Contemporary Web3 credential systems give holders strong control over what they disclose. While this autonomy protects privacy, it also creates a concrete trust problem in credit and reputation applications: a holder can present favorable records while hiding adverse ones (e.g., defaults), leaving verifiers unable to tell whether the disclosed profile is complete. A practical Web3 solution should support comprehensive assessment over relevant issuers without exposing a user's full identity or credential history. Existing revocation, scoring, and public reputation mechanisms cannot provide both guarantees without sacrificing privacy or reintroducing centralized authorities. We introduce ChainCred, a privacy-preserving Web3 credential system that enables mandatory credential disclosure for complete credit assessment within the domain specified by a verifier. For each issuer designated by the verifier, a holder must present the latest interaction credential or prove that no interaction exists. This prevents holders from presenting only favorable records while hiding adverse ones. At the same time, zero-knowledge proofs allow the holder to reveal only the properties required by the verifier, without exposing full credential contents or identity information. Security analysis shows resistance to holder-side repudiation under finalized ledger updates, and our implementation shows practical overheads: credential derivation and presentation proofs take 510ms and 337ms, respectively, with 30 chained credentials.

Distributed Quantum Computing (DQC) expands qubit capacity by interconnecting multiple Quantum Processing Units (QPUs), but remote gate execution introduces significant entanglement overhead. In this paper, we investigate the Remote Gate Scheduling problem in DQC (RGS-DQC) under a hybrid Telegate and Teledata model, provide a formal formulation, and establish its NP-hardness. To address this challenge, we propose LABUBU, a layer-buffered bundled optimization framework that integrates coordinate-wise pruned greedy refinement with bounded perturbation under QPU capacity constraints while maintaining linear complexity per iteration. Extensive simulations on both structured Quantum Fourier Transform circuits and unstructured random circuits show that Labubu consistently reduces entanglement cost compared with Telegate-SA, Telegate-RD, Teledata-ZS, and the competitive GateCover baseline. Experiments on QEC encoded circuits further confirm its potential for large scale fault tolerant distributed quantum computing.

Quantum Computing (QC) is promising for solving complex problems that are intractable for classical computers. However, QC on current quantum processors faces a critical challenge in achieving high execution fidelity due to decoherence and accumulated gate errors, limiting its practical applicability. Quantum circuit compilation (QCC), which transforms highlevel quantum circuits into hardware-compliant implementations, directly affects execution fidelity. Existing work either neglects hardware constraints such as qubit connectivity or does not explicitly optimize for execution fidelity, which can significantly degrade circuit fidelity. This paper investigates the problem of circuit compilation, which jointly considers qubit connectivity constraints and optimizes execution fidelity explicitly. To address the problem, this paper presents a reinforcement learning–based QCC approach named CARL. In particular, CARL unifies mapping and routing into a single crosstalk-aware optimization pipeline to mitigate hardware noise. Extensive evaluations on real-world benchmark quantum circuits demonstrate that the proposed approach achieves significantly higher circuit fidelity than state-of-the-art baselines.

Quantum networking is poised to enable a distributed quantum Internet, connecting many quantum processors to exchange information securely and efficiently. Quantum networks use quantum repeaters for long-distance communication to mitigate the noise that accumulates over distance. However, today's quantum network protocols still face fundamental limitations: most rely on establishing end-to-end entanglement before teleportation, which is costly to establish and is extremely shortlived. While hop-by-hop teleportation provides more flexibility, existing designs assume ideal, noise-free conditions. In effect, noise emerges as the fundamental bottleneck that affects the scalability and reliability of multi-hop quantum transmission. We present a verification-based quantum transmission (VBQT) protocol that addresses noise as a first-class design goal. This new protocol couples entanglement verification with a new hopby-hop teleportation protocol to deliver robust, long-distance quantum communication. Our verification algorithm provably detects and filters low-quality pair-wise entanglements, ensuring that only high-fidelity pairs are used for communication. Once multiple verified pairs are available, the transmission protocol at each repeater decides which verified pair to use for pairwise teleportation that is consistent across hops so that the destination can recover the qubit successfully. Using the NetSquid simulator under real experiment parameters, we show that VBQT improves average qubit fidelity by up to 42.8% over end-to-end and 34.7% over hop-by-hop teleportation, and increases goodput by up to 870% and 167%, respectively. These results establish a practical foundation for reliable, scalable quantum communication within near-term quantum networks.

