The Internet of Things (IoT) enables intelligent transportation services by connecting vehicles with roadside infrastructure and generating time-sensitive data. To support low-latency processing, edge-IoT vehicular networks deploy distributed edge servers near mobile users. However, high vehicular mobility and heterogeneous edge resources make it difficult for existing approaches to effectively exploit spatio-temporal mobility patterns and to support real-time offloading decisions. To address these challenges, this paper proposes TPADO, a Trajectory Prediction-Aware Dynamic Offloading framework that integrates a Spatio-Temporal Graph Convolutional Network (ST-GCN) with a multi-agent decision mechanism based on Proximal Policy Optimization (PPO). TPADO employs ST-GCN to perform high-fidelity trajectory prediction by explicitly modeling the graph structure of vehicular networks, thereby enabling proactive candidate-node selection and mobility-aware result delivery. Based on the predicted mobility information, we further design a hierarchical multi-strategy offloading framework, where a DRL-based policy layer adaptively selects offloading strategies, and a rule layer performs fine-grained node assignment and task partitioning. Extensive simulation results demonstrate that TPADO achieves the best overall performance among the compared methods. Compared with the centralized DQN baseline, it reduces global average latency by 6.4% and system saturation by 3.01 percentage points, while also delivering higher throughput and task success rate. These results validate the effectiveness and generalizability of the proposed framework.

As a key application of the Agricultural Internet of Things (AIoT), multimodal agriproduct anomaly detection faces severe privacy and security challenges. Existing federated learning (FL) methods struggle to capture fine-grained cross-modal correlations and to address the modality under-optimization problem, thereby limiting both detection accuracy and privacy levels. To this end, this paper proposes a privacy-preserving federated multimodal agriproduct anomaly detection scheme in AIoT based on modality-under-optimized knowledge distillation (PAMAD), achieving high-utility anomaly detection with enhanced privacy protection. Specifically, we develop a hierarchical privacy protection method for multimodal fine-grained alignment fusion based on meta-learning (HPPMF), which effectively captures cross-modal semantic correlations and protects the privacy of fused features. In addition, we propose a multi-task pre-training algorithm based on modality-under-optimized knowledge distillation (MTLMKD) to alleviate modal imbalance. We further design a pre-training dynamic protection algorithm based on adaptive gradient quantization (PDAGC) to ensure model security. Subsequently, a multimodal agriproduct anomaly detection method with a self-supervised denoising encoder (MAPADSE) is introduced to improve detection accuracy under noisy conditions. Rigorous security analysis demonstrates that the PAMAD scheme satisfies differential privacy. Experimental results show that, compared with existing state-of-the-art methods, our PAMAD scheme improves AUROC and accuracy by 7.56% and 8.71%, respectively, achieving a desirable balance between privacy protection and anomaly detection accuracy in AIoT services.

To meet the high performance requirement of real time and critical applications in industrial Internet of Things, modern lossless ethernet data center networks (DCNs) deployed with remote direct memory access and priority-based flow control (PFC) are dedicated to delivering low latency and high bandwidth. However, existing load balancing schemes either lack sub-round-trip-time congestion sensing or fail to accurately detect and reroute flows that cause congestion in PFC-enabled lossless DCNs. Therefore, we propose LBoDSN, an in-network load balancing for lossless DCNs using direct switch notification (DSN) for fast congestion flow detection, to address above challenges. LBoDSN tracks the evolution of ingress queue lengths at destination switches to anticipate the initiation of PFC pause, precisely identifies congested flows before PFC pause, and then sends DSNs to source switches to perform rerouting. After rerouting, the congestion notification packet associated with the previous path is selectively discarded to improve transmission performance. Experiments under realistic workloads reveal that, LBoDSN outperforms CONGA by 13%–65%, and 25%–80% in average and tail Flow Completion Times (FCTs), respectively. Compared to ConWeave, LBoDSN achieves approximately 9% improvement in both average and tail FCTs, while reducing switch queue consumption for reordering.

