This paper evaluates QUIC deployment and performance through active measurements of 1,572 Chinese and 1,953 non-Chinese websites. Results show clear regional and categorical differences: HTTP/3 support is 35.0% for non-Chinese websites but only 5.7% for Chinese websites. Performance gains also depend on network conditions, reducing transfer time by 17.06% in Chinese long-tail networks while slightly regressing by 0.79% in optimized low-latency CDN edge scenarios. Overall, QUIC’s deployment and benefits are highly scenario-dependent.

Integrated Sensing and Communication (ISAC) has emerged as a promising approach to improve resource efficiency in wireless systems, particularly for radar and communication functions. However, traditional methods for orthogonal frequency division multiplexing (OFDM)-based radar processing often involve complex two-step fast Fourier transform (FFT) operations, resulting in high computational demands and hardware resource consumption. To address these challenges, this paper proposes a simplified radar signal processing method tailored for ISAC systems, which transforms the two-step FFT process into a more efficient matrix-vector multiplication using a circulant matrix. Through numerical simulations and hardware resource analysis, it is demonstrated that the proposed approach achieves substantial reductions in computational burden. Compared to the traditional two-step FFT method, the proposed algorithm achieves a minimum reduction of 33.59% in hardware multiplier consumption when the matrix dimension is less than 64. This technique enables more efficient radar and communication processing in ISAC systems, making it more viable and cost-effective and paving the way for more advanced radar and communication integration in future ISAC applications.

Quantile estimation is critical for diverse applications, including database management and network traffic monitoring. Probabilistic quantile sketches are widely employed in practice, with the KLL sketch (introduced in 2016) being particularly notable for its theoretically space-optimal properties. However, KLL overlooks the inherent repetition of elements often present in real-world data streams. Such streams are frequently highly skewed, characterized by ''hot items''-items that appear with high frequency. The KLL sketch processes these hot items without accounting for their prevalence, resulting in suboptimal space utilization due to redundant insertions and storage. To overcome this limitation, we propose Cooled-KLL, an enhanced KLL sketch. Cooled-KLL introduces a novel ''Hot Filter'' structure that efficiently identifies and stores hot items as compact key-value pairs. This mechanism ensures that only ''cold'' (less frequent) items are subsequently processed by the core KLL sketch. Our approach significantly reduces memory consumption without compromising processing speed. Extensive experiments demonstrate that Cooled-KLL consistently outperforms five other state-of-the-art algorithms, achieving up to 2.5 orders of magnitude higher accuracy compared to the standard KLL sketch.

Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP’s complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced.

The flow-based OpenFlow architecture decouples the control plane and data plane, and it has involved great evolution towards traditional networks. A particular important issue in OpenFlow architecture is controller capacity, which can be defined as the number of switches a controller can manage. In this paper, we model the flow set-up requests from switches to controller as a batch arrival process Mk/M /1. Further, we analyze the controller performance with queuing theory, and derive the expression of average flow service time. Under the circumstance of a limited flow set-up time, the number of switches is determined, this provides a method to evaluate the controller capacity. Moreover, we extend the scene of a single controller to multiple controllers. All of these results are meaningful to large scale OpenFlow network deployment in the future.