Due to the explosion of mobile users and the ever-increasing heterogeneity and scale of wireless networks, traditional communication protocols and optimizing methods can not satisfy future wireless network (FWN) requirements. As promising technologies, artificial intelligence (AI) and blockchain are deemed as the solution for the FWN. AI, famous for its big data processing ability, will enable the FWN to self-update itself to better adapt to the dynamic network condition. Blockchain, as a distributed ledger, can guarantee data integrity, security, and privacy. In this survey, we overview the concept of AI and blockchain and present their state-of-the-art applications in wireless networks. The potential of AI and blockchain is still huge and waiting to be fully explored in wireless networks. Therefore, we introduce how AI and blockchain can assist each other in FWNs. Furthermore, we explore the current constraints of applying both technologies in the FWNs. In the final part, we discuss the future direction of the deployment of AI and blockchain in FWNs.

The state-space representations grant a convenient, compact, and elegant way to examine the induction and synchronous generator-based wind turbines, with facts readily available for stability, controllability, and observability analysis. The state-space models are used to look into the functionality of different wind turbine technologies to fulfill grid code requirements. This paper deals with the model order reduction of the Variable-Speed Wind Turbines model with the aid of improved stability preserving a balanced realization algorithm based on frequency weighting. The algorithm, which is in view of balanced realization based on frequency weighting, can be utilized for reducing the order of the system. Balanced realization based model design uses a full frequency spectrum to perform the model reduction. However, it is not possible practically to use the full frequency spectrum. The Variable-Speed Wind Turbines model utilized in this paper is stable and includes various input-output states. This brings a complicated state of affairs for analysis, control, and design of the full-scale system. The proposed work produces steady and precise outcomes such as in contrast to conventional reduction methods which shows the efficacy of the proposed algorithm.

The proliferation of electronic health records necessitates secure and privacy-preserving data sharing frameworks to combat escalating cybersecurity threats in healthcare. Current systems face critical limitations including centralized data repositories vulnerable to breaches, static consent mechanisms, and inadequate audit capabilities. This paper introduces an integrated blockchain and federated learning framework that enables privacy-preserving collaborative AI across healthcare institutions without centralized data pooling. The proposed approach combines federated distillation for heterogeneous model collaboration with dynamic differential privacy that adapts noise injection to data sensitivity levels. A novel threshold key-sharing protocol ensures decentralized access control, while a dual-layer Quorum blockchain establishes immutable audit trails for all data sharing transactions. Experimental evaluation on clinical datasets (Mortality Prediction and Clinical Deterioration from eICU-CRD) demonstrates that our framework maintains diagnostic accuracy within 3.6% of centralized approaches while reducing communication overhead by 71% and providing formal privacy guarantees. For Clinical Deterioration prediction, the framework achieves 96.9% absolute accuracy on the Clinical Deterioration task with FD-DP at $ϵ$ = 1.0, representing only 0.14% degradation from centralized performance. The solution supports HIPAA-aligned technical safeguards, mitigates inference and membership attacks, and enables secure cross-institutional data sharing with real-time auditability. This work establishes a new paradigm for privacy-preserving healthcare AI that balances data utility, regulatory requirements, and protection against emerging threats in distributed clinical environments.

The complexity of 2-D systems makes them challenging to analyze and manage. Basic algebraic operations are more intricate in 2-D frameworks than in their 1-D counterparts. Standard 2-D systems can typically only be decomposed into 1-D subsystems if they are represented in a separable denominator form. This research introduces a novel transformation that facilitates the decomposition of a 2-D standard system into a diagonalized 2-D representation; this further enables its partitioning into two 1-D subsystems, bypassing the constraints of minimal rank decomposition criteria. The proposed framework (PF) retains the geometrical symmetry of the resultant 1-D submodels while maintaining balance across the submodels, making them suitable for model reduction in large-scale systems. Furthermore, a novel framework for model reduction is developed to address limitations in existing limited-interval (LI) methods, such as instability within the reduced system. The numerical findings indicate that the proposed transformations adequately diagonalize the standard 2-D system and ensure a stable reduced 2-D model.

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts.

Healthcare analytics involving sensitive patient data demand robust statistical safeguards alongside verifiable compliance mechanisms that allow regulators to independently reconstruct query operations, applied policies, and reported outcomes without accessing raw medical information.We present a governance-first, upgradeable on-chain framework that anchors complete request lifecycles to public blockchain using keccak256 cryptographic commitments. This approach delineates a process wherein researchers submit structured queries, a controller captures active policy states, and hospitals provide result digests. A comprehensive and immutable event trail for this entire process is maintained on the Ethereum Sepolia blockchain. External auditors can then reconstruct complete timelines and verify consistent binding between requests, policies, and outcomes. The proposed modular verifier interface, currently implemented via configurable MockVerifier, maintains stable Application Binary Interface compatibility for future production verifier integration while validating end-to-end governance. We demonstrate the framework’s practicality through a detailed diabetes prevalence analytics case study. We provide detailed gas consumption profiles, request latency measurements, and observable failure modes that demonstrate how governance rules translate into enforceable reversions. The architecture maintains minimal on-chain state with Layer-2 readiness, offering immediate regulatory accountability while preserving straightforward upgrade paths to advanced cryptographic components like zk verifiers (Groth16), verifiable differential privacy through VRF-seeded randomness, content-addressed artifact storage, and versioned policy management. This approach effectively separates initial deployment feasibility from computationally intensive cryptography while delivering immediately actionable, externally verifiable evidence of policy compliance.