleviating the computation burden of resource-constrained mobile devices. Nevertheless, the majority of existing efforts concentrate on offloading computations from mobile de-vices to an edge computing server through the cellular networks only. With the development of wireless connectivity technologies, WiFi networks over unlicensed spectrum provide a “green” (i.e., cost-efficient and economical) alternative for computation offloading. In this paper, we investigate the problem of computation offloading in a heterogeneous WiFi and cellular MEC system, where both the WiFi and the cellular networks are possible for offloading the arriving computation tasks at a mobile user (MU). The objective of an MU is to minimize the long-term cost, which can be described as a single-agent Markov decision process (MDP) by accounting for the inherent system dynamics in the MU mobility, sporadic computation task arrivals and wireless connectivity variations. To solve the optimal strategy for the formulated MDP with a high-dimensional state space but without the statistical knowledge of system dynamics, we resort to a model-free deep reinforcement learning algorithm. Numerical experiments verify that the proposed algorithm is able to significantly reduce the average computation offloading cost compared with other baselines.

The point cloud videos, thanks to the multi-view and immersive experiences, have recently attracted notable attentions from both academia and industry. Due to the high data volume, a point cloud video also raises the challenge of quality-of-experience (QoE), which is in terms of the balance between playback quality and buffering delay during the transmission under time-varying system conditions. In this paper, we propose a deep reinforcement learning (DRL) approach to optimize the expected long-term QoE for the client. Over the time horizon, the proposed approach learns to select the tiles of the corresponding video for transmissions in an iterative way. Under various settings, numerical experiments based on real throughput data traces are conducted to evaluate the proposed approach. Compared to the baselines, our approach not only enhances the video quality but also reduces the re-buffering time, obtaining an improvement of average QoE for the client by 9%–14%.

WiFi-based identity recognition is predominant because of its noninvasive and ubiquitous advantages. However, existing approaches show slow training speed and limited applicability. In this article, we propose a lightweight deep learning model, named as lightweight WiFi-based identification (LW-WiID), to address these technical challenges. LW-WiID reconstructs original data of channel state information into frequency energy graph, which contains not only the temporal feature of the gait but also the spatial feature among subcarriers, ensuring the accuracy of identity recognition. Furthermore, a novel Balloon mechanism is designed to achieve the lightweight. Through information integration crossing both layers and channels, the Balloon mechanism effectively reduces the number of model parameters. Experimental results demonstrate that LW-WiID achieves an accuracy of 99.7% on a 50-person gait data set while the model size is compressed to 5.53% of the existing identity recognition approaches with the same accuracy.

Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable devices. Therefore, in order for convenient real-time MI detection, it is essential to design lightweight models suitable for resource-limited portable devices. This paper proposes a novel multi-channel lightweight model (ML-Net), that provides a new solution for portable detection devices with limited resources. In ML-Net, each electrocardiogram (ECG) lead is assigned an independent channel, ensuring data independence and preserve the ECG characteristics of different angles represented by different leads. Moreover, convolution kernels of heterogeneous sizes are utilized to achieve accurate classification with only a small amount of lead data. Extensive experiments over actual ECG data from the PTB diagnostic database are conducted to evaluate ML-Net. The results show that ML-Net outperforms comparable schemes in diagnosing MI, and it requires lower computational cost and less memory, so that portable devices can be more widely used in the field of Internet of Medical Things(IoMT).

Body movement is one of the most important indicators of sleep quality for elderly people living alone. Body movement is crucial for sleep staging and can be combined with other indicators, such as breathing and heart rate to monitor sleep quality. Nevertheless, traditional sleep monitoring methods are inconvenient and may invade users' privacy. To solve these problems, we propose a contactless body movement recognition (CBMR) method via WiFi signals. First, CBMR uses commercial off-the-shelf WiFi devices to collect channel state information (CSI) data of body movement and segment the CSI data by sliding window. Then, the context information of the segmented CSI data is learned by a bidirectional recurrent neural network (Bi-RNN). Bi-RNN can fuse the forward and backward propagation information at some point, and input it into a deeper independently recurrent neural network (IndRNN) with residual mechanism to extract the deeper features and capture the time dependencies of CSI data. Finally, the type of body movement can be recognized and classified by the softmax function. CBMR can effectively reduce data preprocessing and the delay caused by manually extracting features. The results of an experiment conducted on a complex body movement data set show that our method gives desirable performance and achieves an average accuracy of greater than 93.5%, which implies a prospect application of CBMR.

The proliferation of multi-core architectures has led to explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk, etc. With increasing number of cores, however, it becomes harder to efficiently schedule parallel applications on these resources since current multi-core runtime systems still lack efficient mechanisms to support collaborative scheduling of these applications. In this paper, we study feedback-driven adaptive scheduling based on work stealing, which provides an efficient solution for concurrently executing a set of applications on multi-core systems. To dynamically estimate the number of cores desired by each application, a stable feedback algorithm, called A-Deque, is proposed using the length of active deques, which more precisely captures the parallelism variation of the applications. Furthermore, a prototype system is built by extending the Cilk runtime system, and the experimental results show that feedback-driven scheduling algorithms have more advantages for scheduling parallel applications with dynamic changing parallelism, and better overall performances are achieved with more accurate and stable feedback mechanism. Compared with existing algorithms, A-Deque improves the performances by up to 19.13\% and 28.96\% with respect to average response time and processor utilization respectively.

