The emergence of sixth-generation (6G) mobile networks contributes to the widespread adoption of critical Internet-of-Things (IoT) applications, such as telesurgery and autonomous vehicles. However, such advancements expand attack surfaces and make traditional security mechanisms insufficient. While machine learning (ML)-based intrusion detection systems (IDSs) have been increasingly adopted to detect attacks in such heterogeneous and dynamic environments, their complex architectures often result in high computational costs and latency, making them unsuitable for the real-time demands of 6G-enabled IoT applications. To address these challenges, we propose a novel unsupervised quantum generative adversarial network (QGAN)-based IDS. Our solution enhances the GAN generator’s ability to model complex data distributions by combining a parametrized quantum circuit with a classical neural network (NN), while maintaining a lightweight classical discriminator (fewer than 2.000 parameters) at the edge. This design significantly reduces computational complexity and minimizes detection times, since only the classical discriminator is used during inference. Experimental results demonstrate that our solution achieves competitive detection rates with an ultra-low inference latency of 0,22 ms, outperforming state-of-the-art GAN-based and transformer-based IDSs by over 2,81 times. Therefore, our architecture provides a resource-efficient security framework that delivers the ultra-low latency and high detection rates essential for next-generation IoT applications.
Quantum networks enable secure communication and distributed quantum computation using entanglement, quantum teleportation, and the no-cloning theorem. Future quantum networks will support multiparty applications such as multiparty quantum key distribution and quantum secret sharing, which require simultaneous entanglement distribution among multiple users. Therefore, efficient routing that accounts for quantum network constraints is crucial for practical multiparty quantum communications. Although bipartite entanglement routing has been studied extensively, multipartite entanglement routing with probabilistic quantum link generation and fusion operations remains inadequately addressed. This paper proposes a routing model for multipartite entanglement distribution that explicitly incorporates the probabilistic nature of quantum operations and maximizes the probability of successfully distributing a Greenberger-Horne-Zeilinger (GHZ) state to all requesting users under practical quantum network constraints. We formulate this model as a mixed-integer linear programming (MILP) problem. We prove that finding the optimal Steiner entanglement tree under the proposed model is NP-hard. For large-scale scenarios where MILP becomes computationally intractable, we develop a heuristic approach. Numerical results demonstrate that the proposed model achieves a higher entanglement tree success probability and greater robustness than baseline methods across diverse network conditions, including scenarios with stringent quantum memory constraints and large terminal sets.
The rapid growth of intelligent IoT devices and mobile edge infrastructures has generated massive decentralized data, necessitating efficient and privacy-preserving learning. Federated Learning (FL) enables distributed model training without sharing raw data; however, its performance degrades in dynamic mobile environments due to unpredictable availability, stragglers, and frequent dropouts. In On-Demand FL, training services and models are deployed only when needed rather than relying on always-active infrastructure, requiring orchestrators to adapt to client arrivals, departures, and mobility while efficiently allocating limited resources and maintaining training continuity. Existing client-selection methods rely on static heuristics or centralized Reinforcement Learning models that struggle to scale and adapt to such dynamics. To address these challenges, we propose a cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for client selection in On-Demand FL. Multiple orchestrators act as agents under a Centralized Training and Decentralized Execution (CTDE) paradigm. The problem is formulated as a Partially Observable Markov Game (POMG), capturing mobility, partial observability, and distributed decision-making. A mobility-aware reward integrates accuracy, resource efficiency, and a Useful Handoff Bonus to preserve learning continuity. Containerization further enhances adaptability. Experiments demonstrate faster convergence, improved resource utilization, and enhanced robustness under real mobility traces.
Large-scale multi-domain optical networks are increasingly exposed to spatially correlated and concurrent failures, while monolithic pre-configured protection structures incur rapidly increasing global search complexity and protection-resource costs under heterogeneous physical constraints. To address this challenge, this paper proposes a resilient Hierarchical Polyhedron-based Configuration (HPC) architecture that decomposes a monolithic global survivability requirement into adaptive intra-domain connectivity constraints and a compact inter-domain connectivity constraint. To alleviate resource imbalance caused by topology-only network partitioning, a Topology-Resource Aware Domain Partitioning (TRADP) algorithm is developed to construct protection-supportable sub-domains by jointly considering topological structure and physical resource distribution. To avoid imposing a rigid protection level on heterogeneous domains, HPC assigns adaptive intra-domain connectivity targets according to local failure exposure and physical resource supportability. To reduce the complexity of cross-domain protection while preserving physical feasibility, a super-node abstraction and physical bridge-link mapping mechanism is employed to construct the inter-domain protection backbone under capacity, spectrum, and SRLG constraints. Extensive evaluations on networks of up to 1000 nodes and representative GEANT and NSFNET topologies demonstrate that HPC-TRADP maintains a recovery rate of 92.94% at 1000 nodes and 85.82% even when 30% of physical links fail concurrently. It further reduces protection overhead by up to 79.60% relative to p-KECS while maintaining efficient recovery paths, demonstrating a favorable balance between survivability, resource efficiency, and scalability.
Edge computing service deployment requires the Network Operator (NO) and Service Providers (SPs) to coordinate investment and resource allocation under uncertain demand. This paper develops an audit-based service management framework for daily coalition formation with fixed capacity and service-level agreement (SLA) reservations. A two-stage coalitional game separates infrastructure investment from operational participation. Demand follows a Geometric Random Walk (GRW), and SPs apply an ordered admission and suspension rule using reported marginal contributions. With the NO held fixed, the operational coalition value is submodular; mandatory reservations can produce negative Shapley payoffs. We derive a separable benchmark, compute operational Shapley allocations, and give a sufficient condition for convergence to an individual equilibrium. A probabilistic audit mechanism provides a conditional daily truthfulness guarantee under verified settlement and enforceable penalties. For five service classes over three years, the reference run gains 12.4% over static grand-coalition membership and recovers 94.6% of the full-information optimum. Across 30 runs, the median gain is 13.3%. Sensitivity tests examine policy parameters, capacity and alternative demand models. The gain depends on capacity and update order; abundant capacity can make the admission rule underperform static membership.
