Engineered systems are increasingly integrating sensor techniques to trace their specific degradation behaviors, so as to facilitate their dynamic reliability assessment. Due to the hierarchical structure of these systems, sensing data can be collected at multiple physical levels, including the entire system, subsystems, and components. The quality of collected multilevel sensing data, however, decreases inevitably with the degradation of sensors mounted within each system, leading to a declining trustworthiness of dynamic reliability assessment for each specific individual system. This article develops a new dynamic reliability assessment framework of hierarchical multistate systems suffering from sensors’ degradation. The proposed framework mainly contains three steps: 1) utilizing discrete-state and continuous-state stochastic processes to, respectively, model the degradation behaviors of two types of sensors; 2) integrating these two types of sensors’ degradation models to update the joint state probability distribution of both the monitored objects and sensors by fusing multilevel sensing data; 3) deriving the marginal state probability distribution of the entire system to dynamically assess system reliability. A three-component system and an electromechanical actuator system in landing gear systems are exemplified to illustrate the performance of the proposed method.

Reliability assessment of multiprocessor systems presents the theoretical foundation for the layout and optimization of multiprocessor systems. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h$</tex-math></inline-formula>-extra edge-connectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{h}$</tex-math></inline-formula> and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-component edge-connectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c\lambda _{k}$</tex-math></inline-formula>, as extensions of the classical edge connectivity, are two precise metrics for the measurement of the reliability of multiprocessor systems. For multiprocessor systems, determining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c\lambda _{k}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{k}$</tex-math></inline-formula> of a large <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is still difficult. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta _{G}(0)=0$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta _{G}(i)=\frac{1}{2}(\text{ex}_{i+1}(G)-\text{ex}_{i}(G))$</tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$i\in \lbrace 1, \ldots, |G|-1\rbrace$</tex-math></inline-formula>, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{ex}_{i}(G)={\mathrm{max}}\lbrace 2|E(G[S])|: S\subseteq V(G), |S|=i \rbrace$</tex-math></inline-formula>. In this article, we obtain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c\lambda _{k}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{h}$</tex-math></inline-formula> of the Cartesian powers of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula>-regular graphs for which the lexicographic order yields an optimal order and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta _{G}(i)\leq \frac{d}{2}$</tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$i=0, 1, \ldots, \lfloor \frac{|G|-1}{2}\rfloor$</tex-math></inline-formula>. Our result improves some previous results about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c\lambda _{k}$</tex-math></inline-formula> of Hamming graphs by Yang et al. (2023), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{h}$</tex-math></inline-formula> of the Cartesian powers of the complete graph <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K_{4}$</tex-math></inline-formula> by Tian et al. (2022).

Aerial computing paradigms, particularly those involving UAVs as access points and radio towers, show significant promise for local data analysis and real-time service provision in aerial access networks. However, the limited battery life of UAVs, compounded by the high-energy demands of communication tasks and prolonged computation delays, presents a significant challenge. Deep reinforcement learning (DRL) enables UAVs to autonomously optimize their operations, reducing both energy consumption and latency. Nevertheless, the prolonged learning process of DRL can lead to inefficiencies, especially in dynamic environments where the equitable participation of UAVs is crucial. To address these issues, we introduce a fairness-oriented federated learning (FL) scheme that employs importance sampling to select UAVs for training, ensuring equitable utilization of each UAV's data. Furthermore, we integrate this FL fairness scheme into the design of DRL algorithms, termed FedDRL. This algorithm jointly optimizes the computation capabilities and bandwidth allocation of UAVs to minimize system costs. Numerical results demonstrate the fairness of FedDRL in fFL networks. Specifically, compared to other state-of-the-art DRL algorithms (e.g., TD3 and DDPG), FedDRL reduces system costs by 43.82% and 49.45%, respectively.

Reduced precision redundancy (RPR) has been widely used as an alternative to triple modular redundancy to enhance reliable computing with tolerance to errors and faults; however, it still has stringent requirements for power and area as well as complex decision hardware. Recent works have focused on reduced precision redundancy by approximation (RPA) to attain lower delay, smaller power dissipation, and smaller area because RPA operates using only logic operations (so no arithmetic unit is involved as decision hardware). The proposed RPA-based designs can tolerate up to two erroneous modules, so adding a further data word to the decision process compared to previous three-module based RPA designs found in the technical literature. The proposed designs consist of four modules, two exact data words and two approximate data words (denoted as 2E2A), where E (A) stands for exact (approximate) data words. The probability of generating an output exact data word by RPA under one and both data parts of erroneous modules is analytically found; simulation results are also provided. The difference between simulated and analytical probabilities is at most 7.3%. Figures of merits (such as delay, power dissipation, and area) of the proposed designs are assessed by simulation and compared with RPR and the three-module RPA. The proposed designs incur a similar delay as a three-module RPA; power dissipation and area (due to the additional module) can be adjusted by increasing the number of approximate bits while still retaining a two-module error tolerance. This article also presents its application to image processing, and arithmetic (i.e., the addition operation); the results show that the proposed schemes are very efficient.

