LiDAR is one of the most important sensors in the field of autonomous driving and allows for better and more accurate perception of the changes in the surrounding environment. Most of the existing 3D object detection methods use data augmentation and feature fusion enhancement to improve the performance of detection, but the majority of the methods ignore the handling of sample imbalance problems during data augmentation. Also, the designed feature fusion and enhancement methods were not well suited to work with the enhancement methods. To this end, we developed a combined method involving data augmentation and feature enhancement. The designed approach has two main objectives: 1) to address the problem of unbalanced sample distribution in detection scenes through data augmentation, and 2) to enhance feature perception using a special feature enhancement module. Our proposed method solves the problem of class imbalance by directly increasing the number of pedestrian samples in the scene through mixed data augmentation, i.e., RG-Aug. In addition, we introduce the Decoupling and Attention Fusion module (DAF), which combines classification headers with high-level features and prediction branches with low-level features. Leverage data features between different layers of features to get a more robust feature representation. Finally, the multi-scale pyramid attention enhancement module is designed to achieve feature enhancement of multi-scale features by means of attention to improve the detection ability of small objects in the scene, especially the detection ability of pedestrians. Our method can achieve 1.57%, 2.16%, and 2.05% performance improvement on the KITTI dataset for Easy, Mod, and Hard samples, respectively. Furthermore, for the detection of pedestrians, our method has a significant competitive advantage over other state-of-the-art techniques with a mAP of 73.42%.

Unsupervised anomaly detection in multivariate time series can prevent large-scale system failures and is crucial for various applications. Most existing methods only consider a single temporal pattern and insufficiently model the normal pattern, causing the model to learn incorrect temporal patterns from anomalous or noisy data. This poses a significant challenge for accurate anomaly detection. To overcome these challenges, we introduce a new dynamic decomposition and reconstruction anomaly detection algorithm, DMRAD. DMRAD captures various regular patterns of multivariate time series by designing a dynamic decomposition module that learns trend and seasonal features. By integrating improved channel and temporal attention mechanisms, DMRAD effectively learns the correlations within the sequence and dependencies across different sequences, thereby enhancing the model's capacity to distinguish between features and extract relevant information. DMRAD incorporates a latent anomaly-noise detection algorithm to identify and suppress the influence of noise and latent anomalies, elevating the overall accuracy of anomaly detection. Extensive experimental comparisons demonstrate that DMRAD achieves state-of-the-art performance on a variety of datasets for real-world application scenarios.

Reliable anomaly detection in multivariate time series is essential for distributed sensor networks, industrial IoT platforms, and cloud-edge infrastructures, where failures or attacks must be identified promptly to ensure system reliability and security. The task is challenging due to the scarcity and diversity of anomalies and the limited availability of labeled data in heterogeneous environments. To tackle these challenges, we propose CLASP (Contrastive Learning with Asymmetric Stochastic Patching), a framework that integrates clustering-guided patch mining, asymmetric augmentation, and stochastic patch exchange to provide strong contrastive supervision. This design enables the model to capture subtle and localized anomalies while preserving normal temporal dynamics. Experiments on five benchmark datasets-covering server logs, industrial control systems, and spacecraft telemetry-show that CLASP consistently outperforms competitive baselines, improving the average F1-score by 7.4 %. Well-suited for deployment in real-world distributed and ubiquitous computing environments, CLASP establishes a principled basis for robust anomaly detection, with future work targeting real-time edge deployments.

The development of surround-view cameras is crucial for the advancement of autonomous driving. Utilizing depth information and image features to simulate LiDAR bird’s-eye-view (BEV) features can accomplish efficient 3D object detection tasks. Existing dense BEV generation methods heavily rely on the use of depth features, however, the suboptimal exploitation of these features often results in ambiguity in object location and feature representation during the BEV generation process. To address this, we have designed a hybrid encode and distillation method to enhance 3D object detection performance, termed HybridBEV. Initially, we designed the HybridEncode module, which employs a resampling strategy of depth features in voxel space to obtain BEV features that more accurately reflect the distribution of objects. Subsequently, we introduced multiple distillation methods to supervise the network’s voxel features and BEV feature representations, assisting the student network in learning critical features from the teacher model and ensuring that BEV features can more distinctly represent object distribution. Furthermore, during network training, we loaded pre-trained weights from the teacher network to guide network optimization and accelerate training. Extensive experiments on the nuScenes benchmark demonstrate that HybridBEV can effectively improve the performance of the student network and outperform previous state-of-the-art methods based on surround-view cameras. The code will be published athttps://github.com/wjyxx/HybridBEV

LiDAR is a key sensor for accurately sensing of the environment in autonomous driving. While existing 3D object detection methods generally rely on data augmentation and feature fusion to improve performance, the challenge of dealing with sample imbalance is often overlooked. We design a novel 3D detection network, IFNet, that tackles these issues by introducing mutually reinforcing data augmentation and feature enhancement strategies. It aims to achieve a dual purpose: 1) correcting the category imbalance by directly enhancing pedestrian samples using mixed data augmentation, i.e., RG-Aug; and 2) enhancing feature perception by introducing the decoupling and attention fusion module (DAF). DAF enables robust feature representations across different layers, improving the detection performance, especially for small objects in the scene. Comprehensive experiments on the KITTI dataset and comparisons with state-of-the-art methods demonstrate the superiority of our proposed approach.