Fully decentralized model training for on-road vehicles enables leveraging crowdsourced data without relying on central servers, infrastructure, or persistent Internet connectivity. However, real-world vehicular scenarios pose fundamental challenges to decentralized learning, including highly dynamic network topology, unreliable wireless links, and heterogeneous radio capability constraints. To address these challenges, we propose RoADTrain , a route-assisted decentralized peer model training framework with formal convergence guarantees. In RoADTrain, vehicles share short-horizon route information to estimate inter-vehicle contact duration and link reliability, which are embedded into a base graph. Vehicles then select communication partners by maximizing the algebraic connectivity of the resulting communication graph, thereby accelerating information mixing and model convergence. We further investigate subcarrier reuse and extend the algorithm to three representative radio capability cases. Extensive evaluations show that RoADTrain achieves convergence comparable to communication-unconstrained state-of-the-art approaches while reducing communication overhead by up to 4.5×, and significantly outperforms communication-constrained decentralized baselines by up to 29% higher driving success rates in online evaluations. We further empirically demonstrate the robustness of RoADTrain under a range of unexpected effects encountered in complex real-world applications, as well as in a large-scale traffic scenario.

Distributed Quantum Computing (DQC) scales quantum computing capabilities by interconnecting multiple Quantum Processing Units (QPUs) for collaborative computation, but faces the critical challenge of high entanglement costs associated with remote gate operations. Circuit transformation and qubit mapping are critical components of quantum circuit compilation for minimizing entanglement cost, which are interdependent in DQC. Existing approaches that treat circuit transformation and qubit mapping as separate optimization problems fail to account for their fundamental interdependence, resulting in suboptimal entanglement utilization and degraded performance. This paper presents the first optimization framework for circuit transformation and qubit mapping tailored for DQC. To address this joint optimization problem, we propose Qmactr, a two-phase reinforcement learning approach. The first phase trains a qubit mapping agent, and the second phase trains a circuit transformation agent that utilizes the mapping agent as a reward oracle. Extensive evaluation results demonstrate that the proposed approach reduces entanglement consumption by up to 35% compared to state-of-the-art sequential methods while achieving superior circuit fidelity.

Distributed fault-tolerant quantum computing (DFTQC) enables scalable quantum computation by interconnecting multiple quantum processing units (QPUs), but surfacecode-based DFTQC introduces substantial resource-management challenges due to unreliable remote logical operations, magicstate distillation overhead, and runtime stalls caused by insufficient magic-state availability. In this paper, we study reliabilityaware resource allocation for DFTQC systems. We propose a partitioned Decoupled Compute-Factory Architecture with Buffers that separates logical computation from magic-state distillation and supports magic-state injection through dedicated buffer resources. Based on this architecture, we formulate the joint optimization of computation placement, magic-state factory placement, buffer allocation, and layer-level execution scheduling with the objective of minimizing logical circuit failure probability. To address the resulting NP-hard problem, we develop a hierarchical reliability-aware heuristic that integrates resource-disjoint region selection, probability-aware logical-qubit mapping, and magic-state-buffered circuit evaluation. Simulation results across representative quantum circuits show that DFTQC can reduce circuit failure probability compared with non-fault-tolerant NISQ execution when system parameters are properly configured. We further show that code distance, remote Bell-pair fidelity, and the number of magic-state factories jointly affect DFTQC reliability, and that increasing the number of factories does not always improve reliability. These results provide practical guidance for designing scalable distributed fault-tolerant quantum computing systems.

The security of today's blockchains relies almost entirely on classical cryptography and Proof-of-Work (PoW), both of which are increasingly challenged by advances in quantum computing. Fully replacing PoW with quantum-based consensus, however, remains impractical due to hardware scarcity, high verification cost, and instability under probabilistic validation. This paper presents a hybrid blockchain design that integrates Proof-of-Work and quantum-based consensus into a unified architecture. Our system embeds quantum-validated blocks as periodic checkpoints within a predominantly classical chain, providing quantum-origin security anchors while preserving decentralization, throughput, and miner accessibility. We introduce an epoch-based consensus protocol, a hybrid chainwork formulation that balances classical and quantum contributions, and dynamic adjustment mechanisms that adapt block weights and mining difficulty as quantum hardware evolves. We further evaluate the protocol using discrete-event simulation. The results demonstrate stable protocol operation under probabilistic quantum validation, effective control of the relative contributions of classical and quantum blocks, and predictable incentive behavior across a range of operating conditions. Together, these results demonstrate that hybrid consensus offers a practical and forward-compatible path for evolving classical blockchains toward quantum-resilient operation without disruptive protocol replacement.

