This paper presents ArmNet, a mobile sensing system for capturing the trajectory of the wrist using measurements from wrist-worn IMU devices, specifically targeting robust interaction within Internet of Things (IoT) ecosystems. Unlike existing solutions that directly map IMU data to joint positions, ArmNet integrates physical kinematic constraints with neural network modeling. This hybrid approach is crucial for resource-constrained IoT devices where computational overhead and sensor limitations are primary concerns. Specifically, kinematic priors capture spatial dependencies between the elbow and wrist, generating physics-guided intermediate features that reduce the solution space. These features, together with raw IMU data, are fed into a recurrent neural network to learn joint displacement vectors, which are then integrated into continuous trajectories. Extensive experiments show that ArmNet achieves robust and accurate arm tracking, generalizing well across users and motion patterns, thereby enabling a new modality for seamless human-computer interaction in smart environments.

Facial Expression Recognition (FER) serves as a foundational sensory interface for Human-Centric IoT, supporting applications such as smart healthcare monitoring and affective intelligent environments. However, real-world performance is often hindered by theSemantic Gap, where models confuse visually similar expressions that arise from fundamentally different physiological muscle movements. To bridge this gap, we propose the Physical-Semantic-Aware Multimodal Framework (PSM-FER), which introduces 3D Blendshape (BS) coefficients as explicit physical priors to encode high-level muscle motion semantics. Our framework utilizes two synergistic pathways:Direct Physical Gating(DPG) for robust feature modulation andSemantic-Guided Spatial Attention(SGSA) for anatomical spatial recalibration. Additionally, an auxiliary physical regression task enforces anatomical consistency by regularizing the latent features to follow underlying biomechanical laws. Extensive experiments on the RAF-DB dataset demonstrate that PSM-FER achieves an accuracy of 92.37%, establishing a robust and interpretable foundation for affective sensing in complex IoT ecosystems.

In an era marked by technological innovation, visual applications have become ubiquitous in everyday life. Harnessing the power of computer vision, these applications process and interpret video data from edge cameras, facilitating tasks such as object detection and vehicle counting. Yet, implementing complex deep learning models on cameras with limited computational capacity poses significant challenges. Furthermore, the bandwidth constraints and fluctuating nature of wide-area networks present substantial difficulties for video analysis systems dependent on cloud computing. This paper first characterizes the relationship between different parameter combinations (such as frame rate and resolution) and video analysis accuracy through offline analysis. It proposes a video stream analysis configuration selection scheme, SPStream, for slowly changing scenes, and a configuration file switching strategy, SPStream+, for rapidly changing scenes. These strategies use idle resources at the camera edge end to select the optimal configuration in real-time, adjust video encoding quality, and dynamically switch configuration files based on the changing states of object motion. Finally, a real-time video stream analysis system for vehicle counting and pedestrian detection suitable for both scenarios is designed, which saves bandwidth to the greatest extent while meeting the accuracy requirements of users and achieving high accuracy of video analysis.

The lack of actionable guidance is a fundamental limitation in Action Quality Assessment (AQA), as traditional methods provide overall scores without offering specific insights for improvement. Moreover, existing interpretable approaches often rely on expensive supervised spatial annotations or yield noisy, unsigned saliency maps. To address these challenges, we propose Learning Interpretability Based Supervoxels (LIBS), a novel framework for generating instructional feedback. Distinguishing itself from fully supervised methods, LIBS employs an unsupervised soft-clustering mechanism to segment videos into coherent supervoxels without requiring pixel-level mask annotations. This allows for scalable, fine-grained spatio-temporal analysis while preserving action continuity. Furthermore, we introduce a sensitivity propensity analysis to quantify the contribution of each supervoxel. Unlike traditional attribution methods, this mechanism explicitly decomposes the quality score into positive (strengths) and negative (flaws) components, enabling the system to decode abstract scores into concrete, actionable instructions. Experimental validation across multiple datasets demonstrates that LIBS achieves superior interpretability and efficiency compared to state-of-the-art baselines, marking an improvement from diagnostic to instructional AQA applications.

