The emerging low earth orbit (LEO) satellite Internet has gained worldwide popularity. Starlink, a prominent LEO satellite Internet service provider, has attracted the most users due to its low latency, wide coverage, and strong usability. Current research on Starlink primarily focuses on the space segment and its impact on network performance. However, as an essential component, the architecture and unique features of the Starlink Terrestrial Network (SLTN) are not well-explored, which significantly affects the performance, security, and development of the whole network. In this paper, we fill this gap by conducting a thorough measurement study to profile the SLTN from various aspects and reveal its potential effects, with specific attention to the network assets, topology, and routing strategies. We developed a novel framework including active and passive measurement methods for collecting multiple network assets, tracing different route paths, and scanning active service of the SLTN. The open source intelligence was utilized for the first time to collect extensive real-user network status. Leveraging these techniques, we observed a rapid expansion of the Starlink service with the latest network assets, collected in January 2025, including over 23.5K/28 IP prefixes residing in 143 countries. We mapped the consistent topology of the global Starlink Internet access service and identified the specific IP addresses associated with the four types of network routing nodes. Multiple internal routing strategies were uncovered, which facilitate direct user-to-user interactions. Particularly, we revealed the switches of terrestrial infrastructure that users connected and the changes in routing strategies, which should be considered in future quality of service (QoS) evaluations. Our measurement also provides methods for improving users' perceptions and serves as a basis for studies like security risk evaluation and service discovery.

Tor is one of the most widely adopted anonymity networks, yet its anonymity can be undermined by adversaries through flow correlation attacks. Current mainstream technologies focus on exploiting the sequence characteristics of packet lengths and timestamps to execute attacks. However, the padding mechanism of the Tor network and time delays caused by multi-hop relays obscure these single-modal features. Additionally, the diversity of network services and the randomness of user behavior result in sparse packet distributions, which impact model training and inference. In this paper, we propose SSRCorr, a novel self-supervised learning framework for flow correlation attacks, incorporating the Flow Feature Aggregation (FFA) module and Global-Local Fusion (GLoF) Encoder to address these challenges. Firstly, we construct a Byte-based Traffic Aggregation Matrix (BTAM) by integrating time and length sequences and applying two data augmentation methods tailored for Tor flow correlation, thereby reducing the impact of Tor network noise on attack effectiveness. Secondly, we employ GLoF to extract features from the output by FFA and fuse the global context information of the traffic, thus mitigating the impact of low-information traffic on model performance. Experiments show that SSRCorr achieves a TPR of 96%, surpassing other methods, and maintains robust performance under temporal drift and obfuscation, supporting future research on countering anonymity system defenses.

Active pantograph control is the most promising technique for reducing contact force (CF) fluctuation and improving the train's current collection quality. Existing solutions, however, suffer from two significant limitations: 1) they are incapable of dealing with the various pantograph types, catenary line operating conditions, changing operating speeds, and contingencies well and 2) it is challenging to implement in practical systems due to the lack of rapid adaptability to a new pantograph-catenary system (PCS) operating conditions and environmental disturbances. In this work, we alleviate these problems by developing a revolutionary context-based deep meta-reinforcement learning (CB-DMRL) algorithm. The proposed CB-DMRL algorithm combines Bayesian optimization (BO) with deep reinforcement learning (DRL), allowing the general agent to adapt to new tasks quickly and efficiently. We evaluated the CB-DMRL algorithm's performance on a proven PCS model. The experimental results demonstrate that meta-training DRL policies with latent space swiftly adapt to new operating conditions and unknown perturbations. The meta-agent adapts quickly after two iterations with a high reward, which require only ten spans, approximately equal to 0.5 km of PCS interaction data. Compared with state-of-the-art DRL algorithms and traditional solutions, the proposed method can promptly traverse scenario changes and reduce CF fluctuations, resulting in an excellent performance.

In high-speed railways, the pantograph-catenary system (PCS) is a critical subsystem of the train power supply system. In particular, when the double-PCS (DPCS) is in operation, the passing of the leading pantograph (LP) causes the contact force of the trailing pantograph (TP) to fluctuate violently, affecting the power collection quality of the electric multiple units (EMUs). The actively controlled pantograph is the most promising technique for reducing the pantograph-catenary contact force (PCCF) fluctuation and improving the current collection quality. Based on the Nash equilibrium framework, this study proposes a multiagent reinforcement learning (MARL) algorithm for active pantograph control called cooperative proximity policy optimization (Coo-PPO). In the algorithm implementation, the heterogeneous agents play a unique role in a cooperative environment guided by the global value function. Then, a novel reward propagation channel is proposed to reveal implicit associations between agents. Furthermore, a curriculum learning approach is adopted to strike a balance between reward maximization and rational movement patterns. An existing MARL algorithm and a traditional control strategy are compared in the same scenario to validate the proposed control strategy's performance. The experimental results show that the Coo-PPO algorithm obtains more rewards, significantly suppresses the fluctuation in PCCF (up to 41.55%), and dramatically decreases the TP's offline rate (up to 10.77%). This study adopts MARL technology for the first time to address the coordinated control of double pantographs in DPCS.

With the emergence of Web3, decentralized network protocol technologies have been vigorously developed. As a pioneer for decentralized real-time communication systems, Matrix is an open standard based on a federation specification protocol. Anyone can set up a self-hosted homeserver to participate in the global Matrix network and communicate with others in chat rooms. In this paper, we conduct the first in-depth measurement and exploratory research on Matrix’s ecosystem and security. We designed and implemented several investigation techniques to empirically delve into Matrix federation from various aspects (homeservers, rooms, and users). In the end, we identified a number of interesting findings and potential vulnerabilities, including anti-decentralization phenomena, cybersecurity threats in homeservers, and the confidentiality of encrypted rooms being compromised.

