Fault-tolerant Framework Research Articles

BAFT: bubble-aware fault-tolerant framework for distributed DNN training with hybrid parallelism

BAFT: bubble-aware fault-tolerant framework for distributed DNN training with hybrid parallelism

A fault tolerant node placement algorithm for WSNs and IoT networks

The operation of Internet of Things (IoT) Network and Wireless Sensor Networks (WSNs) can be often disrupted by a number of factors, such as node faults, security attacks, as well as path disconnections. Despite any disruptions or any failures of network components the functionality and performance of the network should remain unaltered. An approach that can assist in resolving the above issues is the use of mobile nodes. In this work, we present a Fault Tolerant Node Placement Algorithm (FTNPA) that utilizes mobile nodes for addressing failures in the network. We initially propose a Mobile Fault Tolerant (MobileFT) Framework, that supplies the two main functionalities of the Fault Tolerant Node Placement Algorithm (FTNPA), which are the detection and the recovery. Then, we present two variations of the FTNPA algorithm, the Decentralized FTNPA and the Centralized FTNPA. The first variation uses a decentralized detection method, where the detection is performed by the neighboring nodes in the network, as well as a local recovery method, where a mobile node is placed in a certain position to assist the affected area. Whereas, the second variation employs a centralized detection method, where the sink is responsible for the detection, and a recovery method that creates alternatives paths of mobile nodes towards a destination node. Simulation results show that the proposed algorithms can significantly contribute to the detection and recovery of faults in IoT Networks and WSNs.

Fault-Tolerant Operation of Bosonic Qubits with Discrete-Variable Ancillae

Fault-tolerant quantum computation with bosonic qubits often necessitates the use of noisy discrete-variable ancillae. In this work, we establish a comprehensive and practical fault-tolerance framework for such a hybrid system and synthesize it with fault-tolerant protocols by combining bosonic quantum error correction (QEC) and advanced quantum control techniques. We introduce essential building blocks of error-corrected gadgets by leveraging ancilla-assisted bosonic operations using a generalized variant of path-independent quantum control. Using these building blocks, we construct a universal set of error-corrected gadgets that tolerate a single-photon loss and an arbitrary ancilla fault for four-legged cat qubits. Notably, our construction requires only dispersive coupling between bosonic modes and ancillae, as well as beam-splitter coupling between bosonic modes, both of which have been experimentally demonstrated with strong strengths and high accuracy. Moreover, each error-corrected bosonic qubit is comprised of only a single bosonic mode and a three-level ancilla, featuring the hardware efficiency of bosonic QEC in the full fault-tolerant setting. We numerically demonstrate the feasibility of our schemes using current experimental parameters in the circuit-QED platform. Finally, we present a hardware-efficient architecture for fault-tolerant quantum computing by concatenating the four-legged cat qubits with an outer qubit code utilizing only beam-splitter couplings. Our estimates suggest that the overall noise threshold can be reached using existing hardware. These developed fault-tolerant schemes extend beyond their applicability to four-legged cat qubits and can be adapted for other rotation-symmetrical codes, offering a promising avenue toward scalable and robust quantum computation with bosonic qubits. Published by the American Physical Society 2024

Ada-FA: A Comprehensive Framework for Adaptive Fault Tolerance and Aging Mitigation in FPGAs

Ada-FA: A Comprehensive Framework for Adaptive Fault Tolerance and Aging Mitigation in FPGAs

Enhancing Global Blockchain Privacy via a Digital Mutual Trust Mechanism

Blockchain technology, initially developed as a decentralized and transparent mechanism for recording transactions, faces significant privacy challenges due to its inherent transparency, exposing sensitive transaction data to all network participants. This study proposes a blockchain privacy protection algorithm that employs a digital mutual trust mechanism integrated with advanced cryptographic techniques to enhance privacy and security in blockchain transactions. The contribution includes the development of a new dynamic Byzantine consensus algorithm within the Practical Byzantine Fault Tolerance framework, incorporating an authorization mechanism from the reputation model and a proof consensus algorithm for robust digital mutual trust. Additionally, the refinement of homomorphic cryptography using the approximate greatest common divisor technique optimizes the encryption process to support complex operations securely. The integration of a smart contract system facilitates automatic and private transaction execution across the blockchain network. Experimental evidence demonstrates the superior performance of the algorithm in handling privacy requests and transaction receipts with reduced delays and increased accuracy, marking a significant improvement over existing methods.

