As the demand for streaming services surges, content delivery network (CDN) operators face increasing pressure to scale live video delivery without proportionally increasing infrastructure costs. While best-effort edge resources offer a cost-effective extension to traditional CDN capacity, their limited bandwidth and unstable performance pose significant challenges. Our operational experience shows that naively layering such resources onto existing CDN infrastructure falls short in meeting performance and scalability demands. This paper presents RLive, a robust delivery system that scales CDN capacity by integrating best-effort edge resources. RLive features a redundancy-free multi-source data plane to support reliable and cost-efficient live streaming, along with a multi-layer collaborative control plane that combines the global view with local adaptability for scalable user-to-node mapping. Deployed in ByteDance CDN to support large-scale live streaming services with hundreds of millions of daily viewers, RLive has tripled delivery capacity while reducing rebuffering events by 14.9–20.1%.

Underwater Optical Wireless Sensor Network (UOWSN) is a promising technology as it can achieve high-speed communication in underwater environment. However, affected by the uncertainty of complex underwater environment, the network topology of UOWSN is highly dynamic, making it difficult to quantify flexibility or further optimize the topological structure. Additionally, node mobility and energy constraints pose significant challenges to reliable communication. In this paper, we propose a mobility-aware and energy-efficient flexibility-based network topology evaluation model (ME-FEM) for UOWSNs. Then, a reinforcement learning model, termed ME-FEM-DRL, for optimizing the network topology based on ME-FEM is developed, which enables UOWSN to maintain an optimal topology when working in harsh underwater environments. Theoretical analysis proves the NP-hardness of the optimization problem and demonstrates that our algorithm achieves an approximation ratio of$O(\log N)$with optimal parameter boundaries. Simulation results demonstrate that the proposed method can significantly improve the network flexibility. Compared with the five baseline algorithms in simulations, ME-FEM-DRL reduces normalized topology optimization time cost by 64% and extends network lifetime by 95% on average. Test-bed experiments verify the applicability and effectiveness in practical applications for detecting emergent events.

Neighbor discovery poses significant challenges in Underwater Optical Wireless Sensor Networks (UOWSNs) due to the unique characteristics of directional transceivers, line-of-sight communication, and mobility induced by water currents. Traditional methods typically rely on prerequisites and prior knowledge, such as centralized coordination, time synchronization, and information about the number of neighbors, which are often unavailable or impractical in underwater environments. In this paper, we make the first attempt to address the issue ofRobust andEfficientNeighborDiscovery (termed the REND problem) in UOWSNs with zero-knowledge. Here, zero-knowledge refers to the capability that enables sensors to identify neighbors in dynamic underwater optical channel conditions without prerequisites or prior knowledge. We design a zero-knowledge distributed directional neighbor discovery scheme inspired by gear meshing. We then propose a deterministic algorithm for the REND problem based on theoretical analysis. Additionally, to further reduce the discovery delay for the periodic REND problem, we develop a greedy-based approximation algorithm with a performance guarantee. Finally, extensive simulations demonstrate that the proposed scheme reduces the discovery delay by 34.9% on average and achieves an additional 54.4% reduction for periodic neighbor discovery. Furthermore, test-bed experiments are carried out to verify the applicability of our zero-knowledge scheme in real-world scenarios.

WiFi-based identity recognition differs from traditional identification technologies as it is not limited by lighting conditions and does not require dense, specialized sensors or wearable devices. This makes it valuable in modern human–machine interactions. However, the diversity of real-world environmental conditions substantially limits the application of existing WiFi-based identity recognition algorithms, particularly when applied across different environments. As a solution, we introduce the incremental wavelet capsule (IWC) model, which combines a newly designed wavelet convolution layer with a capsule network to accelerate precise feature extraction. We adopt a hybrid incremental learning strategy, solving the catastrophic forgetting1problem in cross-environment tasks and enabling the model to adapt to new environments in the data stream without forgetting the original environment. Furthermore, we developed a customized data augmentation method for WiFi signals, enhancing the model’s adaptability and stability across various environments. Experimental results show that the IWC model achieves an average recognition accuracy of 97.36% across five different environments and maintains an accuracy of 91.5% even when only 5% of the training data from a new environment is used. These findings demonstrate the model’s robust performance and practicality in cross-environment scenarios.

As the demand for online video content drives up bandwidth costs for content providers (CPs), there have been efforts to integrate cost-effective techniques to mitigate their bandwidth expenditure (e.g. using set-top boxes to share content). However, the use of such resources requires considerable effort to balance cost vs. user-perceived quality of service. This paper serves as a first step to quantify this trade-off. We collect and analyze data from a major CP that serves millions of users per day using both traditional CDN resources and alternative cheaper resources. Our analysis reveals that introducing cheaper alternative resources does not always yield anticipated cost savings and may lead to a reduction in quality of experience for users. We provide insights into the reasons behind these issues and propose strategies for better utilization of alternative network resources. We work with a major CP to deploy our proposals, and offer insights on how to better leverage different kinds of bandwidth resources for improved cost-efficiency and streaming delivery.

Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods.

Underwater Optical Wireless Sensor Network (UOWSN) is a promising technology as it can achieve high-speed communication in underwater environment. However, affected by the uncertainty of complex underwater environment, the network topology of UOWSN is highly dynamic, making it difficult to quantify flexibility or further optimize the topological structure. In this paper, we propose a flexibility-based network topology evaluation model (FEM) for UOWSNs. Then, a reinforcement learning model, termed FEM-DRL, for optimizing the network topology based on FEM is developed, which enables UOWSN to maintain an optimal topology when working in harsh underwater environments. Simulation results demonstrate that the proposed method can significantly improve the network flexibility and reduces the time cost for constructing network topology by 41.8% compared with baseline algorithms. Test-bed experiments verify the applicability and effectiveness in practical applications for detecting emergent events.

Underwater Optical Wireless Sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promoting the performance of UOWSNs, while the reliability issue has not been fully addressed. In this paper, we propose a novel reliable data delivery scheme based on a cluster structure. First, we determine the orientation of each sensor for directional optical communication, which aims to establish reliable next-hop links among sensors. We formalize such an orientation problem into a submodular function maximization problem and propose a greedy method with an approximation ratio guarantee to solve it. Then, a cluster head designation scheme is developed to improve the data delivery success rate while minimizing the number of cluster heads. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme. The results reveal that compared with other algorithms, the proposed scheme can ensure a data delivery success rate of over 98.5 % while only keeping 45.3% fewer cluster heads. Furthermore, test-bed experiments are carried out to verify the applicability of the proposed scheme in practical applications.

Underwater Optical Wireless Communication (UOWC) enables high-speed data transmission among Autonomous Underwater Vehicles (AUVs). However, due to cost and weight constraints, AUVs can only carry a limited number of directional optical transceivers. This implies that each AUV can communicate with only 1 to 2 neighbors simultaneously, complicating the establishment of an Underwater Optical Wireless Communication Network (UOWN). To address the networking problem with the degree constraint, we propose a topology generation method based on Hamiltonian paths. The topology achieves improved global connectivity at the cost of local optimality while satisfying the communication device limitations of AUVs. Preliminary results show that the generated topology can reduce the average communication overhead.