Both fairness and efficiency are crucial measures for the performance of parallel applications on multiprocessor systems. In this paper, we study online adaptive scheduling for multiple sets of such applications, where each set may contain one or more jobs with time-varying parallelism profile. This scenario arises naturally when dealing with several applications submitted simultaneously by different users in a large parallel system, where both user-level fairness and system-wide efficiency are important concerns. To achieve fairness, we use the equipartitioning algorithm, which evenly splits the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler, which periodically adjusts the processor allocations within each set by consciously exploiting the jobs' execution history. We show that our algorithm is competitive for the objective of minimizing the set response time. For sufficiently large jobs, this theoretical result improves upon an existing algorithm that provides only fairness but lacks efficiency. Furthermore, we conduct simulations to empirically evaluate our algorithm, and the results confirm its improved performance using malleable workloads consisting of a wide range of parallelism variation structures.

With proliferation of multicore computers and multiprocessor systems, an imminent challenge is to efficiently schedule parallel applications on these resources. In contrast to conventional static scheduling, adaptive schedulers that dynamically allocate processors to jobs possess good potential for improving processor utilization and speeding up job's execution. In this paper, we focus on adaptive scheduling of malleable jobs with periodic processor reallocations based on parallelism feedback of the jobs and allocation policy of the system. We present an efficient adaptive scheduler Acdeq that provides parallelism feedback using an adaptive controller A-Control and allocates processors based on the well-known Dynamic Equipartitioning algorithm (Deq). Compared to A-Greedy, an existing adaptive scheduler that experiences feedback instability thus incurs unnecessary scheduling overheads, we show that A-Control achieves much more stable feedback among other desirable control-theoretic properties. Furthermore, we analyze algorithmically the performances of Acdeq in terms of its response time and processor waste for an individual job as well as makespan and total response time for a set of jobs. To the best of our knowledge, Acdeq is the first multiprocessor scheduling algorithm that offers both control-theoretic and algorithmic guarantees. We further evaluate Acdeq via simulations by using Downey's parallel job model augmented with internal parallelism variations. The results confirm its improved performances over Agdeq, and they show that Acdeq excels especially when the scheduling overhead becomes high.

This work addresses the problem of allocating resource-intensive parallel jobs on multicore- and multiprocessor-based systems, where the performance gains largely depend on effectively exploiting application parallelization across the available parallel computing resources. The objective is to find efficient allocation approaches that minimize the parallel jobs' completion time, i.e. makespan. Integrating feedback-driven adaptive strategies, we present a general hierarchical scheduling framework and show that two hierarchical scheduling algorithms: ABG-DS and AG-DS achieve scalable performance in term of makespan regardless of the number of hierarchical levels. Specifically, we prove that both ABG-DS and AG-DS have O(1)-competitive ratio for batched parallel jobs. Extending an existing tool, called Malleable-Lab, we evaluate the performance and scalability of our proposed algorithms and compare with that of well-known EQUI-based strategies. The simulation results demonstrate that both ABG-DS and AG-DS generally outperforms EQUI-EQUI for a wide range of parallel workloads. Moreover, feedback-driven adaptive scheduling algorithms show better scalability when the number of levels increases in the scheduling hierarchy.

The emergence of multi-core computers has led to explosive development of parallel applications and hence the need of efficient schedulers for parallel jobs. Adaptive online schedulers have recently been proposed to exploit the multiple processor resource and shown good promise in theory. To verify the effectiveness of these parallel schedulers, it will be reassuring to test them extensively with various parallel workloads. Unfortunately it is still unknown how the job mixes will eventually evolve for multi-core computers; moreover, it is also non-obvious how the parallelism of a typical job will look like. To evaluate the dynamic behaviors of an adaptive scheduler under various scenarios, an ideal workload model for schedulers should thus allow the user to vary parallelism profiles of individual jobs as well as the job arrival patterns. In this paper, we present a tool called Malleable-Lab, which models malleable parallel jobs by extending the traditional moldable job models. Instead of generating a completely random parallelism, which does not allow clear account of the request-allocate responses, we identify several generic patterns of parallelism variations in parallel programs. Using Malleable-Lab we have evaluated two feedback-driven adaptive schedulers, namely, AG-DEQ (Adaptive-Greedy-DEQ) and ABG-DEQ (Adaptive B-Greedy-DEQ), and the well-known scheduler EQUI (Equi-partition). The results reveal that both feedback-driven schedulers outperform EQUI, but on the other hand suffer from high sensitivity to the scheduling overhead. We also found that ABG-DEQ exhibits better transient responses and stability than AG-DEQ. In conclusion, the tool has enabled us to analyze various aspects of the performance of online schedulers, and we have gained valuable insights for adaptive scheduling of parallel jobs on multiple processors.