Fault Tolerance Research Articles

Hybrid AI- and Blockchain-Powered Secure Internet Hospital Communication and Anomaly Detection in Smart Cities

Internet of Things (IoT) devices have revolutionized real-time monitoring and distant patient care in smart cities’ healthcare systems. However, this advancement has come with several issues, such as data security, scalability, operational efficiency, and fault tolerance. Previous approaches are not well suited to the real-time processing of IoT data in healthcare, given the low latency, high throughput, and effective anomaly detection needed for such a task. Given these challenges, this paper proposes a hybrid Artificial Intelligence (AI)- and blockchain-based IoT governance framework for Internet hospitals using Proof-of-Authority (PoA) in smart cities. It encompasses application of the enhanced RSA for secure data transmission, real-time anomaly detection through the Isolation Forest algorithm, and a private blockchain architecture designed for high scalability. It effectively detects tampering and replay attacks to minimize illegitimate and unauthorized access or manipulation of patients’ data. The proposed framework achieves relatively significant improvements over a state-of-the-art baseline model. It has cut the transaction response time by 50%, doubled the Throughput Per Second (TPS), and attained a 100% detection performance in anomalies. Comparative analysis reveals its linear scalability with increasing workload, ensuring consistent performance under varying transaction volumes. This study’s findings highlight the proposed framework’s potential to mitigate key issues in IoT-enabled Internet hospitals in smart cities.

Modularized Cathode with Neural Network Topology for High Rate and Fault-Tolerant Lithium-Sulfur Batteries.

Enhancing the redox kinetics of electrodes, achieving synergistic optimization of local energy conversion and overall charge transfer, and overcoming the technical bottleneck of significant performance degradation due to local unit failure in traditional electrode systems are crucial for developing high-rate lithium-sulfur batteries. Here, a modular cathode system (CoB1N3-MR/FNN) with a fully connected cascade neural network topology (FNN) is designed by constructing microreactor modules (CoB1N3-MRs) with embedded nanozymes (Co-B1N3), ordering and efficiently interconnecting them. This system not only enables efficient energy conversion within individual microreactors but also significantly enhances the long-range charge transport efficiency and energy aggregation capacity of the electrodes. Furthermore, CoB1N3-MR/FNN achieves fault tolerance to local damage through its distributed energy storage units and redundant charge transport channels. This synergistically enhanced modular electrode system for energy conversion and charge transport exhibits high specific discharge capacity (0.2 C, 1211 mAh g-1) and excellent rate capability (5 C, 731.26 mAh g-1; 10 C, 471.05 mAh g-1), and shows outstanding electrochemical performances in high sulfur loading, low electrolytes, and flexible pouch batteries (0.2 C, 1165 mAh g-1), fully demonstrating its practical application value.

Forensic Support for Abraham et al.’s BB Protocol

The consensus protocol is a fundamental building block in distributed computing and has been widely used in blockchain systems in recent years. Paxos, introduced by Lamport, stands out as one of the most widely adopted consensus protocols and has found application in renowned distributed systems, including Google’s Spanner system. Abraham et al. analyzed the FaB Paxos protocol, a Byzantine version of Paxos. They abstracted the single-shot FaB Paxos into a Byzantine broadcast protocol and further gave an enhanced protocol known as Abraham et al.’s BB. Abraham et al.’s BB protocol achieved optimal two-round message interaction under good conditions, satisfying the optimal fault tolerance threshold of n=5t−1 where n represents total number of nodes in the system and t denotes the tolerable number of Byzantine nodes. This paper delves into scenarios wherein the actual number of Byzantine nodes surpasses the fault tolerance threshold during the operation of Abraham et al.’s BB protocol. To address this, we propose a forensic protocol designed to offer forensic support in cases of agreement violations. The forensic protocol aims to label Byzantine nodes through irrefutable evidence. We analyze the forensic protocol, elucidating the number of Byzantine nodes that the forensic protocol can label under different circumstances, along with the corresponding number of required messages. Additionally, we present an impossibility result, indicating that forensic support for Abraham et al.’s BB is impossible when the number of Byzantine nodes exceeds 2t−2.