In recent years, incomplete multi-view clustering (IMVC) has attracted considerable attention for its ability to acheieve effective clustering results through the integration of key information amidst missing view. However, the existing IMVC methods are still faced with 3 limitations: (1) They exhibit deficiencies in considering the weight distribution within views, (2) they ignore the varying contributions of different views to the common consistent representation, and (3) they struggle to sufficiently extract and recover the vital information within incomplete views. To address these limitations, we incorporates local reasoning and correlation analysis to design an incomplete multi-view clustering method(IMVCLRCA), which introduces a new strategy of feature learning and missing view recovery, fully exploiting local similarity and structural continuity within views and performing precise local reasoning recovery on missing data. By maximizing mutual information between views through contrastive learning, we achieve the consistent representation learning of multiple views. Furthermore, based on semantic consistency, we comprehensively consider the correlation between views, utilized a weight matrix to fuse cross-view data, and constructed a view with a correlation structure, ultimately obtaining a common consistent representation. We conduct extensive experiments on 4 public datasets including Caltech101-20, BBCSport, Scene-15, and LandUse-21. Experimental results demonstrate that IMVCLRCA has higher accuracy and robustness compared to the state-of-the-art IMVC methods. The anonymous code of this project is available on GitHub at https://github.com/ggg2111/2025WSDM-IMVCLRCA.

The network load balancing mechanism plays a pivotal role in enhancing transmission performance in modern cloud data centers. Conventional flowlet-based approaches at host side offer a balance between performance and deployment simplicity. However, their passive load balancing strategy restricts rerouting opportunities, and lacks precision in congestion detection as it necessitates at least one round-trip time (RTT) to acquire end-to-end congestion feedback. To overcome the performance loss caused by the above limitations, we propose BurstLoader, an enhanced flowlet-based mechanism that adapts to varying traffic burst intensities and improves congestion detection accuracy. BurstLoader proactively reroutes congested flows when no new flowlets are detected, while simultaneously avoiding the rerouting of flowlets that are in good transmission states. Furthermore, BurstLoader incorporates delay and its gradient for a more nuanced and precise congestion detection. The extensive experiments demonstrate that BurstLoader achieves a significant reduction in flow completion time (FCT) by up to 48% compared to other flowlet-based solutions deployed at the end host, while maintaining competitive performance even against schemes that require custom switches under realistic workloads.

Datacenter network load balancing schemes handle network traffic generated by massive different applications. Some packet-based or flowlet-based schemes capture traffic bursts for load balancing. But frequent rerouting within a flow can mix ACKs belonging to different paths in congestion control protocols, which adversely affects flow rate control. Besides, performance optimization effect of flowlet-based schemes may be less noticeable under smoother workloads. And several packet-based mechanisms implemented at end hosts can proactively reroute congested flows based on flow status even under a smooth workload, but fail to improve performance with the bursty nature of traffic. Therefore, existing schemes cannot adapt to different burst levels of dynamic traffic in datacenter networks and still have significant performance flaws in some ways. This paper proposes IntFlow, a novel load balancing scheme that integrates end-host based per-packet monitoring of flow status with flowlet switching in programable switches. IntFlow proactively reroutes flows experiencing network congestion or failures and avoids doing flowlet switching for small flows with high sending rate. IntFlow can provide excellent performance under both high burst and smooth workloads. Finally experimental results show IntFlow achieves up to 32% and 28% better performance than CONGA and Hermes under asymmetries, respectively.

In datacenter networks, multipath exists to facilitate parallel data transmission. Taking deployment challenges into account, some optimized alternatives (e.g. CLOVE, Hermes) to ECMP balance load at the virtual edge or hosts. However inaccuracies of congestion detection and reaction exist in these solutions. They either detect congestion through ECN and coarse-grained RTT measurements or are congestion-oblivious. These congestion feedbacks are not sufficient enough to indicate the accurate congestion status under asymmetry. And when rerouting events occur on multiple paths, ACKs with congestion feedback of other paths can improperly influence the current sending rate. Therefore, we explore how to balance load by solving above inaccuracy problems while ensuring good adaptation to commodity switches and existing network protocols. We propose ALB, an adaptive load-balancing mechanism based on accurate congestion feedback running at end hosts, which is resilient to asymmetry. ALB leverage a latency-based congestion detection to precisely route flowlets to lighter load paths, and an ACK correction method to avoid inaccurate flow rate adjustment. In large-scale simulations ALB achieves up to 7% and 40% better flow completion time (FCT) than CONGA and CLOVE-ECN under asymmetry.