Blockchain, originally introduced for cryptocurrencies, provides a secure and decentralized platform for processing and storing transactions among distributed cloud nodes. However, traditional blockchain systems require broadcasting each transaction to all nodes, incurring high computational and communication overhead. In cloud networks, this problem is exacerbated because forwarding transactions consumes limited bandwidth and computing resources, which can discourage nodes from participating honestly. In addition, some nodes may refuse to forward transactions, undermining network consensus. In this paper, we propose Incentive Transaction Forwarding in Cloud networks (ITFC), which rewards cloud nodes with a share of transaction fees for forwarding transactions. We design a dynamic topology update mechanism to enable accurate incentive computation and develop a specified blockchain structure to support fair revenue distribution. We analyze ITFC under a stated rational-adversary model, including Sybil and active-user-node strategy, and establish a local monotonicity property for link removal that preserves the shortest-path level assignment. Extensive simulations evaluate topology-based incentive allocation, the two modeled attacks, heterogeneous cloud-node capacity, and algorithmic computation and storage overhead.

Mobile Edge Computing (MEC) shifts the computing power to the edge of core networks and provides important impetus in the flourishment of delay sensitive services at the network edge. Digital Twin (DT) technique enables object behavior monitoring, analysis, and prediction through data analytics and artificial intelligence, which facilitates inference service provisioning based on machine learning models. In this paper, we deal with the Quality-of-Experience (QoE) issue of user satisfaction on inference services in DT-assisted MEC networks, through executing user tasks locally or offloaded to the MEC network. We formulate two novel optimization problems: the utility maximization problem, and the dynamic utility maximization problem, with the aim to maximize the total utility of user task executions in terms of QoEs and service delays of users with the services. We first provide an Integer Linear Programming solution for the utility maximization problem when the problem size is small or medium; otherwise we devise a randomized algorithm with high probability, at the expense of bounded resource violations. We then develop an efficient online heuristic for the dynamic utility maximization problem. We also devise an online algorithm with a provable competitive ratio for a special case of the dynamic utility maximization problem without the bandwidth constraint. We finally evaluate the performance of proposed algorithms through simulations. The simulation results show that the proposed algorithms are promising.

With the commercialization of private data, location privacy trading in Mobile Crowd Sensing (MCS) has become a fascinating research topic. In consideration of location-dependent sensing tasks, mobile workers take risks at location privacy disclosure when reporting their actual locations. Existing work fail to take workers' diverse privacy protection and trading into account. This paper proposes a novel trading framework with personalized differential privacy guarantee, referred to as Leaper, to bridge the gap between location privacy protection and task allocation efficiency. In particular, Leaper outputs a personalized obfuscated range for each worker and further obfuscates his location based on a perturbation set within this range by incorporating differential privacy and $k$k-anonymity techniques, and thus improves the efficiency of task allocation. Moreover, Leaper quantifies each worker's location privacy loss and compensates him with reasonable payment by running auction in a cost-effective way. Through real-world datasets, our evaluations and analysis demonstrate that Leaper indeed guarantees all desired properties of personalized differential privacy, truthfulness, individual rationality and budget feasibility.

Edge computing enables widely distributed nodes with varying sensing, storage, and computing resources to collaborate effectively. For instance, sensor nodes gather data, store it in storage nodes, and allow computing nodes to access it as needed. However, the heterogeneity of edge computing poses a significant challenge in designing a mechanism to match the diverse and widely deployed devices. Additionally, the vast number of edge devices and their limited computing power make it impractical for a single device to maintain information for the entire network. This paper introduces a blockchain mechanism for large-scale data collection and management in edge networks. We propose a consensus mechanism for nodes in edge networks, enabling interactive clients to evaluate the quality of services provided by sensors. To alleviate the burden on edge devices in maintaining the blockchain, we employ sharding to divide the blockchain into multiple committees, thereby reducing the maintenance load. We also outline the design of the corresponding blockchain structure to adapt our proposed mechanism. Extensive simulation results demonstrate that our mechanism can save on-chain storage, improve data quality, and verify reputation.

