Network covert channels in the Internet of Things (IoT) can conceal device communication behaviors, thereby protecting user privacy and ensuring secure transmission of sensitive data. However, existing active covert channels in IoT are constrained by the low computational power and limited storage of IoT terminal devices, making them unsuitable for complex steganographic algorithms. Moreover, the active covert channel is more vulnerable to targeted detection and blocking by traffic analysis. To address these challenges, this study proposes a passive network storage covert channel (PNSCC) that leverages intermediate IoT nodes. We also present solutions for synchronization and reversible data hiding within the PNSCC. A covert channel kernel module was designed and deployed on smart routers running the OpenWrt system, followed by experimental testing. The results indicate that the PNSCC achieves a relatively high capacity, with a covert data transmission rate of approximately 88.97 bps in real-world network environments. Additionally, it has minimal impact on actual network performance and exhibits resilience against traffic analysis.

As an important and widely studied research topic, the detection of fresh milk plays a crucial role in protecting consumer health. However, due to the diversity of milk brands and environmental conditions in real-world application scenarios, the accuracy of existing detection models often drops significantly when applied to new domains. A significant amount of new data needs to be gathered in order to retrain the model for new domain, which greatly increases both time and labor costs. To tackle this problem, this paper introduces a transfer learning-based cross-domain milk freshness detection method. The method uses transfer learning to generate data for unknown categories within the target domain. Specifically, the transfer generation model trains on data from both the target and source domain categories. With the proposed model, data from other categories in the source domain can be utilized to generate corresponding target domain data. This method comprehensively considers data generation from multiple perspectives of time, frequency, and spatial, to enhance the authenticity of the generated data. It uses a transfer generation architecture, consisting of a transfer network and a decoder, to learn the mapping relationship between the source and target domain, helping to narrow the gap between domains. Additionally, a feature subspace decomposition method is introduced into the decoder, and a cross-domain consistency loss function is formulated to strengthen the model’s learning capability. The proposed method is shown to generate high-quality data for unknown target-domain categories in multiple cross-domain settings, leading to a performance improvement ranging from 17.64% to 32.44% in the detection of cross-domain milk freshness.

With the continuous evolution of the network environment and attack patterns, existing deep learning-based traffic monitoring systems face challenges in terms of adaptability and computational resource requirements. To address these issues, this paper proposes an innovative traffic detection system, DeepFlow-BiViTGAN, which combines Generative Adversarial Network (GAN) and Vision Transformer (ViT) architectures with an improved loss function to enhance detection accuracy and system robustness. Experimental results indicate that DeepFlow-BiViTGAN achieves state-of-the-art detection performance on multiple public datasets when trained with approximately 3% to 5% of the total dataset. Its lightweight design enables efficient operation on resource-constrained IoT devices, offering excellent adaptability and scalability. This research provides new insights into the application of deep learning in traffic monitoring, particularly in IoT scenarios with limited data, and demonstrates significant advantages.

The proliferation of IoT devices has limited the efficiency of heterogeneous data communication in distributed environments and increased security risks. Balancing scalability, efficiency and data privacy in IoT transaction systems becomes critical, and intent-based networks enable optimal configuration with minimal intervention. To optimize the network management environment, we propose a three-stage execution scheme for blockchain cross-shard transactions, which combined with a timeout rollback mechanism ensures atomicity and reduces latency. In addition, we design a fragment-based consensus protocol utilizing a verifiable random function, which improves the consensus efficiency through the randomness of committee member selection. In order to enhance system security, we introduce a reputation evaluation mechanism and a malicious node detection method based on normalized entropy. The mechanism dynamically adjusts the reputation value of a node according to its performance in the consensus process, so that high-reputation nodes can play a greater role in the consensus and detect malicious nodes in the network accordingly. By embedding this mechanism into a network management framework based on users’ intention, it can accurately realize users’ expectations for network performance optimization, security enhancement and efficient operation. Experiments show that our scheme not only improves communication efficiency, but also enhances the security of sharded transactions, effectively matching users’ high-level intentions for network scalability, efficiency, and data privacy.