IDify - Distributed Database System for Digital Identification

”IDify” Distributed database system for digital identification is a big data handling system to centralize the important services provided by the government to make the facilities efficient and reliable. It has integrated services such as digital authentication, E-Banking, Personal Information Management, and Government Surveys to provide users with a centralized and digitized platform to make their lives easier. In this system there is a distributed database platform to carry out all the data storing and data processing tasks to facilitate a large user count and to avoid the drawbacks of monolithic systems. The database system has been designed by integrating several modern database technologies to make the system efficient and coherent as the facilities provided by the system are extremely mission-critical. To make the system low latent on data ingestion and data reading tasks a special library is also implemented in collaboration with modern query processing engines. It is proposed to utilize the thread system of a processor to implement parallelism to enhance the performance of data read/write tasks. Apart from that a fault-tolerant framework has been implemented to free the system from any erroneous scenario taking place.

Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators.

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

Utilizing Flink and Kafka Technologies for Real-Time Data Processing: A Case Study

In today's very competitive business world, being able to use data to its fullest in real time has become a key differentiation. This paper looks at how two cutting-edge technologies, Apache Flink and Apache Kafka, work together and how they are changing the way real-time data is processed and analyzed. With its fault-tolerant framework made for collecting data from many sources, Apache Kafka is a leader in reliability and scalability when it comes to ingesting data. Apache Flink is the perfect partner for Kafka because it is great at stream processing and low-latency event handling. This paper carefully explains how these technologies work together to create a complete set of tools for handling and analyzing data in real time. The paper goes into detail about how Flink and Kafka can work together, showing how data streams can be handled and intelligently put together to produce insights that can be used. This set of tools, which was created after a lot of study and real-world experience, helps organizations that want to start using real-time data in new ways. Evaluations of performance, scalability, and real-world applications show that this integrated method has a real effect. Beyond just talking about ideas, this study paper gives organizations a step-by-step plan for how to use real-time data to improve their decision-making. By taking advantage of how well Flink and Kafka work together, companies can become more flexible, quick to respond, and creative.

Smart Traffic Navigation System for Fault-Tolerant Edge Computing of Internet of Vehicle in Intelligent Transportation Gateway

To investigate the diversified technologies in Internet of Vehicles (IoVs) under intelligent edge computing, brain-inspired computing techniques are proposed in this study, which is a promising biologically inspired method by using brain cognition mechanism for various applications. A neuromorphic approach in a scalable and fault-tolerant framework is presented, targeting to realize the navigation function for the edge computing in IoV applications. A novel fault-tolerant address event representation approach is proposed for the spike information routing, which makes the presented model both scalable and fault-tolerant. Experimental results reveal that the proposed approaches can enhance the communication distance, the load balancing and the maximum throughput of the neuromorphic system accordingly. Based on the proposed neuromorphic model, the effects of the dopamine level are investigated. Besides, the results show that the proposed work can realize the accurate obstacle avoidance for the edge IoV computing, and the performance of the proposed network is superior to the network without the proposed scalable and fault-tolerant design. Therefore, the proposed IoV model provides an experimental basis for the improvement of the IoV system.

M2STaR: A Multimode Spatio-Temporal Redundancy Design for Fault-Tolerant Coarse-Grained Reconfigurable Architectures

Coarse-grained reconfigurable architectures (CGRAs) can provide both energy efficiency and performance for embedded systems, and thus they are increasingly deployed in the areas of aerospace, automotive engineering, and security where reliability is also a main criterion. However, the state-of-the-art fault-tolerant strategies for CGRAs apply either temporal or spatial scheme, including redundancy, periodic detection, workload balancing, and reconfiguration, failing to exploit the feature of dynamic and partial reconfiguration of CGRAs. Also, vulnerable judging circuits and inflexible mode shifting bottleneck the reliability design of fault-tolerant CGRAs. This paper proposes a novel multi-mode fault-tolerant framework for CGRAs, which combines spatial-redundant data-paths with temporal-redundant voters and thus reduces the vulnerable judging circuits while balancing the performance and reliability. This framework can also enable a changing reliability level at runtime via an online configuration transformation method based on pre-compiled patterns. Within the proposed framework, we systematically searched the design space spanning various combinations of the mainstream schemes with a Markov process model to compare the effectiveness and accordingly selected five points as available modes in our design after comprehensive consideration of fault tolerance and time overhead on CGRA. The framework is comprehensively evaluated on a cycle-accurate CGRA simulator, considering both permanent and transient faults. The experimental results show that the fault coverage rate of single transient faults or permanent faults has increased from 71.74% to 93.84%, which means the fault tolerance of the system has been increased by 31.03% compared with the state-of-the-art methods. There is also a great improvement in mean-time-to-failure (MTTF) and reconfiguration latency over baseline designs.