Generating a long-distance quantum entanglement is one of the most essential functions of a quantum network to support quantum communication and computing applications. The successful entanglement rate during a probabilistic entanglement process decreases dramatically with distance, and swapping is a widely applied quantum technique to address this issue. Most existing entanglement routing protocols use a classic entanglement-swapping method based on Bell State measurements that can only fuse two successful entanglement links. This paper appeals to a more general and efficient swapping method, namely n-fusion based on Greenberger-Horne-Zeilinger measurements that can fuse n successful entanglement links, to maximize the entanglement rate for multiple quantum-user pairs over a quantum network. We propose efficient entanglement routing algorithms that utilize the properties of n-fusion for quantum networks with general topologies. Evaluation results highlight that our proposed algorithm under n-fusion can greatly improve the network performance compared with existing ones.

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs.

Network Function Virtualization (NFV) has the potential for cost-efficiency, manage-convenience, and flexibility services but meanwhile poses challenges for the service function chain (SFC) deployment problem, which is NP-hard. It is so complicated that existing work conspicuously neglects the flow changes along the chains and only gives heuristic algorithms without a performance guarantee. In this paper, we fill this gap by formulating a traffic-sensitive online joint SFC placement and flow routing (TO-JPR) model, with the objective of jointly optimize the resource cost and network latency, and proposing a novel two-stage scheme to solve it. We design a dynamic segmental packing (DSP) algorithm for the first stage, which not only maintains the minimal traffic burden for the network but also achieves an approximation ratio of a small constant on the resource cost. Besides, we propose the greedy mapping (GM) algorithm for the second stage, which can guarantee a global approximation ratio of O(d) on the network latency. Here d is the diameter of the network graph and is typically smaller than O(log(M)), where M is the number of servers in the network. Finally, we perform extensive simulations to demonstrate the outstanding performance of our algorithms compared with the optimal solutions and benchmarks.

Heterogeneous devices in edge computing bring challenges as well as opportunities for edge computing to utilize powerful and heterogeneous hardware for a variety of complex tasks. In this paper, we propose a service function chain placement strategy considering the heterogeneity of devices in deviceless edge computing environments. The service function chain system utilizes lightweight virtualization technologies to manage resources, considering the heterogeneity of devices to support various complex tasks, and offer low latency services to user requests. We propose an optimal service function chain placement problem minimizing the service delay and formulate it into a quasi-convex problem. We implement different edge applications that can be served by function chains and conduct extensive experiments over real heterogeneous edge devices. Results from the experiments and simulations show that our proposed service function chain scheme is applicable in edge environments, and perform well over services latency, resource utilization as well as the power consumption of edge devices.

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L >= 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear Program (ILP) solution if the problem size is small or medium; otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising.

Secure data sharing and storage are critical to realizing the ultra-high safety and efficiency promised by intelligent transportation systems (ITS). However, ensuring data integrity under stringent resource constraints of ITS remains an open challenge. Existing approaches either incur substantial computational and transmission overhead or demand resources beyond what ITS environments can provide. To address this dilemma, we propose a lightweight blockchain-based data sharing and storage framework tailored for ITS. The system features a distributed, asynchronous reputation mechanism that enables trustworthy data evaluation and dissemination without burdening critical computing and communication paths. Additionally, a resource-efficient blockchain storage layer ensures low-latency, tamper-resistant access to locally shared data. Extensive simulations demonstrate that our approach outperforms existing solutions in both integrity assurance and resource efficiency.

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs.

Quantum computing has the potential to outperform classical computing in solving specific problems. However, the limited qubit capacity of existing Quantum Processing Units (QPUs) poses significant barriers to the practical implementation of quantum computing. Distributed quantum computing (DQC) offers a promising approach to scaling the qubit capacity of quantum systems by interconnecting multiple QPUs and enabling collaborative computation. Nevertheless, DQC necessitates implementing remote quantum gate operations that consume entangled qubit pairs, which poses a significant challenge for DQC. In this work, we formulate and investigate the remote gate scheduling (RGS) problem, considering two approaches for remote gate operations: Telegate and Teledata. We propose a hybrid heuristic algorithm that dynamically schedules quantum gate operations within a circuit, executed on distributed QPUs, while minimizing entanglement consumption. We conducted extensive simulations using real-world quantum circuits and processors to evaluate the proposed approach. The results show that our approach reduces entanglement consumption by up to 90% and 25% compared to the two baselines, Telegate-SA and Teledata-ZS, respectively. Furthermore, the execution time of our approach is significantly shorter than that of the baselines.