A Single Source Nine‐Level Quadruple Boost Inverter With Optimized Switching Technique for EV Applications

ABSTRACTTraditional MLIs face several challenges, such as complex configurations, intricate switching control, difficulty in generating gate pulses, a large number of components, and high voltage stress on semiconductors. Additionally, increasing the number of voltage levels tends to raise the device count, which can add to the system's complexity, cost, and, in some cases, reduce reliability. In this paper, a new multilevel inverter (MLI) incorporating switched capacitors for improved performance is proposed. The proposed single source quadruple boost nine‐level inverter (SS‐QBNLI) topology addresses these issues by generating a nine‐level output voltage using only 10 switches, 1 diode, and 2 capacitors. Notably, the inverter achieves a voltage gain of four times the input. The capacitors are self‐balanced without requiring any external balancing circuitry. A simple pulse‐width modulation (PWM) technique based on logic gates is employed to ensure balanced capacitor operation. This approach also reduces the number of switches and minimizes voltage stress, while providing built‐in fault tolerance. The paper presents a detailed comparison with other related topologies. Simulations are conducted in MATLAB/Simulink under various conditions to validate the performance. Finally, the ability of the proposed inverter to achieve voltage boosting is experimentally confirmed from a laboratory prototype.

Structure Fault Tolerance of Fully Connected Cubic Networks

An interconnection network is usually modeled by a graph, and fault tolerance of the interconnection network is often measured by connectivity of the graph. Given a connected subgraph L of a graph G and non-negative integer t, the t-extra connectivity κt(G), the L-structure connectivity κ(G;L) and the t-extra L-structure connectivity κg(G;L) of G can provide new metrics to measure the fault tolerance of a network represented by G. Fully connected cubic networks FCn are a class of hierarchical networks which enjoy the strengths of a constant vertex degree and good expansibility. In this paper, we determine κt(FCn), κ(FCn;L) and κt(FCn;L) for t=1 and L∈{K1,1,K1,2,K1,3}. We also establish the edge versions λt(FCn), λ(FCn;L) and λt(FCn;L) for t=1 and L∈{K1,1,K1,2}.

Virtual Channel Purification

Quantum error mitigation is a key approach for extracting target state properties on state-of-the-art noisy machines and early fault-tolerant devices. Using the ideas from flag fault tolerance and virtual state purification, we develop the virtual-channel-purification (VCP) protocol, which consumes similar qubit and gate resources as virtual state purification but offers stronger error suppression with increased system size and more noisy operation copies. The application of VCP does not require specific knowledge about the target quantum state, the target problem and the gate noise model in the target circuit, and can still offer rigorous performance guarantees for practical noise regimes as long as the noise is incoherent. Further connections are made between VCP and quantum error correction to produce the virtual error-correction (VEC) protocol, one of the first protocols that combine quantum error correction (QEC) and quantum error mitigation beyond directly applying error-mitigation protocols on top of logical qubits. Assuming perfect syndrome extraction, VEC can virtually remove all correctable noise in the channel while paying only the same sampling cost as low-order purification. It can achieve QEC-level protection on an unencoded register when transmitting it through a noisy channel, removing the associated encoding qubit overhead. Another variant of VEC can mimic the error-suppression power of the surface code by inputting only a bit-flip and a phase-flip code. Our protocol can also be adapted to key tasks in quantum networks like channel capacity activation and entanglement distribution. Published by the American Physical Society 2025

Fast autoscaling algorithm for cost optimization of container clusters

Container clusters are widely used to execute containerized applications in cloud environments. An essential characteristic implemented by these clusters is autoscaling, which is the ability to automatically adapt the computing resources of a cluster to support variable workloads. Precise adjustment of cluster resources to its workload in each autoscaling operation is essential to control cluster deployment costs. Several resource allocation models have been developed with the objective of cost minimization. However, as the number of containers and virtual machines of the cluster increases, resource allocation problems become too complex, and cannot be solved in reasonable time by existing resource allocation models. In this paper we present FCMA (Fast Container to Machine Allocator), a resource allocation algorithm designed to calculate a suitable allocation of the resources of a cluster in autoscaling operations, to minimize cluster deployment costs. The main motivation for the development of FCMA has been to significantly reduce the solving time of the resource allocation problem compared to a previous state-of-the-art optimal Integer Linear Programming (ILP) model. In addition, FCMA addresses secondary objectives to improve fault tolerance and reduce container and virtual machine recycling costs, load-balancing overloads and container interference. We have conducted an experimental evaluation to assess the effectiveness of FCMA, using the ILP model and two heuristics as a baseline. The experiments show that FCMA is much faster than the ILP model, with an average solving time reduction of two orders of magnitude. This gain in speed does not compromise the quality of the solutions, which have a cost on par with those of the ILP model. In comparison to the heuristics, FCMA achieves similar solving times while consistently delivering more cost-effective solutions.

