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Summary: The need to provide services closer to the end‐user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo‐distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo‐separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software‐defined networking (SDN) gained much attention from academia, researchers, and industrial sectors. Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal. [ABSTRACT FROM AUTHOR]

This paper proposes a credit-driven practical byzantine fault tolerance based on a consensus algorithm for sustainable 6G communication to address the potential threat of malicious activity in green and sustainable 6G-based Industrial Internet of Things (IIoT) applications. The dual-layer architecture allows for simultaneous verification of transactions and the execution of read-write operations. According to simulation studies, CD-PBFT enhances algorithm performance and stability while efficiently reducing validation time. Empirical findings demonstrate that CD-PBFT achieves both efficiency and security fault tolerance by reducing network transaction latency by an average of 34.8% and increasing throughput by 25.2% compared to PBFT. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT)-based smart grid employs IoT smart devices which gather private data about electricity from consumers and distribute data to service providers via an open network, causing new security issues. In order to overcome these restrictions, an efficient chaotic MHT-PUF based IoT device authentication with QPBFT for smart grid infrastructure has been proposed. The IoT devices from the manufacturers are initially registered to cloud based on the PUF mechanism by generating challenge–response pair, and these responses are stored in blockchain for authentication. Following IoT device authentication, data transmission depends on the chaotic Merkle hash tree cryptography to generate the key and encryption of message. Next, the data will be securely transacted to the blockchain by utilizing the QPBFT consensus algorithm to store the encrypted data. Consensus is a technique used in blockchain to ensure that all nodes concur on the network's current state and the authenticity of transactions. The model that has been created is assessed using the program called Python. The evaluation performances of the proposed method are 0.0209 ms of encryption time, 0.0851 ms of decryption time, 0.1899 ms of block generation time, 1.41 ms of key generation time, 480.92 kbps of throughput, 0.1013 ms of transaction delay, 6% of packet loss and 94% of PDR. Thus, the proposed model with an efficient chaotic MHT-PUF based IoT device authentication with QPBFT operates well for effectively storing and securing data in smart grid infrastructure. [ABSTRACT FROM AUTHOR]

The existence of large transition resistance in high-voltage transmission lines easily causes the distance protection to refuse to act or to act incorrectly, which brings great challenges to the safe and stable operation of the power system. To eliminate the effect of transition resistance on distance protection, a single-phase ground distance protection scheme based on the compensated cosine volume is proposed. Firstly, the expression of cosine angle compensation is given by analyzing fault point voltage and voltage cosine volume at protection. Second, the voltage cosine volume ratio when a fault occurs to the boundary value of the voltage cosine volume during normal operation is calculated. The angle between the voltage cosine volume and fault voltage is used to construct the compensation initiation factor. Then, the compensation quantity is solved by multiplying the ratio, the compensation initiation factor, and the line impedance angle. Finally, the compensation quantity is substituted into the cosine angle compensation expression to compensate for the cosine volume and construct a novel distance protection criterion. Matlab/Simulink is used to verify the proposed scheme is not affected by transition resistance, fault location, and operating conditions, and can accurately identify single-phase ground faults with high tolerance to transition resistance. [ABSTRACT FROM AUTHOR]

In view of the problems such as too many times of rejected tasks and waste of resources in fault - tolerant fair scheduling FT-FS(Fault Tolerate Fair Scheduler) algorithm, a new fair scheduling algorithm PA - FTFS (Primary Alternate and Fault Tolerant Fair Scheduling) is proposed based on FT-FS algorithm by integrating the main/alternative PA (Primary Alternate) fault-tolerant strategy. This algorithm can reduce the number of rejected tasks in multi core processor system and reduce the waste of resources. MATLAB simulation software is used to verify the above algorithms. The experimental results show that PA-FTFS algorithm using proportional replacement task strategy has significantly reduced the number of task rejection times compared with FT - FS algorithm after permanent kernel failure. Data analysis shows that PA-FTFS algorithm has better failure rate and scheduling rate than FT-FS algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the simultaneous fault-tolerant control and disturbance rejection of impulsive switched linear systems. First, estimates of faults and disturbances in the system can be obtained by designing observers. Then, through the obtained estimation, the controller with anti-interference performance and fault tolerance performance is designed. Under the arbitrary switching rules and the minimum dwell time switching rules, the stability of the system is guaranteed. Finally, the effectiveness of the method is verified by numerical simulation and example simulation. [ABSTRACT FROM AUTHOR]