The breathtaking progress in machine learning has motivated studies in learning-based congestion control algorithms, which are expected to adjust congestion window choices according to the dynamic network environment. Nevertheless, existing learning-based congestion control protocols are mostly designed and trained for a specific network environment. When applied to a different network environment, the previously trained model may see a considerable degradation in performance. As the rapid development of communication technologies has given rise to the emergence of a diversity of new networks, it is desirable for learning-based congestion control models to quickly transfer to different network environments. Driven by this motivation, we propose a novel TRansferable Congestion Control (TRCC) protocol, which takes full advantage of both reinforcement learning and transfer learning to intelligently cope with network congestion in scenarios. The key idea to enable a fast transfer from the source network environment to the target network environment is to fine-tune a well-trained model in the source network to suit the target network in a time-effective way. We theoretically prove the transferability and quick convergence of our proposed transfer reinforcement learning-based congestion control algorithm by deriving the Markov transition matrix and the similarity of the reward function. Our experiments validate that TRCC can converge in a new network environment in a short time while achieving comparable performance with baseline algorithms.

With the networks becoming complex, traditional congestion control protocols face increasing challenges in providing high-quality services for users. Traditional TCP and its variants fail to achieve high performance due to drawbacks in architectural design: predefined actions to specific network feedback. In this paper, we develop a learning-based TCP congestion control scheme RLCC, featuring a deep Q-network framework, in which senders learn the optimal control policies from observations instead of predefined rules. To apply DQN algorithms to congestion control problems, we first prove theoretically that congestion control problems are of Markov property. Therefore, the model-free reinforcement learning algorithm DQN can be used to solve congestion control. This is because the application of DQN to the network congestion control problem is convergent, and there exists an optimal strategy to obtain the best action for congestion control. We improved the network's performance by carefully designing the reward function and choosing the appropriate form and parameters through extensive experimentation. Extensive experiments on real-world environments of Pantheon via AWS confirm that RLCC can achieve utilization improvements over the traditional TCP congestion control schemes with higher throughput and lower transmission latency, and outperform the recently proposed learning-based congestion control protocol.

The advent of new network architectures has resulted in the rise of network applications with different network performance requirements: live video streaming applications require low latency. In contrast, file transfer applications require high throughput. Existing congestion control protocols may fail to simultaneously meet the performance requirements of these different types of applications since their designed objective function is fixed and difficult to readjust according to the needs of the application. In this paper, we develop MOCC (Multi-Objective Congestion Control), a novel multi-objective congestion control protocol that can meet the performance requirements of different applications without the need to redesign the objective function. MOCC leverages multi-objective reinforcement learning with preferences in order to adapt to different types of applications. By addressing challenges such as slow convergence speed and the difficulty of designing the end of the episode, MOCC can quickly converge to the equilibrium point and adapt multi-objective reinforcement learning to congestion control. Through an extensive array of experiments, we discover that MOCC outperforms the most recent state-of-the-art congestion control protocols and can achieve a trade-off between throughput, latency, and packet loss, meeting the performance requirements of different types of applications by setting preferences.

Smart city communication networks hold the promise of harnessing the Internet of Things, smart devices, intelligent energy grids, autonomous cars, and many other data architectures to improve quality of life for every citizen. As time and progress have gradually shifted this concept from an idea into a reality, the possibilities for what services a smart city might provide have exploded. Along with that expansion, the number of devices that may need to be supported across a communications network has multiplied enormously. However, existing congestion control protocols are not equipped to support this higher requirement for network performance. New protocols are needed to provide higher data throughputs, reduce queuing delays and packet loss, and maintain stable and reliable communications pathways. The end-to-end congestion control protocol presented in this article does all these things. Called high throughput congestion control (HTCC), the framework takes full advantage of reinforcement learning and a fast growth algorithm to adaptively control the bytes in flight across the network's links to suit the prevailing network conditions. The result is a protocol that not only finds the optimal tradeoff between throughput, latency, and packet loss, but also significantly speeds up the learning process to avoid wasting network resources, use all free bandwidth and maximize network utilization. A comprehensive set of experiments with HTCC and six state-of-the-art protocols-Copa, PCC Vivace, PCC-Allegro, TCP Cubic, TaoVA-100x, and FillP-Sheep-demonstrate that, in most conditions, HTCC is able to make the best tradeoff between throughput, delay, and loss rate.

Vehicular ad hoc networks ( VANETs ) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.