With the widespread adoption of multi-view data in numerous fields, multi-view unsupervised feature selection (MUFS) has made notable strides in both feature pruning and missing-view completion. Nonetheless, existing MUFS methods typically rely on centralized servers, which cannot meet real-world demands for privacy preservation and distributed learning, and they often suffer from suboptimal solution and weak convergence guarantees. To address these challenges, IMUFFS, an incomplete multi-view unsupervised federated feature selection via cooperative particle swarm optimization (CPSO) and tensor-aligned learning (TAL) is proposed. Specifically, each client executes CPSO-TAL at two stages: (i) an external optimization phase that involves a CPSO, inspired by the co-evolutionary mechanism of hybrid breeding optimization algorithm, performing a global search in the feature space, and (ii) an internal optimization phase that leverages TAL with imputation and CP decomposition, where CP decomposition reduces dimensionality by decomposing the original tensor into a sum of core components, to learn low-dimensional embeddings, while simultaneously updating anchor graphs and view preference weights, thereby harmonizing imputation and representation learning. On the server side, a federated aggregation strategy using adaptive normalized mutual information (NMI) weighting combines the locally optimized feature selection (FS) weights and NMI scores from clients, ensuring privacy while improving the quality of FS and convergence. Extensive experiments on multiple datasets demonstrate that IMUFFS consistently outperforms state-of-the-art methods, yielding more effective and robust FS and enhancing better missing-view completion.

Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems, where accurate State of Health (SoH) prediction is essential for safety and reliability. While deep learning has shown promise for SoH modeling, obtaining high-quality training data remains challenging due to high testing costs and privacy constraints. This study proposes an enhanced federated learning (FL) approach, enabling multiple institutions to collaboratively train SoH models while preserving local data privacy. To address communication overhead in resource-constrained Internet of Things (IoT) environments and data heterogeneity across non-IID battery datasets, we introduce an adaptive wavelet-based compression framework. An Agent-specific Adaptive Top-K Sparsification (AATKS) mechanism dynamically adjusts the sparsification rate for each agent, mitigating the impact of data heterogeneity by preventing excessive information loss in critical updates while ensuring efficient communication. Additionally, a Layer-wise Energy Weighted Aggregation (LEWAgg) strategy enhances global model consistency by prioritizing high-energy coefficients, leading to more stable learning across heterogeneous clients. Experiments on three real-world LIBs datasets demonstrate that the proposed WCFL framework achieves a 14W reduction in communication cost with less than 0.75% accuracy loss compared to centralized training. This study provides an efficient and scalable solution for collaborative battery health management in practical IoT settings.

As the proliferation of 5th Generation Mobile Communication Technology (5G) accelerates the adoption of Internet of Things (IoT) applications, building robust and secure communication channel becomes increasingly crucial with the exponential growth of connected devices. The 3rd Generation Partnership Project (3GPP) has established security standards for 5G systems, including mechanisms such as the 5G-Authentication and Key Agreement (5G-AKA), which enables establish secure sessions in untrustworthy participants or insecure channels. The private key which untrustworthy parties have independently or transmitted through insecure channels, may involve risk of information leakage in 5G-AKA. Motivated by this challenge, we propose a practical and secure dual-server key agreement scheme based on password authentication for IoT devices in 5G networks. The scheme ensures secure reliable key storage and key transmission, mitigating risks associated with key information leakage through a dual-server architecture and three-lock security policy. Importantly, we avoid ownership of the complete key by any untrustworthy entity in insecure 5G network to ensure key security. The scheme can resilience to various security threats prevalent in 5G networks through rigorous formal security. We analyze the communication and computational loads to illustrate the protocol’s practicality and efficacy.

The collective acoustic pressure output of a piezoelectric ultrasound transducer array is limited by the residual stress induced non-uniformity of resonant frequencies among neighboring transducers. This limits the performance of array-level applications such as mid-air haptic feedback and range-finding. We propose a novel design with sector-shaped slits in the Lead Zirconate Titanate (PZT) layer of the transducer to not only reduce the sensitivity to residual stresses by 2.1×, but also to boost the baseline acoustic pressure output by 9.4 × as demonstrated by finite element analysis. Our patented design can be combined with the recent sputtered PZT platform to enable smaller transducer arrays with fewer transducers and high acoustic pressure output to enable future integration into mobile devices.

As the aging population of society continues to intensify, the series of problems brought about by aging is becoming more and more serious. Because the health problem of the elderly brings many social problems, people have paid close attention to it. Fortunately, as a typical smart healthcare system, wireless body area networks (WBANs) present quit nice medical care for people, especially the aged. However, personal health information is very sensitive. But, the common communication channel is used in WBANs and any malicious entity can initiate a security attack on WBANs. To ensure secure communication and privacy-preserving which are the premise of the sound development of WBANs, an improved and efficient certificateless authentication scheme with conditional privacy-preserving is proposed in this paper on the basis of analyzing the most recent presented certificateless authentication scheme for WBANs. The proposed scheme also provides batch authentication to decrease authentication and communication cost. A rigid security proof demonstrates that our proposed scheme resists every type of security attack and can provide condition privacy-preserving. The performance analysis shows that our proposed scheme has some advantages in computation and communication cost.

With the development of the Web, social media, the SINA Microblog for instance, is gaining wider popularity. This indicates most users' attitudes towards emerging technologies that happened recently. To obtain the data on social media, the traditional Web crawler is only able to obtain part of the information by collecting data in a non-login status because the crawler is not provided with the capabilities to log into the system. To enable information sharing among users, the SINA Microblog Open Platform offers a number APIs in which certain limitations such as those on the number of user requests of the microblog server, still exist. In this paper, a method that simulates user login to collect Microblog data related to hot topics on the SINA platform is proposed. What's more, the method overcomes the limitations of the SINA Microblog APIs, thus leading to getting more data from the system. In addition, the typical user behaviors, through the collected data, is found when they interact with social media.