We here address problems of nonunique paths and poor fault tolerance in the traditional shortest path transfer method and propose an overall adjustment method of relative corrections of master reference stations in BDS/GNSS Network RTK systems. Our method comprehensively considers the vector relationship of baseline error corrections, the baseline closure error of Delaunay triangulation and the interpolation relationship between stations to realize the one-time generation, obtaining all relative error corrections of the master reference station in a network RTK system. The test results showed that our method can achieve a success rate of more than 95% and an incorrect rate of less than 0.01%. The error corrections, generated by the two methods, are verified and analysed by the positioning accuracy of the rover. The results show that the positioning accuracy of the method in this paper is improved by about 17% over the shortest path transfer method. [ABSTRACT FROM AUTHOR]

Due to the significant characteristics of decentralization and tamper resistance of blockchain technology, it helps to solve the problems of timeliness and slow permission control in traditional information sharing. However, most researchers are committed to exploring how to improve the efficiency, reliability, and privacy security of digital twin data. There is a lack of research on issues such as blockchain participants free-riding and not actively participating in information sharing, and how to promote information sharing through incentives. Therefore, this paper designs a distance grouping based practical byzantine fault tolerance consensus algorithm to address the credibility issue of transmitted data, thereby improving the efficiency of negotiation. The feasibility of the algorithm is verified through cases analysis. In addition, for the problem of low subjective willingness of information sharing, this paper designs an information sharing incentive strategy based on blockchain intelligent contracts, establishes digital twin data sharing evolutionary game models with reputation incentives and non-reputation incentives, so as to explore the impact of reputation incentive on evolutionary stability strategies. Finally, simulation analysis is conducted on the evolutionary game model of digital twin data sharing. The impact of several key indicators such as data complementarity, trust, positive incentive coefficient, and data sharing cost on the evolution process of information sharing are considered, and corresponding management insights are obtained. [ABSTRACT FROM AUTHOR]

The coupled relationship between inputs and outputs in multiple-input multiple-output (MIMO) systems, as well as the multiplicative uncertainties caused by multiplicative faults, increases the complexity of fault diagnosis (FD) and fault-tolerant control (FTC). Research has indicated that coprime factor uncertainties are suitable for modeling multiplicative uncertainties. This paper presents an FD and FTC strategy for MIMO systems based on the ν -gap metric technique within the coprime factorization framework. In the offline phase, the ν -gap metric-based hierarchical clustering method is designed to classify fault samples. Next, core systems and boundary systems are calculated for each fault category, and corresponding residual compensation controllers are designed. In the online phase, by computing the relevant ν -gap metric values, the fault severity of the real-time system is determined, and the core system with similar dynamic behaviors is identified. This FD result drives the switching of residual compensation controller, achieving FTC and ensuring system stability and robustness. This strategy eliminates the need for online solving of fault-tolerant controller, saving computational resources. Finally, the ν -gap metric-based FD and FTC strategy is validated with simulations on a three-phase voltage source inverter system. • Under the coprime factorization framework, FD and FTC strategy is presented. The Youla parameterization method is employed to parameterize the closed-loop system, resulting in a residual compensation controller. Fault tolerance control is realized by residual compensation controller. • By combining online and offline mechanisms, the results of FD directly drive the switching of fault-tolerant controllers, thereby reducing the online computational requirements and enhancing the timeliness of FD and FTC. • The v -gap metric-based hierarchical clustering algorithm is presented to identify faulty systems with similar dynamic performance. This approach effectively avoids redundancy in fault-tolerant controller design in situations with diverse faults. [ABSTRACT FROM AUTHOR]