Robust adaptive quadrotor position tracking control for uncertain and fault conditions

The combination of nonlinear indirect and direct adaptive control with an active fault-tolerant framework is proposed in this paper for quadrotor position tracking control under uncertain and fault conditions. An inner loop direct model reference adaptive controller generates the required force while moving along the reference trajectory, while nonlinear indirect adaptive controller maintains the required attitude angles. Furthermore, for uncertain conditions, our proposed framework provides significantly stable characteristics. Otherwise, when a fault occurs at actuators or sensors, the quadrotor vehicle cannot guarantee global asymptotic stability. This study contributes to an active fault-tolerant control strategy for solving the complex position tracking problem using the adaptive two-stage Kalman filter (ATSKF). Based on the fault information, a fault compensation term is added to the control law to improve convergence and system robustness in the presence of uncertain and fault conditions. Finally, simulation results show satisfactory performance for quadrotor vehicle position tracking, even with actuator faults and uncertainties.

Novel Particle Bee Optimisation based Framework for Fault Tolerance in Fog Environment

Fog Computing characteristics have fascinated its services in many applicability areas such as Industrial IoT, health monitoring systems and high performance computing, weather forecasting, agronomy, etc. Here, data is captured using the various sensor nodes installed at different locations and further processed by a cloud-based server to obtain the desired results. Heterogeneity of fog nodes and end devices in fog computing architecture is a major feature that helps to utilize already existing computing resources, yet it complicates the managing process. Such a diversified environment must be considered when designing fog environments in terms of computing capabilities, power consumption, and connectivity. In this work An efficient Novel Particle Bee Optimization based Fault Tolerance framework has been projected that ensures the implementation of Fog computing in real time. The proposed framework handling live server faults and also diminishes latency and bandwidth usage of the network and improves Quality of Service (QoS). The achieved results for the projected technique represent the reduction of network bandwidth and reduction in overall cost.

Effective Information Retrieval Framework for Twitter Data Analytics

The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The contribution of this article is to touch upon mentioned challenges by presenting a climbable, quick and fault tolerant framework to process real-time data to extract hidden insights. This framework is versatile enough to support batch processing along with real time data streams in parallel and distributed environment. Experimental analysis of proposed framework on twitter posts concludes it as quicker, robust, fault tolerant, and comparatively more accurate with traditional approaches.

Retracted: SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Retracted: SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Wireless Distributed Consensus for Connected Autonomous Systems

Connected critical autonomous systems (C-CAS) are envisioned to significantly change our life and work styles through emerging vertical applications such as autonomous vehicles and cooperative robots. However, as the scale of the connected nodes continues to grow, their heterogeneity and cyber-security threats are more eminent, and conventional centralized communications and decision-making methodology are reaching their limit. This paper is the first exploration of a trustworthy and fault-tolerant framework for C-CAS for achieving hyper-reliable global decision-making in a trustless environment, where the connected sensors/nodes are less reliable due to either communication failure or local decision error (e.g., by sensing algorithm/AI, etc.). The proposed framework is based on two iconic distributed consensus (DC) mechanisms, practical Byzantine fault tolerance (PBFT) and Raft, under the proposed PICA (Perception-Initiative-Consensus-Action) protocol with wireless connections among the nodes. We first analytically derived consensus reliability in six different system models. The other fundamental performance metrics such as the consensus throughput and latency, node scalability and reliability gain are also analytically derived. These analytical results provide basic design guidelines for wireless Distributed Consensus (WDC) usage in the C-CAS systems. The results show that WDC significantly improves overall system reliability with the increasing number of participating nodes.

