Due to the emergence of heterogeneous Internet of Medical Things (IoMT) (e.g., wearable health devices, smartwatch monitoring, and automated insulin delivery systems), large volumes of patient data are dispatched to central cloud servers for disease analysis and diagnosis. Although this direct mode brings a lot of convenience for both patients and medical professionals (MPs), the open communication channel between them also incurs several security and privacy issues, such as man-in-the-middle attacks, eavesdropping attacks, and tracking attacks. Based on the unsolved challenges in wireless medical sensor networks (WMSNs), several researchers have proposed various authentication and key agreement (AKA) protocols for this type of healthcare system recently. However, most of these protocols do not perceive physical-layer security and over-centralized server problem in WMSN. In this article, to address these two open problems, we propose a lightweight and reliable authentication protocol for WMSN, which is composed of cutting-edge blockchain technology and physically unclonable functions (PUFs). In addition, a fuzzy extractor scheme is introduced to deal with biometric information. Subsequently, two security evaluation methods are used to prove the high reliability of our proposed scheme. Finally, performance evaluation experiments illustrate that the proposed mutual authentication protocol requires the least computation and communication cost among the compared schemes.

A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS This system is part of a project being developed at the Adaptive Systems Laboratory of the University of Aizu and Aizu Computer Science Laboratories, Inc., Aizu-Wakamatsu. for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with R2 score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.

Metaverse and blockchain, as the latest buzzwords, have attracted great attention from industry and academia. They will inevitably promote technological innovation in the field of building information modeling (BIM) in the future. BIM organizes various building information into a whole by establishing a virtual three-dimensional model of architectural engineering using digital technology. The metaverse seamlessly integrates the real world and the virtual world, and conducts rich activities such as creation, display, and trading. Therefore, through the exploration of the metaverse, it will be possible to build an exciting digital world and transform the physical world better. Meanwhile, introducing the blockchain technology could ensure the fairness and security of resource transactions, data storage, and other activities. In this survey, we delve into the metaverse and blockchain empowerment by studying BIM components, metaverse applications in virtual world construction, and the latest research on blockchain. We also discuss how BIM technology and blockchain can be integrated with metaverse. The collaborations between academia and industry would be certainly required for further development and interdisciplinary research on the metaverse and the integration of blockchain into BIM. We hope to see our survey help researchers, engineers and educators build an open, fair and rational future BIM ecosystem.

Federated Learning (FL) is a distributed machine-learning paradigm that enables models to be trained across multiple decentralized devices or servers holding local data without transferring the raw data to a central location. However, applying FL to heterogeneous IoT scenarios comes with several challenges due to the diverse nature of these devices in terms of hardware capabilities, communications, and data heterogeneity. Furthermore, the conventional parameter server-based FL paradigm aggregates the trained parameters of devices directly, which incurs high communication overhead. To this end, this paper designs a hierarchical federated-learning framework for heterogeneous IoT systems, focusing on enhancing communication efficiency and ensuring data security through lightweight encryption. By leveraging hierarchical aggregation, lightweight stream encryption, and adaptive device participation, the proposed framework provides an efficient and robust solution for federated learning in dynamic and resource-constrained IoT environments. The extensive experimental results show that the proposed FL paradigm significantly reduces round time by 20%.

In Internet of Things (IoT) environments, anti-honeypot attacks exploit device fingerprinting and protocol analysis to undermine traditional honeypot defenses. To this end, we introduce an innovative camouflage defense strategy that dynamically disguises real IoT devices as honeypots to mislead attackers and protect critical resources. We formalize this adversarial interaction using a multi-stage signaling game, thereby reversing the information asymmetry that typically favors attackers. Subsequently, we theoretically derive the Perfect Bayesian Nash Equilibrium (PBNE) under conditions of incomplete information, thereby confirming the existence of mixed-strategy equilibria. Practically, we develop a signaling game-based multi-agent proximal policy optimization (SG-MAPPO) algorithm by integrating LSTM networks and Bayesian updates, overcoming challenges in long-term strategy optimization and parameter convergence in dynamic IoT contexts. Experiments demonstrate that SG-MAPPO outperforms existing methods in terms of training efficiency and stability, effectively reducing attacker success rates and providing a proactive defense solution for IoT security.

With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. In addition, there is a risk of privacy leakage during interactions between the edge and the server. To tackle these problems, we propose an effective, privacy-preserving edge–cloud collaborative interaction scheme based on WiFi, named P-CA, for human behavior sensing. In our scheme, a convolutional autoencoder neural network is split into two parts. The shallow layers are deployed on the edge side for inference and privacy-preserving processing, while the deep layers are deployed on the server side to leverage its computing resources. Experimental results based on datasets collected from real testbeds demonstrate the effectiveness and considerable performance of the P-CA. The recognition accuracy can maintain 88%, although it could achieve about 94.8% without the mixing operation. In addition, the proposed P-CA achieves better recognition accuracy than two state-of-the-art methods, i.e., FedLoc and PPDFL, by 2.7% and 2.1%, respectively, while maintaining privacy.