Due to the dynamic change of the network topology and no fixed structure of the Internet of Vehicles, it is difficult to maintain a stable end-to-end connection, so it needs the routing cooperation between vehicles. However, due to the packet loss of selfish nodes und the influence of malicious nodes tampering with information, existing Internet of Vehicles routing cooperation with blockchain lacks effective cooperative incentive mecha nism and high data security guarantee. 1n order to solve this problem, this paper proposes a blockchain-based security cooperation communication scheme for Internet of Vehicles, and designs the detailed workflow of this scheme. In the workflow, a route cooperation scheme based on improved credit value and link lifetime algorithm, an electronic money incentive mechanism based on vehicle type and message type, and a practical byzantine fault tolerance consensus mechanism based on credit improvement (PBFT-CI) are designed for Internet of Vehicles. Analysis results show that the electronic money incentive mechanism can motivate vehicles to participate in cooperation and improve the security of information transmission. PBFT-CI can motivate road side units to participate in consensus and improve the security of the system. Simulation results show that when the number of vehicles is 100 and the number of selfish vehicles is 5, the proposed route cooperation scheme improves the delivery success rate by 0.27 when compared with greedy perimeter stateless routing (GPSR). In addition, the transaction throughput of PBFT-CI is about 1.72 times of that of practical byzantine fault tolerance (PBFT) consensus mechanism. [ABSTRACT FROM AUTHOR]

This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks (SD-WANs), with a particular emphasis on the multi-objective Controller Placement Problem (CPP). As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management, the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge. This review delves into recent innovations in multi-objective controller placement strategies, including clustering techniques, heuristic-based approaches, and the integration of machine learning and deep learning models. Each methodology is critically evaluated in terms of its ability to minimize network latency, enhance fault tolerance, and improve overall network performance. Furthermore, this paper discusses the inherent limitations and challenges associated with these techniques, providing a critical evaluation of their current utility and outlining potential avenues for future research. By offering a thorough overview of state-of-the-art approaches to multi-objective controller placement in SD-WANs, this review aims to inform ongoing advancements and highlight emerging research opportunities in this evolving field. [ABSTRACT FROM AUTHOR]

The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related to power consumption and system reliability. To address the challenges of latency, energy consumption, and fault tolerance in these environments, this paper proposes a latency-aware, fault-tolerant framework for resource scheduling and data management, referred to as the FORD framework, for smart cities in fog environments. This framework is designed to meet the demands of time-sensitive applications, such as those in smart transportation systems. The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments, leveraging resources from both fog and cloud environments. Through simulation-based executions, tasks are allocated to the nearest available nodes with minimum latency. In the event of execution failure, a fault-tolerant mechanism is employed to ensure the successful completion of tasks. Upon successful execution, data is efficiently stored in the cloud data center, ensuring data integrity and reliability within the smart city ecosystem. [ABSTRACT FROM AUTHOR]

Theft of electricity is an increasingly common problem that has a detrimental effect on utility providers as well as power consumers. It affects consumers’ high energy costs, creates electric risks, and affects the electric utility industry’s capacity to grow economically. The biggest issue facing smart grids is the theft of electricity. In order to monitor loads and regulate energy usage, smart meters (SMs) are installed at the end of customers’ residences and are used to periodically transmit fine-grained power consumption measurements to the supplier. Due to nonlinear correlations and missing values, existing theft detection techniques have difficulty processing large electrical usage information. An integrated system for coordinating the examination of electrical demand data is also absent. To address these limitations in this research we propose a new electricity theft detection framework to further enhance the system reliability and fault tolerance. Initially, we utilize the quantum key distribution (QKD) with the rolling optimization strategy (ROS) to reduce the computational overhead and efficiently minimize the fluctuations. After that extreme gradient boosting (EGB) and coati optimization algorithm (COA) are employed for classification. Finally, we proposed privacy functionality trade-off strategies for smart meters to improve the consumer trust and confidence in the smart grid system. Thus, the proposed new electricity theft detection framework to further enhance the system reliability and fault tolerance method’s simulation in Matlab-R2023a\Simulink demonstrates superior performance compared to existing techniques. The approaches in this study to overcome the issues faced in previous technologies are tested using different metrics of authentication rate at 98(%), accuracy at 99 (%), detection rate at 98.6 (%), precision at (97%), recall at (98.6%), at F1-score (95.15%), and AUC at (0.3123). [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained. This research proposes an extra level of security to Federated Q-learning by merging Blockchain technology with VANETs. Initially, traffic data is encrypted utilizing the Extended Elliptic Curve Cryptography (EX-ECC) technique to enhance the security of data. Then, the Federated Q-learning model trains the data and ensures higher privacy protection. Moreover, interplanetary file system (IPFS) technology allows Blockchain storage to improve the security of VANETs information. Additionally, the validation process of the proposed Blockchain framework is performed by utilizing a Delegated Practical Byzantine Fault Tolerance (DPBFT) based consensus algorithm. The proposed approach to federated Q-learning offered by Blockchain technology has the potential to develop VANET safety and performance. Comprehensive simulation tests are performed with several assessment criteria considered for number of vehicles 100, Throughput (102465.8 KB/s), Communication overhead (360.57 Mb), Average Latency (864.425 ms), Communication Time (19.51 s), Encryption time (0.98 ms), Decryption time (1.97 ms), Consensus delay (50 ms) and Validation delay (1.68 ms), respectively. As a result, the proposed approach performs significantly better than the existing approaches. [ABSTRACT FROM AUTHOR]

Consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system's security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences. Instead, the proposal to reach consensus is directly decided by specific nodes, with other nodes merely verifying the proposal's validity, which can easily result in monopolistic or dictatorial outcomes. In response, we proposed the Aggregating Preferences with Practical Byzantine Fault Tolerance (AP-PBFT) consensus algorithm, which allows nodes to express preferences for multiple proposals. AP-PBFT ensures the validity of consensus results through a consensus output protocol, and incentivizes nodes to act honestly during the consensus process by incentive mechanism. First, AP-PBFT leverages Verifiable Random Function to select both consensus nodes and a primary node from the candidates. The primary node gathers proposals, assembles them into a proposal package, and broadcasts it to other consensus nodes. The consensus nodes independently vote to express their preferences for different proposals in the package, execute the consensus output protocol to reach local consensus, and the primary node aggregates these results to form the global consensus. Once the global consensus is finalized, AP-PBFT evaluates node behavior based on the consensus output protocol, penalizes nodes that acted maliciously, and rewards those that adhered to the protocol. Additionally, nodes can interact and adopt different strategies while executing the consensus output protocol, which can influence the consensus outcome. Therefore, we established an evolutionary game model based on hypergraph to analyze these interactions. Theoretical analysis shows that the incentive mechanism in AP-PBFT effectively encourages nodes to honestly follow the consensus output protocol, ensuring that AP-PBFT satisfies the properties of consistency, validity, and termination. Finally, the simulation results demonstrate that the AP-PBFT algorithm possesses good scalability and the capability to handle dynamic changes in nodes, surpassing some mainstream consensus algorithms in terms of transaction throughput and consensus achievement time. Moreover, AP-PBFT can incentivize honest behavior among consensus nodes, thereby enhancing the reliability of consensus and strengthening the security of the network. [ABSTRACT FROM AUTHOR]

Wearable robots are often powered by elastic actuators, which can mimic the intrinsic compliance observed in human joints, contributing to safe and seamless interaction. However, due to their increased complexity, when compared to direct drives, elastic actuators are susceptible to faults, which pose significant challenges, potentially compromising user experience and safety during interaction. In this article, we developed a fault-tolerant control strategy for torque assistance in a knee exoskeleton and investigated user experience during a walking task while emulating faults. We implemented and evaluated the torque control scheme, based on impedance control, for a mechanically adjustable compliance actuator with nonlinear torque-deflection characteristics. Conducted functional evaluation experiments showed that the control strategy is capable of providing support during gait based on a torque profile. A user study was conducted to evaluate the impact of fault severity and compensation on the perception of support, stiffness, comfort, and trust while walking with the exoskeleton. Results from the user study revealed significant differences in participants' responses when comparing support and stiffness levels without fault compensation. In contrast, no significant differences were found when faults were compensated, indicating that fault tolerance can be achieved in practice. Meanwhile, comfort and trust measurements do not seem to depend directly on torque support levels, pointing to other influencing factors that could be considered in future research. [ABSTRACT FROM AUTHOR]

The Software‐Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi‐controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in‐packet request‐response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration‐in and migration‐out domains by selecting a subset of switches. The architecture utilizes online Q‐learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real‐world applications. [ABSTRACT FROM AUTHOR]

