Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework’s effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations.

Unmanned Aerial Vehicles (UAVs) were popularly used by hobbyists in the past, but they have now become critical enablers for managing disasters, handling emergencies, and so on. For example, one of their most critical applications is to provide seamless wireless communication services in remote rural areas. Thus, it is substantial to identify and consider the different security challenges in the research and development associated with advanced UAV-based B5G/6G architectures. Catering to this requirement, this article conducts a comprehensive review of the security aspects of UAVs with respect to the 5G/6G system architecture, its enabling technologies, and privacy issues. It exhibits security integration at all the protocol stack layers and analyzes the existing mechanisms to secure UAV-based B5G/6G communications and its energy and power optimization factors. Last, this article also summarizes modern technological trends for establishing security and protecting UAV-based systems, along with the open challenges and strategies for future research work.

As the boundaries of human computer interaction expand, Generative AI emerges as a key driver in reshaping user interfaces, introducing new possibilities for personalized, multimodal and cross-platform interactions. This integration reflects a growing demand for more adaptive and intuitive user interfaces that can accommodate diverse input types such as text, voice and video, and deliver seamless experiences across devices. This paper explores the integration of generative AI in modern user interfaces, examining historical developments and focusing on multimodal interaction, cross-platform adaptability and dynamic personalization. A central theme is the interface dilemma, which addresses the challenge of designing effective interactions for multimodal large language models, assessing the trade-offs between graphical, voice-based and immersive interfaces. The paper further evaluates lightweight frameworks tailored for mobile platforms, spotlighting the role of mobile hardware in enabling scalable multimodal AI. Technical and ethical challenges, including context retention, privacy concerns and balancing cloud and on-device processing are thoroughly examined. Finally, the paper outlines future directions such as emotionally adaptive interfaces, predictive AI driven user interfaces and real-time collaborative systems, underscoring generative AI's potential to redefine adaptive user-centric interfaces across platforms.

Consumer Electronics (CE) devices (e.g., wearables and in-home monitors) generate sensitive health/activity data, but centralization in consumer-centric Internet of Medical Things (IoMT) is impractical due to privacy, communication, energy, and regulatory constraints. Federated learning (FL) enables cross-device training on CE/gateways yet remains vulnerable to poisoning, Sybil attacks, inference risks, and unreliable clients. We propose a blockchain-enabled FL architecture that uses a permissioned blockchain for identity, access control, auditability, and incentives, while keeping compute-intensive model updates off-chain for scalability. A novel reputation-based mechanism scores client updates via validation improvement, cross-round consistency, and plausibility checks, updates reputations, and enforces access, incentives, and slashing; robust aggregation, differential privacy, secure aggregation, and communication compression further mitigate adversaries and bandwidth limits. We compare seven reputation scoring methods, Weighted Average, Beta Reputation, Fuzzy Trust, Tanh Utility, Exponential Decay, Entropy-based, and Cosine Similarity, and evaluate on representative IoMT tasks/datasets, demonstrating feasible, secure, privacy-preserving, and auditable learning for next-generation consumer healthcare CE ecosystems.