Low Earth Orbit (LEO) satellite networks have attracted considerable attention for their ability to deliver global, low-latency broadband Internet services. In this paper, we present a large-scale measurement study of the Starlink network, the largest LEO satellite constellation to date. We first propose an efficient method for discovering active Starlink user routers, identifying approximately 5.98 million IPv6 addresses across 208 regions in 165 countries. Compared to general-purpose IPv6 target generation algorithms, our router-centric approach achieves near-complete coverage and, to the best of our knowledge, yields the most comprehensive known set of active IPv6 addresses for Starlink user routers. Based on the discovered user routers, we further propose an efficient method for mapping the Starlink backbone network and uncover a topology consisting of 49 Points of Presence (PoPs) interconnected by 98 links. We conduct a detailed statistical analysis of active Starlink user routers and PoPs, and further characterize the IPv6 address assignment strategy adopted by the Starlink network. Finally, we analyze the latency of Starlink user routers, propose a method to distinguish different types of users within the same region using outside-in measurement, and identify the ongoing V2 Mini satellite deployment as a potential driver of the performance improvements. The dataset of the Starlink backbone network is publicly available at https://ki3.org.cn/#/starlink-network.

Low-Earth-Orbit (LEO) satellite networks are expanding broadband access, but major Real-Time Communication (RTC) platforms, including Google Meet, Zoom, and Microsoft Teams, still rely on service-point selection mechanisms that appear to be largely shaped by terrestrial-network assumptions. As a result, they may not consistently account for LEO-specific dynamics such as satellite topology and Point-of-Presence (PoP) changes, especially during in-flight connectivity. To characterize RTC service-point selection over Starlink, we measure RTC performance using a Canada-wide testbed and more than 24 hours of in-flight experiments. Our results show clear platform differences. Google Meet tends to align more closely with the serving PoP, consistent with broader observed edge coverage. Zoom follows a regional service-point selection pattern, while Microsoft Teams often remains tied to fixed service points. Moreover, we show that in-flight PoP handovers can shift traffic egress points mid-session, and that RTC platforms respond differently to these changes. In some cases, this interaction leads to suboptimal service-point selections and RTT inflation. Overall, our findings suggest that LEO connectivity can expose mismatches between Starlink egress dynamics and RTC service-point selection, motivating further measurement and LEO-optimized designs for service placement and selection.

Low Earth Orbit (LEO) satellite ISPs promise universal Internet connectivity, yet their interaction with content delivery remains poorly understood. We present the first comprehensive measurement study decomposing Starlink's web content delivery performance decomposed across Point of Presence (PoP), DNS, and CDN layers. Through two years of measurements combining 225K Cloudflare AIM tests, M-Lab data, and active probing from 99 RIPE Atlas and controlled Starlink probes, we collect 6.1M traceroutes and 10.8M DNS queries to quantify how satellite architecture disrupts terrestrial CDN assumptions. We identify three distinct performance regimes based on infrastructure density. Regions with local content-rich PoPs achieve near-terrestrial latencies with the satellite segment dominating 80-90% of RTT. Infrastructure-sparse regions suffer cascading penalties: remote PoPs force distant resolver selection, which triggers CDN mis-localization, pushing latencies beyond 200 ms. Dense-infrastructure regions show minimal sensitivity to PoP changes. Leveraging Starlink's infrastructure expansion in early 2025 as a natural experiment, we demonstrate that relocating PoPs closer to user location reduces median page-fetch times by 60%. Our findings reveal that infrastructure proximity, not satellite coverage, influences web performance, requiring fundamental changes to CDN mapping and DNS resolution for satellite ISPs.

Low earth orbit (LEO) satellite networks are pivotal for sixth-generation (6G) wireless systems, yet their high-speed mobility induces frequent packet loss, causing severe head-of-line blocking delays under traditional retransmission mechanisms. While streaming forward erasure correction (FEC) can mitigate retransmissions, existing packet loss models fail to capture the unique dynamics of LEO networks, causing difficulties in the design and analysis of FEC schemes. This paper addresses this problem through the following contributions. First, based on real-world Starlink measurements, we reveal the inadequacy of conventional loss models such as those based on Markov chains. Second, we propose a Markovian arrival process (MAP) to model LEO packet loss. Using an expectation-maximization (EM) algorithm to fit Starlink traces, we demonstrate its superior accuracy over existing models. Third, based on MAP modeling, we show that the decoding delay of a typical streaming FEC scheme with fixed repair insertion intervals can be analyzed by approximating it as the busy period of a MAP/D/1 queue. Using matrix-analytic methods, we provide a numerical recipe to compute this delay. Simulations validate the precision of the model in predicting delay, offering practical guidelines for FEC design in LEO networks.