Summary: The precise diagnosis and effective treatment of patients rely heavily on healthcare data. However, sharing healthcare information can be challenging due to the potential risks of unauthorized tampering and data leakage. To address these concerns and facilitate secure and efficient data access for stakeholders within and outside the healthcare system, this study introduces a patient‐enabled blockchain system (PEBS). Patient‐enabled blockchain system uses the Model View Controller (MVC) approach where the model manages the off‐chain and on‐chain data, the view is the user‐accessible module, and the controller acts as an interface between a user interface and storage layer. It enables patients to control their data by determining specific access permissions and executes various smart contracts for stakeholders' registration, authorization, data storage, query, and update operations. Patient‐enabled blockchain system incorporates Modified Proof‐of‐Authority (MPoA), which has been compared against various consensus algorithms such as Proof‐of‐Work (PoW), Proof‐of‐Authority (PoA), and Istanbul Byzantine Fault Tolerance (IBFT). Furthermore, the suggested system incorporates the utilization of the Interplanetary File System (IPFS) to address concerns related to performance and storage. We conducted an in‐depth analysis and comparison of the system's performance using key parameters such as transaction latency and throughput. Experiments are carried out using network sizes of 10 and 30, with transaction counts from 5 to 500. The experiments show that the highest latency for the proposed system is 58,105 ms, almost 4.8 times less than PoW, which is 283,575 and provides 2.7 times higher throughput (101 transactions per second) than PoW (38 transactions per second). [ABSTRACT FROM AUTHOR]

This paper investigates the effects of winding configurations on force density and fault tolerance in linear primary permanent magnet vernier (LPPMV) machines. Firstly, the LPPMV machines with integral slot distributed windings (ISDWs) and fractional slot concentrated windings (FSCWs) are discussed. Due to the high modulation ratio of ISDW machine, it has the potential to achieve higher thrust force capabilities. Then, the operation principle of the LPPMV machines is analyzed from the perspective of air-gap magnetic modulation. Furthermore, it should be noted that the winding configurations of ISDW machine has larger spans, resulting in insufficient fault-tolerance. To solve this limitation, a new modular ISDW LPPMV machine was proposed and optimized. In the modular ISDW LPPMV machine, a 3 × 3-phase winding configuration is employed. It is demonstrated that modular ISDW LPPMV machines exhibit superior characteristics in both thrust density and fault tolerance. Finally, the experiments are carried out in a linear test bench, verifying the theoretical analysis. [ABSTRACT FROM AUTHOR]

Currently, cloud computing is increasing on a daily basis and has evolved into an efficient and flexible paradigm for addressing large‐scale issues. It is recognized as an internet‐based computing model where various cloud users share computing and virtual resources such as services, applications, storage, servers and networks. In the present study, we propose an innovative strategy for enhancing the fault tolerance and load balancing capabilities of cloud computing environments: we combined graph neural networks (GNNs) with dynamic multiqueue optimization scheduling (DMQOS). The present study uses GNNs and DMQOS to provide a novel solution to these challenges. GNN–DMQS uses a DMQOS system that adjusts to the dynamic nature of cloud workloads. This dynamic method develops response times and resource consumption, which improve load balancing and system effectiveness. Using GNNs to predict and mitigate probable faults grows fault tolerance and safeguards service accessibility. We evaluate the proposed method, GNN–DMQOS, using extensive experiments on real‐world cloud computing datasets. The results demonstrate significant developments: 95.66% in fault tolerance, 97.13% in adaptability, 1598.14 kbps in throughput, 94.78% in resource utilization, 96.77% in reliability, 2.876 ms in response time, 0.141 s in network lifetime, 1.627 s in end‐to‐end delay and 129.34 ms in time complexity compared with traditional methods. In addition, our method, GNN–DMQOS, exhibits adaptability to varying workloads, making it suitable for dynamic cloud environments. [ABSTRACT FROM AUTHOR]

Blockchain technology can be used in the IoT to ensure the data privacy collected by sensors. In blockchain systems, consensus mechanisms are a key technology for maintaining data consistency and correctness. Among the various consensus protocols, asynchronous Byzantine consensus protocols offer strong robustness as they do not rely on any network timing assumptions during design. As a result, these protocols have become a research hotspot in the field of blockchain. Based on different structural design approaches, asynchronous Byzantine consensus protocols can be divided into two categories: protocols based on the DAG structure and protocols based on the ACS structure. The paper describes their principles and summarizes the related research works. The advantages and disadvantages of the protocols are also compared and analyzed. At the end of the paper, future research directions are identified. [ABSTRACT FROM AUTHOR]