In the emerging environment of the Internet of Things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in mining useful information from RFID trajectories. RFID data usually contain a considerable degree of uncertainty caused by various factors such as hardware flaws, transmission faults and environment instability. In this paper, we propose an efficient clustering algorithm that is much less sensitive to noise and outliers than the existing methods. To better facilitate the emerging cloud computing resources, our algorithm is designed cloud-friendly so that it can be easily adopted in a cloud environment. The scalability and efficiency of the proposed algorithm are demonstrated through an extensive set of experimental studies.

Recently, the development of cloud computing has received considerable attention. For cloud service providers, packing VMs onto a small number of servers is an effective way to reduce energy costs, so as to improve the efficiency of the data center. However allocating too many VMs on a physical machine may cause some hot spots which violate the SLA of applications. Load balancing of the entire system is hence needed to guarantee the SLA. In this paper, we present a simulated-annealing load balancing algorithm for solving the resource allocation and scheduling problem in a cloud computing environment. Experimental results show that this method is able to achieve load balancing, and performs better than the round robin and basic simulated-annealing algorithms.

In the emerging environment of the Internet of things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we propose a fully distributed model for federated RFID data streams. This model combines two techniques, namely, tilted time frame and histogram to represent the patterns of object flows. Our model is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose several algorithms that use a statistically minimum number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Unfortunately, enabling traceability across independent organizations still poses significant challenges in dealing with large volume of data and sovereignty of the participants. This paper describes PeerTrack, a scalable platform for efficiently and effectively tracking and tracing objects in large-scale traceability networks. With a novel data model, a DHT-based indexer, and a distributed query processor, PeerTrack provides an environment where traceability applications can share data across independent organizations in a peer-to-peer fashion. This paper presents the motivation, system design, implementation, and a proof-of-concept system of the PeerTrack platform.

The multicast problem in multi-channel multi-radio wireless mesh networks has received much attention recently. Most recent studies on this problem focus on improving the network throughput. However, many real-world applications require routing algorithms to achieve low-delay and low-loss. In this paper, we tackle the problem of constructing a robust minimum-cost multicast tree that tolerates link interference. To save bandwidth resource and alleviate the interference in the communication, we propose a robust multicast algorithm for multi-channel multi-radio wireless mesh networks. Our experimental results show that our algorithm is very efficient to achieve better performances in network throughput and end-to-end delay than previous studies.

Many real applications, such as network traffic monitoring, intrusion detection, satellite remote sensing, and electronic business, generate data in the form of a stream arriving continuously at high speed. Clustering is an important data analysis tool for knowledge discovery. Compared with traditional clustering algorithms, clustering stream data is an important and challenging problem which has attracted many researchers. Clustering stream data is facing two main challenges. First, as the data is continuously arriving with high rate and the computer storage capacity is limited, raw data can only be scaned in one pass. Second, stream data is always changing with time, so viewing a data stream as a set of static data can deteriorate the clustering quality. In fact, users are more concerned with the evolving behaviors of clusters which can help people making correct decisions. This paper proposes a density-grid based clustering algorithm, PKS-Stream-I, for stream data. It is an optimization of PKS-Stream in density detection period selection, sporadic grid detection and removal. Empirical results show the proposed method yields out better performance.

With recent advances in technologies such as radio-frequency identification (RFID) and new standards such as the electronic product code (EPC), large-scale traceability is emerging as a key differentiator in a wide range of enterprise applications (e.g., counterfeit prevention, product recalls, and pilferage reduction). Such traceability applications often need to access data collected by individual enterprises in a distributed environment. Traditional centralized approaches (e.g., data ware-housing) are not feasible for these applications due to their unique characteristics such as large volume of data and sovereignty of the participants. In this paper, we describe an approach that enables applications to share traceability data across independent enterprises in a pure Peer-to-Peer (P2P) fashion. Data are stored in local repositories of participants and indexed in the network based on structured P2P overlays. In particular, we present a generic approach for efficiently indexing and locating individual objects in large, distributed traceable networks, most notably, in the emerging environment of the Internet of Things. The results from extensive experiments show that our approach scales well in both data volume and network size.

With recent advances in technologies such as radio-frequency identification and new standards such as the electronic product code, large-scale traceability is emerging as a key differentiator in a wide range of enterprise applications (e.g. counterfeit prevention, product recalls and pilferage reduction). Such traceability applications often need to access data collected by individual enterprises in a distributed environment. Traditional centralized approaches (e.g. data warehousing) are not feasible for these applications due to their unique characteristics such as large volume of data and sovereignty of the participants. In this paper, we describe an approach that enables applications to share traceability data across independent enterprises in a pure peer-to-peer (P2P) fashion. Data are stored in local repositories of participants and indexed in the network based on structured P2P overlays. In particular, we present a generic approach for efficiently indexing and locating individual objects in large, distributed traceable networks, most notably, in the emerging environment of the internet of things. The results from extensive experiments show that our approach scales well in both data volume and network size. A real-world returnable assets management system is also developed using the proposed techniques to demonstrate its feasibility.

The ability to track individual objects is essential to many aspects of our modern life such as product recalls and anti-counterfeiting. As a non-line-of-sight technology, radio-frequency identification (RFID) provides an effective way to record movements of objects and has recently emerged as an enabling technology for traceability applications. Unfortunately, realizing RFID traceability networks in large-scale, distributed environments brings many fundamental research and development issues. In particular, applications will generate an unprecedented amount of transactions and data that requires novel approaches not only in RFID data processing and management, but infrastructure and architecture design. In this paper, we describe our approach for realizing a scalable RFID traceability network, which can efficiently and effectively support traceability applications. With a novel data model, traceability applications can share data across independent organizations in a peer-to-peer fashion.