Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes recursive machine learning models as the Learned Hash Function to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits scalability and robustness against system churn, providing a robust, scalable structure for efficient data retrieval in distributed key-value systems.

Blockchain interoperability and throughput scalability are two crucial problems that limit the wide adoption of blockchain applications. Payment channel networks (PCNs) provide a promising solution to the inherent scalability problem of blockchain technologies, allowing off-chain payments between senders and receivers via multi-hop payment paths. This paper presents a cross-chain PCN, called XHub, that extends PCNs to support multi-hop paths across multiple blockchains and resolves both interoperability and throughput scalability. XHub achieves service availability, transaction atomicity, and auditability. Users who correctly follow the protocols will succeed in making payments or get profits from doing the services. In addition, trustworthy information about hubs will be managed in a decentralized manner and available to all users. We conduct prototype implementation of machines that exchange Internet messages and run with two real blockchains as well as large-scale simulations based on real-world PCN topologies and transactions. The results show that XHub has small latency for cross-chain payments and can achieve a significantly higher success rate compared to the version without hub management protocols. This work is an important step towards the big picture of a decentralized transaction system that connects a wide scope of users in different blockchains.

Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits remarkable scalability and robustness against system churn, providing a robust, scalable solution for efficient data retrieval in distributed key-value systems.

Integrated Sensing and Communication (ISAC) is a key enabler for Sixth-Generation (6G) and future wireless networks, which seamlessly combines ambient sensing with data communication. In this paper, we propose a novel ISAC-enabled Non-Orthogonal Multiple Access (NOMA) scheme named ISAC-Space-Time Block Coding (STBC) NOMA. We present a thorough performance comparison of our proposed scheme against three previously studied ISAC-NOMA variants: Conventional ISAC-NOMA, ISAC-Unmanned Aerial Vehicle (UAV) NOMA, and ISAC-Generalized Space Shift Keying (GSSK) NOMA, in a multi-user scenario. The comparison specifically focuses on three critical performance metrics: spectral efficiency, Bit Error Rate (BER), and Successive Interference Cancellation (SIC) decoding complexity. The evaluation results show that ISAC-STBC NOMA consistently outperforms the other schemes across all metrics. Specifically, ISAC-STBC NOMA achieves approximately 24% higher spectral efficiency at 30 dB Signal-to-Noise Ratio (SNR), reduces the BER by approximately 30%, and lowers SIC decoding complexity by up to 25%. These findings position ISAC-STBC NOMA as a strong candidate for next-generation networks, offering a well-balanced solution that enhances communication robustness, spectrum utilization, and computational efficiency.

Multi-enterprise applications in fields like supply chain management, finance, and healthcare require complex collaboration and data exchange among organizations to ensure operational efficiency and build trust. Permissioned blockchains emerged as a promising solution, providing shared, immutable ledgers that enhance transparency, traceability, and trust among authorized parties. However, during asset trading between organizations, they must verify the legitimacy of asset transfers, including asset ownership and quantity, while protecting sensitive asset owner information. To achieve both verifiability and privacy, this paper introduces PAVE, Privacy-preserving Aggregated Verification system for Multi-Enterprises Blockchain, a framework that integrates zero-knowledge proofs to enable secure asset verification without breaking user privacy. To achieve proof efficiency, PAVE introduces a proof aggregation mechanism that consolidates multiple transaction verifications into a single proof, significantly reducing computational overhead for large-scale scenarios. Evaluation results show that, with the proof aggregation mechanism, PAVE achieves low verification latency and resource utilization, making it a scalable solution for privacy-preserving asset verification across multiple enterprises.

Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as storage systems, decentralized databases, content distribution networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. Preliminary results indicate LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems while maintaining high scalability and resilience to network churn.

Decentralized federated learning (DFL) uses peer-topeer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solution for machine learning tasks on distributed devices. We provide the first solution to a fundamental network problem of DFL: what overlay network should DFL use to achieve fast training of highly accurate models, low communication, and decentralized construction and maintenance? Overlay topologies of DFL have been investigated, but no existing DFL topology includes decentralized protocols for network construction and topology maintenance. Without these protocols, DFL cannot run in practice. This work presents an overlay network, called FedLay, which provides fast training and low communication cost for practical DFL. FedLay is the first solution for constructing near-random regular topologies in a decentralized manner and maintaining the topologies under node joins and failures. Experiments based on prototype implementation and simulations show that FedLay achieves the fastest model convergence and highest accuracy on real datasets compared to existing DFL solutions while incurring small communication costs and being resilient to node joins and failures.

Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.

Payment channel networks (PCNs) have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. It provides a high-throughput solution for blockchain-based payment systems. However, such “layer-2” blockchain solutions have their own problems: payment channels require a separate deposit for each channel of two users. Thus it significantly locks funds from users into particular channels without the flexibility of moving these funds across channels. In this paper, we proposed Aggregated Payment Channel Network (APCN), in which flexible funds are used as a per-user basis instead of a per-channel basis. To prevent users from misbehaving such as double-spending, APCN includes mechanisms that make use of hardware trusted execution environments (TEEs) to control funds, balances, and payments. The distributed routing protocol in APCN also addresses the congestion problem to further improve resource utilization. Our prototype implementation and simulation results show that APCN achieves significant improvements on transaction success ratio with low routing latency, compared to even the most advanced PCN routing.