Accurate classification of encrypted IoT traffic remains challenging due to evolving applications and distribution shifts. This work presents a self-updating hybrid meta-learning framework that integrates Bayesian Neural Networks (BNN) for uncertainty-aware update triggering with a Random Forest meta-classifier for robust decision fusion. The proposed design improves scalability and interpretability through feature-importance analysis and lightweight ensemble learning. Prediction instability is quantified using the Hellinger distance, avoiding normalization overhead and enabling an adaptive familiarity score via a tunable parameter α. Experimental results on encrypted traffic datasets demonstrate significant gains in reliability, achieving up to 95.7% accuracy and 0.95 macro-F1, and effective selective retraining under distribution shifts.

To capture the small signal of smart sensor system, the high-precision sigma delta analog to digital converter (ΣΔ ADC) is the essential components. Based on the reinforcement learning (RL) algorithm, a high-efficiency design method for 16-bit ΣΔ ADC is proposed in this research. The key circuit modules in the ΣΔ ADC are extracted to be separately optimized to decrease the design complexity. The prior knowledge of circuits can be introduced into the search process of RL algorithm to accelerate the search speed. In addition, the deep neural network (DNN) models are trained to replace the circuit simulator to reduce the cost of simulation resource. Two key circuits in the ΣΔ ADC are optimized. Compared with the advanced method, the proposed optimization method has the fastest convergency and best performance, which demonstrates the effectiveness of the proposed method. In addition, the simulation time can be decreased by 98.3 %, which can greatly speed up the optimization process. The optimized ΣΔ ADC is taped out. The test results show that the effective resolution is 16.16 bit, and the voltage noise is 8.7e-05 V. Compared with the original chip, the effective resolution can be improved by 6.3%, and the noise voltage can be decreased by 47%. Therefore, the proposed optimization design method for ΣΔ ADC can greatly decrease the design cycle and improve the performance of ΣΔ ADC, which presents the enormous potential application in the internet of things.

With the wide use of distributed energy resources, it is important to build efficient and trustworthy coordination among source, grid, load, and storage (SGLS) for the Energy Internet. Blockchain can provide a base of trust, but getting off-chain data through decentralized oracles still faces problems of low efficiency and poor reliability. To address these issues, this paper proposes a four-layer architecture that joins blockchain and oracle services. It also includes a data aggregation and checking algorithm based on threshold signatures and a reputation-based oracle selection method. The main idea of the algorithm is to find reliable nodes more efficiently by using a changing, multi-factor reputation model, and to make the aggregation process faster through preselection and threshold signatures. In this way, it keeps both reliability and efficiency in complex network settings. Simulation results show that the proposed method increases the speed of putting data on the chain and lowers delay, while enhancing the robustness of the oracle network under adverse network conditions. This work providesuseful technical support for building an efficient and dependable distributed-energy coordination infrastructure.

With the widespread adoption of mobile smart devices and the Internet, a new paradigm known as Mobile Crowdsourcing (MCS) has emerged, which can provide efficient execution of large-scale tasks. However, it faces challenges due to an insufficient number of crowdsourcing participants. Existing works primarily focus on increasing participants’ willingness to engage, without addressing the issue of a limited user pool. In this paper, we incentivize users to diffuse the task within their social networks through financial rewards in the task duration, thereby recruiting additional social neighbors, expanding the user pool, and maximizing task completion. Specifically, we propose a Dynamic Reward Update and User Selection under the Diffusion (DRUUSD) algorithm for social network diffusion scenarios, given a budget constraint. The algorithm dynamically updates the task diffusion reward and task execution reward based on the current recruitment, while selecting users with the highest utility whose activated social neighbors can contribute more to task completion to diffuse tasks. Through multiple time steps of user selection and reward updates, the algorithm aims to recruit more participants and maximize task completion. We run extensive experiments to evaluate the proposed algorithm against four benchmark methods. The experimental results show that under the same total budget constraint, our algorithm can recruit more mobile users and achieve higher task completion compared to other benchmark algorithms.