Flexible wearable sensors, characterized by their stretchability, flexibility, lightweight, high sensitivity, and the ability to dynamically and continuously measure multiple parameters, have been widely applied in areas such as physiological health monitoring, human-computer interaction control, real-time motion monitoring, and personalized medical assistance. With the development of artificial intelligence technology, the integration of flexible wearable sensors with artificial intelligence technologies such as machine learning has become increasingly profound. This integration has facilitated the emergence and development of intelligent sensing systems with human-like perception capabilities, garnering growing attention towards flexible wearable intelligent sensing technology. This paper focuses on introducing various sensor mechanisms, intelligent sensing algorithms, and practical applications of flexible wearable intelligent sensing technology. The challenges and possible development directions of flexible wearable smart sensing technology, encompassing aspects such as power supply, multimodal sensing capability, preparation materials, processing and fabrication techniques, and smart sensing algorithms, are also discussed and prospected. Such a review can provide a reference for research in the field of flexible wearable smart sensing technology.

This paper considers a device-edge co-inference system for image classification tasks. The system splits a deep learning model between an edge device (ED) and an edge server (ES), where the ED feeds raw images to its local model and transmits the output semantic features to the ES for classification. We address two key challenges in transmitting continuous features through modern digital wireless communication systems. First, we devise a smooth sine-based surrogate function to approximate the non-differentiable quantization computation applied to the continuous feature vector during model training. This approach results in stable gradient backpropagation and efficient training convergence. Second, we propose a novel task-oriented automatic repeat request (ARQ) mechanism, namely TARQ, to overcome communication error. Compared to the conventional bit-error detection based ARQ, TARQ makes retransmission decisions based on the current SNR (Signal-to-Noise Ratio) and inference result, effectively reducing the retransmission probability without degrading the inference accuracy. Simulation results show that the proposed scheme enjoys fast training convergence and attains an inference accuracy only 0.62% lower than the ideal performance. Meanwhile, it reduces the inference delay of the conventional ARQ scheme by 25.5% on average, achieving an efficient tradeoff between inference accuracy and delay.

Efficient incomplete multi-view clustering has received increasing attention due to its ability to handle large-scale and missing data. Although existing methods have promising performance, 1) they typically generate anchors directly from incomplete and noisy raw data, resulting in uncomprehensive anchor coverage and unreliable results; 2) they typically use only sparse regularization to remove noise and overlook outliers; 3) they ignore the inherent consistency of features in a view. To address these issues, we propose a smoothness-induced efficient incomplete multi-view clustering (SEIC) method. SEIC regards available data as natural anchors selected from complete data, and performs matrix decomposition only on them to obtain reliable small-size representation matrices. View-specific representation matrices are constructed as a tensor to capture consensus and guide matrix decomposition. More significantly, we enforce both smoothness and low-rank coupling on the tensor. Smoothness induces continuous variation of the tensor to further eliminate noise and enhance the relation among features. Benefiting from the noise robustness of SEIC, we design an adaptive noise balance parameter that renders SEIC parameter-free. Furthermore, by constructing a sparse anchor graph on the learned tensor, we propose the spectral clustering version SEIC-SC. Experiments on multiple datasets demonstrate the superior performance and efficiency of SEIC and SEIC-SC.

Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynamic environments. Addressing these challenges, this study introduces an adaptive deep ant colony optimization (ADACO) algorithm, which significantly improves efficiency and convergence speed through enhanced pheromone diffusion mechanisms and updating strategies, applied to global path planning. To adapt to dynamically changing environments and achieve more precise local path planning, an asymmetric strategy network TD3 algorithm (ATD3) is further proposed, which utilizes global path planning information within the strategy network only, creating a new hierarchical path planning algorithm—ADACO-ATD3. Simulation experiments demonstrate that the proposed algorithm significantly outperforms in terms of path length and number of iterations, effectively enhancing the mobile robot’s path planning performance in complex dynamic environments.

The imminent deployment of sixth-generation (6G) wireless communication systems promises new opportunities and challenges for model training using data from the edge devices in the Internet of Things (IoT). However, current research has yet to fully address the efficiency and scalability challenges arising from the extensive connectivity of edge devices across various scenarios. The presence of malicious devices further intensifies the system uncertainty during large-scale data interactions and model training, making it difficult for a single model to effectively manage the complexities introduced by heterogeneous devices and dynamic network conditions. To overcome these challenges, we propose FedSC, an innovative edge computing framework that leverages side-chain technology for efficient edge node management and employs federated learning to enable robust cross-device and cross-scenario model interactions. To accelerate the multimodel aggregation process, we introduce an asynchronous cross-domain iterative algorithm (ACDI) based on smart contracts. Additionally, to mitigate the impact of malicious and inactive nodes, we propose a robust consensus algorithm and a committee mechanism for leader node election based on contribution value. Experimental results demonstrate that the proposed FedSC achieves a 3.2% and 44.23% accuracy improvement on i.i.d. and non-i.i.d. dataset, respectively, along with a remarkable latency reduction of 256.51%, compared to FedAvg. Our work is conducive to the training of multiple models in different IoT scenarios, utilizing substantial amounts of IoT device data and facilitating collaboration between models. Furthermore, it enables the provision of fundamental services to diverse applications in 6G.

Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.