Millimeter-wave (mmWave) 5G-V2X communications face significant challenges in real-time beam alignment within high-mobility vehicular networks. While environmentaware beam prediction methods mitigate channel estimation overhead, their efficacy is severely compromised by modality data loss stemming from lighting variations, adverse weather, or sensor failures. To address this issue, we propose a Cross-modal Losstolerant Beam Prediction model (CLBP). CLBP robustly fuses RGB camera and LiDAR data, employing a novel cross-modal attention mechanism to achieve resilient feature alignment across these heterogeneous modalities. Furthermore, a Branch Features Dynamic Fusion (BFDF) module adaptively reweights modality features, suppressing noise from degraded inputs and promoting effective information propagation to enhance resilience. To facilitate realistic evaluation, we introduce a Data-Conditioned Missingness Mechanism (DCMM), which augments the DeepSense 6G V2V dataset with meticulously simulated sensor failure scenarios. Experimental results demonstrate CLBP's superior performance, achieving 94.48% Top-5 beam prediction accuracy even under 10% modality loss, and a 29% reduction in average power loss compared to baseline methods. These findings demonstrate CLBP's significant robustness in dynamic vehicular environments and its capacity to maintain consistent, high-performance beam prediction despite challenging data imperfections.

Sign Language recognition is practically important in various scenarios such as smart home, medical rehabilitation, and intelligent industry. Compared with wireless sensing and computer vision methods, data glove-based methods have gained a plenty of attention, because they can perform well even in the environments with multi-path noise or visual occlusion. However, existing data glove-based methods usually require complex calibration and laborious dataset collection, and suffer from accumulated error. To address these challenges, we introduce a robust sign language recognition system with cross-domain generation, called SmartGlove, the first approach to achieve robust sign language recognition. To avoid complex calibration process, we propose a customized feature set that can enable user-insensitive and unintentional system calibration. To avoid the labor cost in training data collection, we propose a cross-domain data transformation technique to generate training data in target domain. To eliminate the accumulated error of sentence recognition, we utilize a context-based calibration method considering correlation among adjacent words. We implement SmartGlove with COTS devices, and extensive experiments reveal that SmartGlove achieves accuracy exceeding 97.11% for 30 sign language words, with an average recognition time of 47 milliseconds per word. Furthermore, the system recognizes 30 common sign language sentences with accuracy of 97.17%.

The application scenarios of drone collaboration are rapidly expanding, such as the low-altitude economy and wildfire protection. Blockchain-based drone collaboration requires a consensus mechanism to ensure efficient and secure consistency among large-scale distributed nodes. However, the existing consensus mechanism has problems with poor fault tolerance of topology and rigid proposal concurrency. To this end, this paper proposes AirBFT, an efficient and robust consensus mechanism for large-scale drone collaboration. First, this paper designs a new four-layer network topology, using upper-member and lower-member communication, while ensuring the maximum 1/3 resilience and fanout of √N. Secondly, this paper proposes a dynamic pipelining algorithm to adjust the parallelism of proposals according to the real-time network status. Finally, this paper proposes a committee sampling technology based on the EigenTrust algorithm to reduce the impact of the malicious behavior of Byzantine nodes. Experiments based on the public consensus framework show that compared with Kauri and HotStuff, the proposed AirBFT reduces transaction confirmation delay by 58%, the throughput is increased by 1.9 times, and it can ensure efficient operation with a 1/3 Byzantine node ratio.

Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a better quality of service and become MEC providers. These MEC providers require to rent wide area network (WAN) connections to transfer industrial data, which is a considerable expense. In this article, we propose a framework calledSublessorto reduce the WAN transmission cost for a group of cooperative MEC providers. The key idea ofSublessoris allowing some specific MEC providers to act as Internet transit brokers, transmitting not only their own network traffic but also the traffic of their partners under a reasonable reselling price. This article formulates the problem as a mixed-integer programming and finds the most suitable broker number and corresponding reselling price without damaging the profit of both brokers and partners by a deep-reinforcement-learning-based algorithm. Experimental results show that our algorithm can significantly reduce the traffic transmission cost by up to 35%.

The Inter-datacenter transfer is a fundamental service for global cloud applications. Geo-distributed data centers become an essential resource for their application performance which may be destroyed by network congestion. Recent years, most inter-datacenter transfer methods focus on allocating transfers by bandwidth allocation to achieve low cost or high utilization. However, the congestion condition is rarely considered in these works. In this article, we introduce a congestion-aware traffic allocation method named CONA (CONgestion-Aware), whose target is to maximize the profit of allocation transfer among multiple data centers. On this purpose, a maximizing optimization model is proposed, and an efficient link grading strategy is presented. A matrix transformation method is also introduced to simplify the optimization problem. Furthermore, the link congestion condition is considered by the controller, as well as the prediction on link congestion. To verify our proposed method, simulation model is established and comprehensive experiments are conducted. The experimental results show that our method brings higher profit than fair share and greedy traffic allocation method.