Two-path transmission with the involvement of LEO satellites is an increasingly common scenario. LEO networks may offer higher bandwidth compared with some terrestrial networks, such as cellular, but often come with increased variability in latency. Effective traffic allocation to maximize bandwidth utilization in such dynamic environments is challenging. This paper addresses two-path scheduling problem under a high dynamic environment by proposing the FTRL-WRR algorithm, which combines a lightweight, learning-based optimization algorithm with a weighted round-robin scheduler. We model traffic allocation as a 1-dimensional optimization problem and demonstrate the algorithm's effectiveness through evaluations in emulated Starlink-cellular scenarios. Results show that FTRL-WRR improves bandwidth utilization and reduces median completion time by up to 27.71%.

Real-time communication (RTC) systems, such as video conferencing and cloud gaming, depend on effective congestion control (CC) algorithms to manage diverse network conditions and access technologies like Wi-Fi, LTE/5G, and satellite networks. While tools like AlphaRTC and Pandia have significantly advanced CC algorithm development for WebRTC, there is an absence of a unified framework for systematic benchmarking and cross-platform evaluation.

The growing popularity of Low-Earth Orbit (LEO) satellites, which are increasingly cheaper to manufacture and launch, has revolutionized the Internet market. Given the impact of packet loss on the quality of service (QoS) and optimization strategies of communication systems, it is of great significance to model packet loss in LEO satellite networks. The existing packet loss models considered in the literature have different assumptions and characteristics, but it remains unclear whether they are suitable for LEO satellite networks. This paper aims to evaluate several packet loss models. Through the evaluation of several metrics, the advantages and disadvantages of each model are discussed. We then introduce the Markovian Arrival Process (MAP) as a choice for packet loss modeling, and the experimental results show that the performance is better than that of existing models.

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink’s network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines.

Low-earth-orbit (LEO) satellite networks have become very popular in recent years, exemplified by Starlink, OneWeb, Kuiper and others, due to the dramatically reduced launch cost and increased demand for connectivity anytime, anywhere. After an exploration of Starlink access, core and backbone networks, in this paper we focus on the satellite access network (SAN) of Starlink around the world. Particularly, we measure the access performance in terms of one-way delay and round-trip time from user terminal (UT) to ground station (GS) and point-of-presence (PoP), both inside-out and outside-in, and even on inactive dishes. It reveals the unique characteristics of Starlink SAN in terms of satellite-GS scheduling, media access control and user contention, and sheds light on the challenges and opportunities for network protocols and applications. The paper will be complemented by public dataset release and conference on-site demo for the research and industry community.

In light of Starlink's recent rapid growth in constructing a global low-Earth-orbit satellite constellation and offering high-speed, low-latency Internet services, the implications of utilizing Starlink for low-latency live video streaming, particularly in the context of its fluctuating latency and regular satellite handovers events, remain insufficiently explored. In this paper, we conducted a thorough measurement study on the Starlink access network, examining its performance across different protocol layers and at multiple geographical installations, including locations where laser intersatellite links are utilized in practice. We performed a comprehensive latency target-based analysis of low-latency live video streaming with three state-of-the-art adaptive bitrate (ABR) algorithms in dash.js over Starlink. We presented a novel ABR algorithm designed for low-latency live video streaming over Starlink networks which leverages satellite handover patterns observed from measurements to dynamically adjust video bitrate and playback speed. The performance evaluation of the proposed algorithm was conducted using both a purpose-built network emulator and actual Starlink networks. The results demonstrate that the proposed algorithm effectively delivers a better quality of experience for low-latency live video streaming over Starlink networks, characterized by low live latency, high average bitrate, minimal rebuffering events and reduced visual quality fluctuation.

Low-Earth-Orbit (LEO) satellite constellations are narrowing the performance gap between satellite networks and the terrestrial Internet. Low-latency satellite Internet offered by Starlink enables functionalities that are otherwise unachievable with the traditional geosynchronous equatorial orbit (GEO) satellite networks, including low-latency live video streaming, cloud gaming and real-time video conferencing. The absence of a comprehensive and long-term network measurement dataset with a global perspective poses significant challenges for researchers to evaluate the application performance over Starlink networks. In this paper, we introduce LENS, which is a LEO satellite network measurement dataset, collected from 13 Starlink dishes, associated with 7 Point-of-Presence (PoP) locations across 3 continents. The dataset currently consists of network latency traces from Starlink dishes with different hardware revisions, various service subscriptions and distinct sky obstruction ratios. We provide a high-level overview and analysis of the latency performance using the dataset and discuss various use cases. This dataset is useful for researchers who wish to understand the long-term network performance of Starlink and to evaluate and optimize the performance of multimedia applications over satellite networks.