Static and dynamic methods are used to assess the efficiency and vulnerability of transportation networks. Dynamic methods identify both where and when disruptions would be most detrimental. However, exhaustive analysis is computationally prohibitive for large networks because thousands of simulations are required. To enhance the scalability of dynamic transportation network vulnerability assessment, this article presents a subnetwork approach to identify the impact of an edge closure at a specific time by simulating only a portion of the network immediately surrounding the disruption and then incorporating this subnetwork simulation into the baseline scenario without disruptions. Our experiments confirm a strong correlation between the results of complete network simulation and the subnetwork approach with significant computational reductions. Subnetwork size is a tunable parameter, enabling a tradeoff between the accuracy of the subnetwork approach relative to the exhaustive approach and time savings achieved. We also identify a heuristic method to select the subnetwork size for any network to reap the greatest benefits with respect to accuracy and speed up. The subnetwork vulnerability assessment method is subsequently used to allocate limited resources to mitigate travel time disruptions. The approach only required hours not months to complete, advancing methods for city-scale disaster recovery and planning.

Most network planning problems in the literature consider metrics, such as cost, availability, and other technology-aware attributes. However, network operators now face new challenges in designing their networks to minimize their dependencies on manufacturers. A low dependency is associated with higher network robustness in case one or more manufacturers fail due to erroneous component design, geopolitical banning of manufacturers, or other reasons discussed in this work. Our work discusses network sovereignty, i.e., the ability to operate a network while minimizing the dependencies on a particular manufacturer to minimize the impact of simultaneous manufacturer failure(s). Network sovereignty is considered by solving the manufacturer assignment problem in the network such that robustness is maximized. The following three main contributions of this work are, first, the discussion of network sovereignty as a special attribute of dependability, second, the introduction of a novel metric—the path set diversity (PSD) score to measure a network's sovereignty based on the manufacturers used in the network, and, third, the introduction of Naga, an integer linear program formulation to maximize network sovereignty using the PSD score. We compare the Naga’s performance with centrality metrics-based heuristics and an availability-based optimization. Our work aims to be the foundation to guide network operators in increasing their network sovereignty.

Connection reliability, which describes the capability that paths exist between specified nodes in a network, has been widely studied. However, the states of both the network and its nodes/edges have epistemic uncertainty owing to the lack of data and information, which makes existing connection reliability assessment methods unacceptable. To solve this problem, this article proposes a new connection reliability based on uncertainty theory: Belief connection reliability. Considering different node connection requirements, we define two belief connection reliability metrics as single-node-pair belief connection reliability (SBCR) and multi-node-pair belief connection reliability (MBCR). Based on the uncertain graph, an extended uncertain graph is built to model networks whose nodes and edges' existence has epistemic uncertainty, and two algorithms are proposed to compute SBCR and MBCR based on finding the most reliable connection path. Finally, a comparison study on a small network is used to illustrate the correctness of the proposed method, and the Belgian telephone interzonal network is used as a case to indicate the effectiveness of our network model and algorithms.

Power substations are widely used as crucial components of power grids but also exposed to a high risk of earthquakes. Enhancing the resilience of power substations plays an important role for power grids to resist earthquakes. However, the uncertainty of equipment failure has posed a significant obstacle to enhancing resilience. In this article, considering the equipment hardening strategy for the power substation, a two-stage distributionally robust optimization model is put forth for earthquake resilience enhancement of the power substations. The optimal equipment hardening strategy is determined prior to an earthquake, and the recovery sequence of the damaged equipment is optimized after the earthquake to enhance the resilience of the power substation. A decision-dependent moment-based ambiguity set is constructed to model the impact of the hardening strategy on the uncertain failure probability of equipment. A customized nested column-and-constraint generation algorithm (NC&CG) is put forth to solve the optimization model. To improve the computational efficiency, we propose a novel heuristic algorithm to solve the main problem of the inner NC&CG according to the power substation structure. The proposed method is demonstrated in the case of a simplified 220 kV power substation. The results show that the distributionally robust approach can effectively enhance the earthquake resilience of power infrastructure under uncertainty of equipment failure. The value of implementing an effective algorithm to quickly obtain the optimal solution in large-scale problems is also highlighted.

Aviation Internet of Things (AIoT) data sharing can create tremendous value for participants. With the development of AIoT and intelligent civil aviation, data security and privacy protection have become more prominent. To improve the data security and privacy of AIoT, and allow heterogeneous devices to participate in federated training, in this article, a blockchain-based federated knowledge distillation (BFKD) is proposed. First, federated knowledge distillation is used for knowledge transferring between clients to protect client data privacy. Second, a clustering algorithm is used to select aggregated soft labels to enhance Byzantine resistance. Finally, the consortium blockchain validates the data exchange process of federated knowledge distillation. In addition, a federated score grouping practical Byzantine fault-tolerant algorithm is proposed for improving consensus efficiency and to encourage participants to contribute more public data and honestly participate in federated training. Theoretical analysis and experimental results show that BFKD boasts high data mining performance and communication efficiency while safeguarding data privacy and security.