In data centers, a lot of cluster computing applications follow the coflow working pattern. That is, a collection of flows between two groups of machines is semantically related. On the other hand, network function virtualization sufficiently improves the performance of data center networks. It however complicates the network environment by introducing many multi-function middleboxes each with multiple resources. Coflows encounter extremely different processing delays under diverse network functions. Prior coflow scheduling schemes are insufficient to guarantee the coflow completion time in the multi-resource environment. In this paper, we propose, model, and analyze the coflow scheduling problem in the multi-resource environment. We present a dedicated method, data rate guarantee for coflow (DRGC), to guarantee the data rate requirements of coflows in this situation. DRGC prioritizes the coflow scheduling sequence, assigns precise data rates for coflows, and deploys a packet scheduling algorithm at middleboxes to guarantee their transmissions. In our experiments, DRGC efficiently guarantees the completion times of coflows and supports 15% more workload, compared with other scheduling schemes.

Inefficient bandwidth sharing in a datacenter network, between different application frameworks, e.g., MapReduce and Spark, can lead to inelastic and skewed usage of link bandwidth and increased completion times for the applications. Existing work, however, either solely focuses on managing computation and storage resources or controlling only sending/receiving rate at hosts. In this paper, we present CoMan, a solution that provides global in-network bandwidth management in multiplexed data centers, with two goals: improving bandwidth utilization and reducing application completion time. CoMan first designs a novel abstraction of virtual link groups (VLGs) to establish a shared bandwidth resource pool. Based on this pool, CoMan implements a three-level bandwidth allocation model, which enables elastic bandwidth sharing among computing frameworks as well as guarantees network performance for the applications. CoMan further improves the bandwidth utilization by devising a VLG dependency graph and solves an optimization problem to guide the path selection using a 32-approximation algorithm. We conduct comprehensive trace-driven simulations as well as small-scale testbed experiments to evaluate the performance of CoMan. Extensive simulation results show that CoMan improves the bandwidth utilization and speeds up the application completion time by up to 2.83× and 6.68×, respectively, compared to the ECMP + ElasticSwitch solution. Our implementation also verifies that CoMan can realistically speed up the application completion times by 2.32× on average.

In data centers, a lot of cluster computing applications follow a coflow pattern. On the other hand, network function virtualization (NFV) sufficiently improves the performance of the data center network. However, coflows encounter extremely different processing delays under diverse network functions. Traditional coflow scheduling schemes become insufficient in this situation. Based on the observation that the benefit of coflows is closely related to the data rates of flows, we propose DRGC (Data Rate Guarantee for Coflow) to guarantee the data rate requirements of coflows in the NFV environment. We prioritize the scheduling sequence of coflows, precisely allocate data rates for individual flows, and design an efficient scheduling algorithm. DRGC maintains the desired data rates of coflows with higher priorities at middleboxes and leaves more scheduling opportunities to the ones with lower priorities. In the large-scale trace-driven experiment, DRGC efficiently guarantees the data rate requirements of coflows and supports more 15% workload, compared with other scheduling schemes.

Traditional networks are surprisingly fragile and difficult to manage. Software Defined Networking (SDN) gained significant attention from both academia and industry, as if simplify network management through centralized configuration. Existing work primarily focuses on networks of limited scope such as data-centers and enterprises, which makes the development of SDN hindered when it comes to large-scale network environments. One way of enabling communication between data-centers, enterprises and ISPs in a large-scale network is to establish a standard communication mechanism between these entities. In this paper, we propose Zebra, a framework for enabling communication between different SDN domains. Zebra has two modules: Heterogeneous Controller Management (HCM) module and Domain Relationships Management (DRM) module. HCM collects network information from a group of controllers with no interconnection and generate a domain-wide network view. DRM collects network information from other domains to generate a global-wide network view. Moreover, HCM supports different SDN controllers, such as floodlight, maestro and so on. To test this framework, we develop a prototype system, and give some experimental results.