Resource-constrained IoT devices in Industrial Internet environments are highly vulnerable to DDoS attacks due to infrequent security updates and insufficient built-in protection mechanisms. Existing defense solutions primarily rely on external filtering servers or programmable switches, but these approaches fail to simultaneously meet the stringent real-time performance and high accuracy requirements of industrial applications. To address these limitations, we propose a novel cross-plane defense framework that exploits the temporal invariance characteristics of attack traffic patterns. In the data plane, an adaptive variance threshold mechanism immediately mitigates high-volume, low-variance traffic flows, while a bidirectional dual-hash table captures low-collision flow features for efficient export to the control plane. The control plane constructs temporally-enhanced flow sequences that enable deep learning models to perform accurate attack detection, subsequently directing the data plane to block identified malicious sources. We implemented and evaluated a prototype of this framework on a software switch platform using both real-world attack datasets and custom-generated traffic patterns. Experimental results demonstrate that our framework successfully mitigates 86% of attack traffic within milliseconds and achieves complete source blocking within 52 seconds. Compared to baseline methods, our framework can effectively counter both DoS and DDoS attacks without generating false positives on benign traffic.

Wireless rechargeable sensor networks (WRSNs) have emerged as a promising solution to overcome the energy bottleneck in traditional battery-powered sensor networks. However, the uncertain energy demands and dense deployment of sensor nodes pose significant challenges to efficient charging scheduling in WRSNs. To address these challenges, this article proposes a novel multi-AAV assisted on-demand partial charging scheduling (MOPCS) algorithm. MOPCS integrates the advantages of one-to-many charging, partial charging, and dynamic multi-AAV coordination to maximize the network lifetime and energy utilization. The key contributions of this work include a real-time adaptive charging scheduling trigger mechanism, an energy-efficient charging cluster division method, a spatiotemporally balanced task allocation among multiple autonomous aerial vehicles (AAVs), and a hybrid priority-based charging path planning algorithm. Extensive simulations demonstrate that MOPCS significantly outperforms state-of-the-art algorithms in terms of charging request response timeliness, node survival rate, and AAV energy efficiency, especially in dense network deployments. This work provides valuable insights and practical solutions for the design and optimization of AAV-assisted charging scheduling in WRSNs, paving the way for more sustainable and scalable wireless sensor networks in various application scenarios.

Supply Chain Networks (SCNs) play a vital role in achieving strategic decision-making for production and distribution facilities, aiming to meet market demands and gain competitive advantages. With the application of new-generation information technology in the supply chain, enterprises within SCNs generate a substantial volume of relevant business data. Sharing this data among SCN enterprises can effectively reduce operating costs, optimize business processes, and enhance the overall efficiency of the supply chain. However, effective data sharing among SCN participants faces challenges, such as data leakage, data quality assurance, and fair data value allocation. To address these challenges, this paper proposes a secure cross-domain data sharing model in SCNs (named SCN-CDSM) based on consortium blockchain technology. The model introduces trust, enables cross-domain data exchange, and promotes cooperation among supply chain enterprises. To ensure privacy, group signatures and access control smart contracts are designed, along with an approach to reduce blockchain throughput limitations. Furthermore, a sharing incentive mechanism utilizing the Stackelberg game model based on data value is designed to foster fairness and collaboration. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed schemes, achieving both security and efficiency in data sharing within SCNs.