Blockchain interoperability and throughput scalability are two crucial problems that limit the wide adoption of blockchain applications. Payment channel networks (PCNs) provide a promising solution to the inherent scalability problem of blockchain technologies, allowing off-chain payments between senders and receivers via multi-hop payment paths. This paper presents a cross-chain PCN, called XHub, that extends PfCNs to support multi-hop paths across multiple blockchains and resolves both interoperability and throughput scalability. XHub achieves service availability, transaction atomicity, and auditability. Users who correctly follow the protocols will succeed in making payments or get profits from doing the services. In addition, trustworthy information about hubs will be managed in a decentralized manner and available to all users. We conduct prototype implementation of machines that exchange Internet messages and run with two real blockchains as well as large-scale simulations based on real-world PCN topologies and transactions. The results show that XHubs has small latency for cross-chain payments and can achieve a significantly higher success rate compared to the version without hub management protocols. This work is an important step towards the big picture of a decentralized transaction system that connects a wide scope of users in different blockchains.

Decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation to avoid the single point of failure problem in federated learning (FL). However, this process is vulnerable to attackers who share false models and data. In this work, we propose Blockchain-based Verifiable Decentralized Federated Learning (BVDFL), which leverages a blockchain for decentralized model verification and auditing. BVDFL includes an auditor committee for model verification, a reputation model to evaluate the trustworthiness of clients, and a protocol suite for dynamic network updates. Simulation results show that, with the reputation mechanism, BVDFL achieves fast model convergence and high accuracy on real datasets with malicious clients in the system.

Off-chain networks have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. The most widely used one, the Lightning Network (LN), has developed rapidly since its introduction in 2015. While much research has been dedicated to improving LN's routing efficiency, security protocols, and network analysis, there remains a gap in understanding how end-users should establish channels to optimize transaction efficiency. In this work, we conduct a holistic measurement study that focuses on the practical aspects of Lightning Network transaction performance. By evaluating transaction latency, fees, and success rates under diverse sce-narios, we provide users with actionable insights to enhance their network efficiency. Furthermore, our research aids those interested in leveraging the LN for transaction relaying, fostering a more efficient and dynamic network ecosystem. Through empirical findings and analytical deductions, we pave the way for users to harness the full potential of the Lightning Network and contribute to its ongoing growth and development.

Off-chain networks have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. Routing is a core problem. An ideal off-chain networks routing method needs to achieve 1) high scalability that can maintain low per-node memory and communication cost for large networks and 2) high resource utilization of channels. However, none of the existing off-chain routing methods achieve both requirements. In this work, we propose WebFlow, a distributed routing solution for off-chain networks, which only requires each user to maintain localized information and can be used for massive-scale networks with high resource utilization. We make use of two distributed data structures: multi-hop Delaunay triangulation (MDT) originally proposed for wireless networks and our innovation called distributed Voronoi diagram. We propose new protocols to generate a virtual Euclidean space in order to apply MDT to off-chain networks and use the distributed Voronoi diagram to enhance routing privacy. We conduct extensive simulations and prototype implementation to further evaluate WebFlow. The results using real and synthetic off-chain network topologies and transaction traces show that WebFlow can achieve extremely low per-node overhead and a high success rate compared to existing methods.

Payment channel networks (PCNs) have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. It provides a high-throughput solution for blockchain-based payment systems. However, such “layer-2” blockchain solutions have their own problems: payment channels require a separate deposit for each channel of two users. Thus it significantly locks funds from users into particular channels without the flexibility of moving these funds across channels. In this paper, we proposed Aggregated Payment Channel Network (APCN), in which flexible funds are used as a per-user basis instead of a per-channel basis. To prevent users from misbehaving such as double-spending, APCN includes mechanisms that make use of hardware trusted execution environments (TEEs) to control funds, balances, and payments. The distributed routing protocol in APCN also addresses the congestion problem to further improve resource utilization. Our prototype implementation and simulation results show that APCN achieves significant improvements on transaction success ratio with low routing latency, compared to even the most advanced PCN routing.

The ever-increasing number of IoT devices in our surrounding environment bring us tremendous amount of opportunities but also challenges including limited battery life, low computational capability and scalability of multiple access. Recent advances in backscatter communication have enabled ubiquitous IoT devices to communicate in a cost-and power-efficient way. However, most of the proposed backscatter solutions nowadays focus on the single tag paradigm, i.e., multiple tags do not transmit simultaneously and thus the solutions have difficulties to scale with a large number of tags. This work presents CBMA, a backscatter system that enables multiple concurrent backscatter tags to communicate reliably and efficiently. For the first time, we demonstrate that multiple tags can backscatter concurrently and efficiently with novel impedance-based power control at the tag, and can be successfully decoded with commodity WiFi devices without affecting the existing WiFi communication. We present the design details of CBMA and build a prototype with off-the-shelf WiFi devices and FPGA. The CBMA system achieves a 10-tag bit rate of 8Mbps while supporting a communication distance up to 10m. Compared to single-tag solutions, CBMA improves the backscatter throughput by more than 10× even in challenging indoor scenarios with rich multipath and interference.