Network reliability of Wireless Sensor Networks (WSNs) is difficult to be evaluated because of its complexity, multistates, and dynamic characteristics. To satisfy the user's need of reliability evaluation for network transmission we propose some evaluation models and a dynamical evaluation framework. The evaluation models are mission-oriented and based on transmission paths (uplink and downlink). The dynamic evaluation framework can be on-demand customized and it will be autoupdated once the communication environment changes or the nodes fail. Finally, we simulated clustered and mesh WSNs with NS-2. Simulation result shows that the proposed evaluation framework was effective and accurate.

Task assignment is a crucial problem in wireless sensor networks (WSNs) that may affect the completion quality of sensing tasks. From the perspective of global optimization, a transmission-oriented reliable and energy-efficient task allocation (TRETA) is proposed, which is based on a comprehensive multi-level view of the network and an evaluation model for transmission in WSNs. To deliver better fault tolerance, TRETA dynamically adjusts in event-driven mode. Aiming to solve the reliable and efficient distributed task allocation problem in WSNs, two distributed task assignments for WSNs based on TRETA are proposed. In the former, the sink assigns reliability to all cluster heads according to the reliability requirements, so the cluster head performs local task allocation according to the assigned phase target reliability constraints. Simulation results show the reduction of the communication cost and latency of task allocation compared to centralized task assignments. Like the latter, the global view is obtained by fetching local views from multiple sink nodes, as well as multiple sinks having a consistent comprehensive view for global optimization. The way to respond to local task allocation requirements without the need to communicate with remote nodes overcomes the disadvantages of centralized task allocation in large-scale sensor networks with significant communication overheads and considerable delay, and has better scalability.

Bearing fault diagnosis is a critical issue in rotating machinery. However, existing multimodal diagnostic methods often struggle to effectively extract key information in environments of strong noise interference. Large language models possess the capability to understand the internal physical mechanisms of bearings and can assist in feature learning, thereby enhancing diagnostic performance. Therefore, a semantic-aware and enhanced cross-attention method (SA-ECAM) is proposed for multimodal bearing fault diagnosis. First, a semantic-guided feature mapping mechanism enhances noise robustness by purifying sensor signals within a unified semantic space. Subsequently, a time-event synchronization strategy achieves precise cross-modal alignment. Finally, the derived domain-invariant semantic representations overcome the limitations of cross-operating-condition generalization in multimodal data fusion. To address sufficient separation and utilization of shared and private features in multimodal diagnosis, a cross-modal cross-attention mechanism (CMCA) is developed to build correlations between modalities, enabling information complementarity and enhancement. Furthermore, an SNR-based gating mechanism is introduced to dynamically suppress noise interference in modal features. Experimental results on two publicly available datasets show that the proposed method achieves high fault classification accuracy even in various strong noise environments and maintains robust performance under complex operating conditions, thereby fully validating its effectiveness and superiority.

Rolling bearings are essential in rotating machinery, and accurate remaining useful life (RUL) predictions are necessary for effective maintenance and optimal performance. Poor trend of health indicators (HI) and operating condition variations can reduce the reliability and accuracy of RUL predictions.To address these challenges, we propose a deep transfer network based on dual-task learning for predicting the remaining life of rolling bearings (DTLDL-RUL). This framework integrates health status assessment and RUL prediction, leveraging task commonalities and differences to create a robust model, then transfers it to improve generalization in the target domain. Specifically, we first mine spatiotemporal features from vibration signals to generate and classify HI, labeling them for subsequent tasks. Next, we use the shared feature extractors and private residual networks to capture common and specific features of each task, merge them with the multi-gate control networks, and adaptively adjust task weights in the loss function to enhance model adaptability. Finally, the trained model is transferred to the target domain using domain adaptation to extract domain invariant features and consider target-specific features, enhancing generalization. Furthermore, to enhance prediction accuracy, we incorporate physical models as constraints in the loss function, combining data-driven and physical principles to improve model interpretability. Experiments conducted on the 2012 PHM and XJTU-SY datasets demonstrate that the proposed method achieves high accuracy and generalization in RUL predictions.

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With the rapid growth in the number of IoT devices at the edge of the network, fast, flexible and secure edge computing has emerged, but the disadvantage of the insufficient computing power of edge servers is evident when dealing with massive computing tasks. To address this situation, firstly, a software-defined edge-computing architecture (SDEC) is proposed, merging the control layer of the software-defined architecture with the edge layer of edge computing, where multiple controllers share global information about the network state through an east–west message exchange, providing global state for the collaboration of edge servers. Secondly, a reinforcement-learning-based software-defined edge task allocation algorithm (RL-SDETA) is proposed in the software-defined IoT, which enables controllers to allocate computational tasks to the most appropriate edge servers for execution based on the global network information they have obtained. Simulation results show that the RL-SDETA algorithm can effectively reduce the finding cost of the optimal edge server and reduce the task completion time and its energy consumption compared to various task allocation methods such as random and uniform.