An active fault tolerance framework for uncertain nonlinear high-order fully-actuated systems

This paper presents a novel active fault tolerance framework for uncertain high-order fully-actuated systems (HOFASs). Starting from the uncertain faulty HOFAS model with nonlinear measurement, the advantages of this description are revealed via Lie Derivatives. A HOFAS adaptive observer and controller integration is established and further a closed-loop HOFAS dynamic data modeling structure is derived. This structure uncovers the dynamic characteristics including fault features, and naturally yields a fault detectability condition. Based on the fault information from the dynamic data model, an active fault tolerant control strategy for HOFASs is proposed, which enriches HOFAS control theory. The uniformly bounded stability of the HOFAS model is proved theoretically and illustrated experimentally.

Online Fault Diagnosis and Tolerance Based on Multiexpert Joint Belief Rule Base for Sensor Failures of Vehicles

As sensors readings are used for vehicle flight control, their reliability directly affects the flight performance. This paper develops a new fault diagnosis and tolerance method for sensor failures of vehicles by addressing three problems:unavailable faulty data, difficulty in establishing analytical system, and inconsistence of expert cognitive ability. For the purpose, a new belief rule base model with multi-expert joint (BRB-ME) is proposed. The first two problems are handled by combining the small size of observation data and the uncertain knowledge from multiple experts in BRB-ME. For the third problem, a new multi-expert joint strategy is proposed in the BRB-ME model. The experts first construct their own models, and then the models are fused with different weights according to the experts’ ability, such as research fields, the working time and etc. Then, a new fault diagnosis and tolerance framework is developed based on BRB-ME for detecting vehicles’ sensor failures, where the sensor failures are tolerated by the reconstruction strategy for faulty sensor output. Moreover, in order to address the influence of the uncertain expert knowledge, an optimization model is constructed for obtaining the optimal solutions for the framework. An experimental illustration is conducted for accelerometer failure. The diagnosis accuracy is 97.50%, and the developed framework can ensure the navigation accuracy of the vehicle under accelerometer failure.

Fault-Tolerant Framework for State-of-Health Prediction of Lithium Batteries

Accurately predicting the state of health (SoH) of lithium-ion batteries remains one of the top challenges, especially in uncertain environments where feature availability is not guaranteed. The main contribution of this article is to propose a highly available feature fault-tolerant prediction framework capable of predicting the SoH of batteries with a very high accuracy under sensor data unavailability. Previously published studies mitigated the feature incompleteness issue using models that deeply depend on a specific set of features; if one of the features becomes unavailable, the prediction model fails. Unlike the existing methods, the proposed feature fault-tolerant prediction framework proposes a novel agile approach so that when one of the features is missing, the model considers the next available feature to make the prediction. Our fault-tolerant prediction framework is highly available for SoH prediction and always predicts at a very high accuracy level. A hybrid deep learning model combining long short-term memory and deep neural networks along with a meticulous feature engineering design is used to train the NASA dataset and predict the SoH of batteries with a high precision. In order to prove the efficiency of the model, the feature fault-tolerant prediction framework configuration has been validated against two other datasets: Toyota Research Institute in partnership with MIT and Stanford dataset and NASA dynamic profile datasets. The performance is evaluated using the error indicators: mean absolute error, root-mean-square error, 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">$R$</tex-math></inline-formula> square.

A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems

This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework.

Fault-Tolerant Relay Node Placement in Wireless Sensor Networks for Surveillance of Overhead Transmission Lines

Wireless sensor networks provide a promising technology to surveil overhead transmission lines in smart grid communication. However, a great challenge for researchers is proposed by adverse outdoor environments and essential requirements of strong as well as flexible smart grid communication. Specifically, nearly linear topology, limited energy, and tolerance for faults are entangled to make the surveillance system complex. Because of the need for deep understanding of system, significant efforts have been made in the past few years. We have proposed a fault-tolerance framework for surveillance of overhead transmission lines. In this paper, we follow the framework and further explore deployment problems systematically. Firstly, we present a fault-tolerance placement model. After analyzing the model, we identify the optimal placement for fault tolerance and then propose a placement algorithm with optimal deployment. An extensive experiment highlights some useful observations for design of the fault-tolerance system and demonstrates efficient fault tolerance for relay nodes. Numerical results show that the fault-tolerance ratio is improved by at least 6 % compared to that of previous algorithms.