Summary: Dragonfly topology is a commonly utilized design for interconnection networks in parallel and distributed systems. A classical dragonfly can be denoted as dragonfly(k$$ k $$,m$$ m $$,l$$ l $$), wherem$$ m $$ is the number of routers in a group,l$$ l $$ is the number of links per router connected to other groups, andk$$ k $$ is the number of links per router connected to compute nodes. Each router has otherm−1$$ m-1 $$ links fully connected to otherm−1$$ m-1 $$ routers within a group. Each group hasml$$ ml $$ links connected to other groups. The groups are also fully connected, therefore there areml+1$$ ml+1 $$ groups in total. The router radix in a dragonfly(k$$ k $$,m$$ m $$,l$$ l $$) isl+k+m−1$$ l+k+m-1 $$. Building a large dragonfly system requires a large number of high‐radix routers, increasing hardware costs. To reduce hardware costs, this paper proposes a more flexible topology called dimension‐extended dragonfly (DED). Rather than routers in a group being fully connected, each router in a group is arranged in ann$$ n $$‐dimensional matrix, and routers of the same dimension are fully connected. We usen$$ n $$ to denote the dimension such that each group in the DED hasmn$$ {m}^n $$ routers. This study comprehensively evaluates DED in terms of cost, performance, fault tolerance, and packet latency. The findings show that DED provides a more economical hardware solution compared to traditional Dragonfly and Cascade topologies, especially forn≥3$$ n\ge 3 $$. Beyond cost‐efficiency, DED enhances system design flexibility. It offers diverse possibilities for system scaling through different combinations of diameter and radix, giving system architects more adaptable options. To further enhance the versatility of DED, three disjoint path routing algorithms are proposed and their fault tolerance is evaluated through simulation. The simulation results also show that the packet latency of DED is lower than dragonfly and cascade. [ABSTRACT FROM AUTHOR]

Summary: Wireless Sensor Networks (WSNs) are critical for communication within a mile radius and industrial applications. These networks are very prone to failure due to their enormous number of nodes and their unique hardware and software restrictions. To make sure network performance, a lot of study needs to be done to improve failure tolerance and stability. This study looks at how to judge the availability and dependability of WSNs that have long‐term issues. The suggested method checks how well a network works in various failure cases by using fault trees and Markov chain analysis. Such methods help us find and study possible failure scenarios and how they might impact the network's dependability in a planned way. The results show that WSNs have major flaws and give useful suggestions for making the systems work better. The findings show that using these evaluation methods may greatly enhance the ability to handle faults, lower the risk of damage, and allow developers of WSNs to make smart choices. [ABSTRACT FROM AUTHOR]

Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine‐learning (ML) assisted scheduling optimization [J. Comput. Chem.2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load‐balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re‐normalized exciton model with time‐dependent density functional theory (REM‐TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML‐assisted coded calculations can further improve the load‐balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states. [ABSTRACT FROM AUTHOR]

Anonymous communication is crucial for preserving user privacy in various applications, such as anonymous browsing, secure online payments, and electronic voting. However, current systems face significant challenges related to robustness, fault tolerance, and efficient communication management. This paper introduces SRFACS (Secure and Robust Framework for Anonymous Communication Systems), designed to address these issues by integrating advanced cryptographic techniques with a structured communication framework. Traditional anonymous communication systems usually lack sufficient fault tolerance, making them vulnerable to node failures, especially in asynchronous environments. In order to overcome these limitations and ensure that the system can keep running in the event of node failure (especially in an asynchronous environment), SRFACS integrates an improved asynchronous Byzantine Fault Tolerant (ABFT) protocol. We use this protocol to expand the traditional single communication node into a structured node group, and handle up to (n − 1)/3 faulty nodes in these groups, so as to enhance fault tolerance and maintain continuous operation. To efficiently manage inter-group communication, SRFACS utilizes leader nodes to coordinate and streamline the communication processes. To mitigate the risks associated with leader failures, we have implemented an efficient leader change protocol that promptly replaces defective leaders, ensuring uninterrupted system performance. Additionally, to prevent erroneous leader actions from compromising the system, we have introduced an advanced multi-signature scheme. This approach secures communication by requiring multiple signatures for verification processes. Furthermore, we have implemented a reputation incentive mechanism to encourage nodes to maintain optimal performance and avoid malicious behavior. This mechanism evaluates nodes according to their past activities and reliability to achieve dynamic updates of SRFACS node groups. We have conducted rigorous security analyses and component performance evaluations of SRFACS, further confirming its potential as a promising secure communication solution. [ABSTRACT FROM AUTHOR]