Design of a Universal Safety Control Computer for Aerostats

Amid rapid global aviation development and increasingly stringent safety standards, aerostats demonstrate vast potential in environmental monitoring, communication relay, cargo transportation, and other applications. However, their operational safety has become a critical focus. These systems face complex flight environments and dynamic mission requirements that demand exceptionally high safety control standards. As the core component, the safety control computer directly determines the overall safety and stability of aerostat operations. This study employed a systems engineering methodology integrating hardware selection, software architecture design, fault diagnosis, and fault tolerance to develop a universal safety control computer system with high reliability, robust real-time performance, and adaptive capabilities. By adopting high-performance processors, redundant design techniques, and modular software programming, the system significantly enhanced anti-interference performance and fault recovery capabilities. These improvements ensured precise and rapid safety control monitoring under diverse operational conditions. Experimental validation demonstrated the system’s effectiveness in supporting both remote and autonomous safety control modes, substantially mitigating flight risks. This technological breakthrough provides robust technical support for the large-scale development and safe operation of universal aerostat systems, while offering valuable insights for safety control system design in other aerospace vehicles.

Current Balancing Control Strategy for Dual‐Channel PMSM Under Unbalanced Power Supplies

ABSTRACTThe dual‐channel permanent magnet synchronous motor (PMSM) combines high torque density, low torque ripple, and strong fault tolerance, making it ideal for applications in electric vehicles, aerospace, and wind turbine generators. However, the specific existence of unbalanced power supplies leads to current harmonics and current unbalance, which increases system losses and significantly affects control performance. In this paper, an improved current control scheme is proposed to solve these problems. First of all, the proposed approach introduces a transformation matrix that reformulates two odd harmonics into one even harmonic component, reducing the number of controllers. Meanwhile, the PI controller paralleled with multiple resonant controllers is developed, considering compensation in both the d–q and x–y subspaces, so that the major harmonics are suppressed. Even under substantial voltage differences, the system achieves a marked reduction in harmonics compared to traditional unbalanced supply systems. Finally, the experimental results demonstrate that the proposed control strategy can effectively improve performance.

Methods for Ensuring Fault Tolerance in High-Load Applications

Methods for Ensuring Fault Tolerance in High-Load Applications

A Two-Stage Fault Reconfiguration Strategy for Distribution Networks with High Penetration of Distributed Generators

In distribution networks with high penetration of distributed generators (DGs), traditional fault reconfiguration strategies often fail to achieve maximum load recovery and encounter operational stability challenges. This paper proposes a novel two-stage fault reconfiguration strategy that addresses both the fault ride-through capability and output uncertainty of DGs. The first stage introduces a rapid power restoration reconfiguration model that integrates network reconfiguration with fault ride-through, enabling DGs to provide power support to the distribution network during faults, thereby significantly improving the recovery rate of lost loads. An AdaBoost-enhanced decision tree algorithm is utilized to accelerate the computational process. The second stage proposes a post-recovery optimal reconfiguration model that uses fuzzy mathematics theory and the transformation of chance constraints to quantify the uncertainty of both generation and load, thereby improving the system’s static voltage stability index. Case studies using the IEEE 69-bus system and a real-world distribution network validate the effectiveness of the proposed strategy. This two-stage strategy facilitates short-term rapid load power restoration and enhances long-term operational stability, improving both the resilience and reliability of distribution networks with high DG penetration. The findings of this research contribute to enhancing the fault tolerance and operational efficiency of modern power systems, which is essential for integrating higher levels of renewable energy.

Streaming Queries: Enabling Real-Time Elastic Scaling in Modern Applications

The evolution of streaming queries has revolutionized how organizations handle real-time data processing and elastic scaling in modern computing environments. The shift from traditional batch processing to streaming architectures addresses critical challenges in resource allocation, latency reduction, and system performance optimization. By enabling continuous data processing and immediate response capabilities, streaming queries have transformed how businesses manage dynamic workloads across content delivery, e-commerce, and transportation sectors. The integration of AI-driven optimization and structured streaming techniques has established new benchmarks in processing efficiency, resource utilization, and fault tolerance, fundamentally changing how organizations approach real-time data analytics and decision-making.

Akka as a tool for modelling and managing a smartgrid systems

The article addresses big data processing challenges, fault tolerance, and consumer interaction in a smart grid network management system. It analyses the use of Akka for agent-based modelling of smart grid systems, which enables the creation of distributed, resilient systems that can efficiently scale and recover from failures. The article describes how the hierarchical structure of Akka actors can improve the management of disparate components within a smart grid system, allowing developers to create complex interaction and management models. The approach ensures multithreading and asynchronous operation, geographic distribution of management nodes, and enhanced system security, allowing the simulation of system hierarchy down to the level of the end device or user, which is crucial for overall system reliability.