With the rapid growth of application migration, the anonymity in data center networks becomes important in breaking attack chains and guaranteeing user privacy. However, existing anonymity systems are designed for the Internet environment, which suffer from high computational and network resource consumption and deliver low performance, thus failing to be directly deployed in data centers. In order to address this problem, this paper proposes an efficient and easily deployed anonymity scheme for software defined networking-based data centers, called mimic channel (MIC). The main idea behind MIC is to conceal the communication participants by modifying the source/destination addresses, such as media access control (MAC) and Internet protocol (IP) address at switch nodes, so as to achieve anonymity. Compared with the traditional overlay-based approaches, our in-network scheme has shorter transmission paths and less intermediate operations, thus achieving higher performance with less overhead. We also propose a collision avoidance mechanism to ensure the correctness of routing, and three mechanisms to enhance the traffic-analysis resistance. To enhance the practicality, we further propose solutions to enable MIC co-existing with some MIC-incompatible systems, such as packet analysis systems, intrusion detection systems, and firewall systems. Our security analysis demonstrates that MIC ensures unlinkability and improves traffic-analysis resistance. Our experiments show that MIC has extremely low overhead compared with the base-line transmission control protocol (TCP) (or secure sockets layer (SSL)), e.g., less than 1% overhead in terms of throughput. Experiments on MIC-based distributed file system show the applicability and efficiency of MIC.

With the rapid growth of application migration, the anonymity in data center networks becomes important in breaking attack chains and guaranteeing user privacy. However, existing anonymity systems are designed for the Internet environment, which suffer from high computational and network resource consumption and deliver low performance, thus failing to be directly deployed in data centers. In order to address this problem, this paper proposes an efficient and easily deployed anonymity scheme for SDN-based data centers, called MIC. The main idea behind MIC is to conceal the communication participants by modifying the source/destination addresses (such as MAC, IP and port) at switch nodes, so as to achieve anonymity. Compared with the traditional overlay-based approaches, our in-network scheme has shorter transmission paths and less intermediate operations, thus achieving higher performance with less overhead. We also propose a collision avoidance mechanism to ensure the correctness of routing, and two mechanisms to enhance the traffic-analysis resistance. Our security analysis demonstrates that MIC ensures unlinkability and improves traffic-analysis resistance. Our experiments show that MIC has extremely low overhead compared with the base-line TCP (or SSL), e.g., less than 1% overhead in terms of throughput.

Continuously enriched distributed systems in data centers generate much network traffic in push-style one-to-many group mode, raising new requirements for multicast transport in terms of efficiency and robustness. Existing reliable multicast solutions, which suffer from low robustness and inefficiency in either host-side protocols or multicast routing, are not suitable for data centers. In order to address the problems of inefficiency and low robustness, we present a sender-initiated, efficient, congestion-aware and robust reliable multicast solution mainly for small groups in SDN-based data centers, called MCTCP. The main idea behind MCTCP is to manage the multicast groups in a centralized manner, and reactively schedule multicast flows to active and low-utilized links, by extending TCP as the host-side protocol and managing multicast groups in the SDN-controller. The multicast spanning trees are calculated and adjusted according to the network status to perform a better allocation of resources. Our experiments show that, MCTCP can dynamically bypass the congested and failing links, achieving high efficiency and robustness. As a result, MCTCP outperforms the state-of-the-art reliable multicast schemes. Moreover, MCTCP improves the performance of data replication in HDFS compared with the original and TCP-SMO based ones, e.g., achieves 101% and 50% improvements in terms of bandwidth, respectively.