Multipath transport protocols including multipath TCP (MPTCP) and multipath QUIC (MPQUIC) are designed to utilize multiple network paths for simultaneous data transfer. These protocols try to improve network performance and offer better resilience in dynamic network environments. Nonethe-less, the actual performance improvement is heavily reliant on the effectiveness of the multipath scheduling algorithms. In specific scenarios such as adaptive video streaming, most existing solutions feature two separate and independent control loops for multipath scheduling and video bitrate adaptation, while multipath scheduling algorithms are usually transparent to the video bitrate adaptation process. Lacking the context of inter-path differences and intra-path fluctuations for both network throughput and latency may potentially result in a suboptimal quality of experience (QoE) for video streaming. Such circumstances may lead to a reduced video bitrate, increased latency, and a greater number of rebuffering events. In this paper, we present a QoE-driven joint decision-making framework based on contextual multi-armed bandit (CMAB) algorithms to efficiently address multipath adaptive video streaming problems. This approach merges application-layer (playback buffer ratio) and network-layer (throughput and latency) metrics to create a context-aware online learning model, which can adaptively select the ideal network path and bitrate for multipath adaptive video streaming. Both network emulation and real-world experiments demonstrate that the proposed algorithm delivers better QoE, including higher average video bitrate and fewer rebuffering events when compared to independent decision-making algorithms.

Starlink and alike have attracted a lot of attention recently, however, the inner working of these low-earth-orbit (LEO) satellite networks is still largely unknown. This paper presents an ongoing measurement campaign focusing on Starlink, including its satellite access networks, gateway and point-of-presence structures, and backbone and Internet connections, revealing insights applicable to other LEO satellite providers. It also highlights the challenges and research opportunities of the integrated space-air-ground-aqua network envisioned by 6G mobile communication systems, and calls for a concerted community effort from practical and experimentation aspects.

Searchable encryption(SE) allows users to efficiently retrieve data over encrypted cloud data, but most existing SE schemes only support exact keyword search, resulting in false results due to minor typos or format inconsistencies of queried keywords. The fuzzy keyword search can avoid this limitation, but still incurs low search accuracy and efficiency. Besides, most of fuzzy keyword search schemes do not consider malicious cloud servers which may execute a fraction of search operations or forge some results due to various interest incentives such as saving computation or storage resources. To solve these problems, we propose an efficient and Verifiable Ranked Fuzzy Multi-keyword Search scheme, called VRFMS. VRFMS uses locality-sensitive hashing and bloom filter to implement fuzzy keyword search, and employs Term Frequency-Inverse Document Frequency(TF-IDF) to sort the relevant results. Aiming to further improve the search accuracy, we design an improved bi-gram keyword transformation method. Furthermore, the homomorphic MAC technique and a random challenge technique are utilized to verify the correctness and completeness of returned results, respectively. Formal security analysis and empirical experiments demonstrate that VRFMS is secure and efficient in practical applications, respectively.

Abstract To address the gloomy sky and the low contrast caused by the left fog in the existing image dehazing methods, we propose a robust haze removal algorithm for images and videos. First, a sky detection‐based adaptive atmospheric light estimation method is designed for brighter and cleaner restoration results for the sky regions. Second, in order to reconstruct a transmission map in line with the depth variation, we preprocess the input image with texture smoothing to keep the color consistency inside the same planar object and devise a texture smoothing‐based robust transmission estimation method, with which the contrast and color saturation of fog‐free image are greatly promoted. Finally, the restored results are post‐processed with the joint bilateral filter for the purpose of noise removal. What's more, a guided filter‐based temporally coherent atmospheric light smoothing strategy and a Gaussian filter‐based spatial‐temporally coherent transmission smoothing strategy are put forward for video dehazing, which can ensure the spatial as well as temporal continuity of the haze‐free videos. Experimental results show that the recovered haze‐free images and videos have high contrast and color saturation with cleaner sky regions, and the haze‐free videos are free of jittering and flickering phenomena.