Dual-Level Fault-Tolerant FPGA-Based Flexible Manufacturing System

This paper proposes a fault-tolerant flexible manufacturing system (FMS) that features a dual-level fault tolerance mechanism at both the workcell and system levels to enhance reliability. The workcell controller was implemented on a Field Programmable Gate Array (FPGA). Reconfigurable duplication was used as the first level of fault tolerance at the workcell level. It was shown how to detect and recover from FPGA faults such as Single Event Upsets (SEUs), hard faults, and Single Event Functional Interrupts (SEFIs). The prototype of the workcell controller was successfully implemented using two Zybo Z7-20 AMD boards and an Arduino DUE. Petri Nets were used to prove that controller reliability increased by 346% after 1440 operational hours. The second level of fault tolerance was at the FMS level; the Supervisor (SUP) took over the responsibilities of any malfunctioning workcell controller. Riverbed software was used to prove that the system successfully met the end-to-end delay requirements. Finally, Matlab showed that there is a further increase in performability.

Design of a Novel Nine-Phase Ferrite-Assisted Synchronous Reluctance Machine with Skewed Stator Slots

This paper proposes a novel nine-phase ferrite-assisted synchronous reluctance machine (FA-SynRM) featuring skewed stator slots to address challenges related to harmonic distortion, torque ripple, and material sustainability which are prevalent in conventional permanent magnet-assisted synchronous reluctance motors (PMa-SynRMs). Existing PMa-SynRMs often suffer from increased torque ripples and harmonic distortion, while reliance on rare-earth materials raises cost and sustainability concerns. To address these issues, the proposed design incorporates low-cost ferrite magnets embedded within the rotor flux barriers to achieve a flux-concentrated effect and enhanced torque production. The nine-phase winding configuration is utilized to improve fault tolerance, reduce harmonic distortion, and enable smoother torque output compared with conventional three-phase counterparts. In addition, the skewed stator slot design further minimizes harmonic components, reducing overall distortion. The proposed machine is validated through finite element analysis (FEA), and experimental verification is obtained by measuring the inductance characteristics and back-EMF of the nine-phase winding, confirming the feasibility of the electromagnetic design. The results demonstrate significant reductions in harmonic distortion and torque ripples, verifying the potential of this design.

ДОСЛІДЖЕННЯ УПРАВЛІННЯ НАДІЙНІСТЮ ТА ВІДМОВОСТІЙКІСТЮ В ІНФОКОМУНІКАЦІЙНИХ МЕРЕЖАХ: МОДЕЛЮВАННЯ ТА ТЕСТУВАННЯ ПРОТОКОЛІВ РЕЗЕРВУВАННЯ ШЛЮЗІВ ЗА ЗАМОВЧУВАННЯМ

The article is devoted to researching reliability and fault tolerance management mechanisms in infocommunication networks, focusing on modeling and testing of gateway redundancy protocols. The work considers the VRRP (Virtual Router Redundancy Protocol) and GLBP (Gateway Load Balancing Protocol) protocols, which ensure continuous network operation in the event of a primary gateway failure. The purpose of the study is to compare these protocols using the virtual environments EVE-NG and Containerlab, as well as to test them on physical and virtual devices. The article analyzes the characteristics and mechanisms of both protocols, the software implementation, and the configuration of the protocols on Cisco devices in emulation environments and tests their effectiveness in the face of gateway failures. The results showed that VRRP provides minimal packet loss and fast switching between master and backup gateways, making it optimal for networks with high requirements for connection continuity. Instead, GLBP provides load balancing between gateways but is accompanied by higher packet loss and longer switching times, which limits its use in critical networks. In particular, testing in virtual environments has shown that when using virtualized links and resources, VRRP delays and packet loss are significantly lower than GLBP. Therefore, testing in virtual environments is useful for preliminary analysis of configurations, although testing on real devices is necessary to determine the protocols' effectiveness accurately. We also consider the possibilities of using GLBP in large networks to provide not only redundancy but also optimize traffic balancing. Based on the results obtained, we formulate suggestions for future research, including optimizing switching times between gateways and developing hybrid solutions that combine the advantages of both protocols, VRRP and GLBP, to improve traffic management and redundancy and create more flexible and fault-tolerant infrastructures.

Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

Robust structural damage detection with deep multiple instance learning for sensor fault tolerance

Robust structural damage detection with deep multiple instance learning for sensor fault tolerance

Determining the superiority of a robust cloud fault tolerance mechanism using a spherical cubic fuzzy set-based decision approach

Determining the superiority of a robust cloud fault tolerance mechanism using a spherical cubic fuzzy set-based decision approach

Efficient fault tolerance and diagnosis mechanism for Network-on-Chips

Efficient fault tolerance and diagnosis mechanism for Network-on-Chips