The fault tolerance of an interconnection network of parallel and distributed systems can be evaluated by various topological parameters of its underlying graph G , with strong Menger edge connectivity being a vital parameter in this regard. A connected graph G is called strongly Menger edge connected (SM- λ) if it connects any pair of vertices u and v with min { d G (u) , d G (v) } number of edge-disjoint paths. Under the uniform distribution of faults in a large interconnection network, it is improbable that each faulty edge incident to a vertex will occur simultaneously. Thus, m -strongly Menger edge connected of order t was introduced in 2018 by He et al. Here, G is called as m -strongly Menger edge connected of order t , if G − F remains SM- λ , where F is an arbitrary edge set in a graph G with | F | ≤ m and the minimum degree of the remaining graph δ (G − F) ≥ t. The largest m keeping the property of G − F being SM- λ is denoted as s m λ t (G). Among variants of hypercube, the n -dimensional folded-crossed hypercube F C Q n attracts attention in recent years. In this paper, we focus on calculating the exact value of the maximum conditional edge-fault-tolerant number of order t of F C Q n , s m λ t (F C Q n) = 2 t (n + 1 − t) − (n + 1) for two integers 1 ≤ t ≤ n − 2 and n ≥ 3. [ABSTRACT FROM AUTHOR]

Ensuring fault tolerance in systems of multiple sources of energy (SMSE) is crucial for reliable operation. Detecting, localizing, and characterizing faults are essential tasks for system integrity. This paper presents a novel approach utilizing hierarchical Bayesian belief networks (HBBNs) to identify and isolate open-circuit faults in DC–DC power converters commonly employed in SMSE applications. Our method addresses the challenge of fault detection and isolation, significantly enhancing system reliability. In particular, we design a comprehensive system capable of detecting and isolating faults in a system of multiple DC–DC converters, leveraging the interpretability and efficiency of HBBNs. We also utilize measurements from other converters to detect and isolate faults in a single converter, enabling efficient fault management. Our approach utilizes regularly monitored variables of the system, eliminating the need for additional sensors, thereby reducing complexity and cost. Additionally, we generalize HBBNs to be adaptable to any number of converters, providing scalability and flexibility in fault detection and isolation. Notably, the interpretability and simplicity of HBBNs, with a small number of parameters compared to other data-driven methods such as neural networks, contribute to their effectiveness in fault management. Through extensive testing on simulated data generated via a developed state space model, our approach demonstrates its effectiveness in bolstering the robustness of DC–DC power converters against open-circuit faults. [ABSTRACT FROM AUTHOR]

Switched Reluctance Motor (SRM) drives have gained a lot of attention in the drive domain because of their built-in benefits, which include ease of use, longevity, and compatibility with high-speed applications. Precise speed control is essential to minimize torque variations and maximize SRM performance. Traditionally, the proportional–integral controller is used for speed control. The nonlinearity and uncertain complexities of SRM have shifted the focus to sophisticated control techniques from conventional. In this work, the Model Predictive Controller (MPC) is applied for SRM speed control, and its performance is compared with that of conventional control methods. MPC improves speed control precision and disturbance rejection by taking into account input restrictions and system dynamics through optimization across a constrained prediction horizon, a strong fault tolerance capability. Furthermore, three different models are developed to control the speed of the SRM using a PI controller, a lookup table-based controller, and an adaptive neuro-fuzzy inference system controller to compare the results of MPC. Through extensive simulations, the study seeks to show how successful MPC is in terms of speed tracking accuracy, transient responsiveness, and stability when compared to standard control techniques, especially under variable operating circumstances and load disturbances. The practicality of the suggested control approach is illustrated by comparing it with other conventional control methods, and its performances are evaluated by using real-time simulator OPAL-RT 4510 under various situations. [ABSTRACT FROM AUTHOR]

Blockchain (BC) technology has attracted a lot of interest due to its excellent security and privacy features as well as its immutability. While BC can address security and privacy challenges in IoT, it is often computationally expensive, suffers from limited scalability, and introduces bandwidth overheads and delays, making it unsuitable for IoT applications. This paper proposes a novel lightweight and scalable blockchain (LIGHT-SB) designed specifically for IoT environments. In a smart IoT ecosystem, low-resource devices benefit from a centralized manager that handles communication keys and manages requests. LIGHT-SB enhances decentralization while maintaining robust privacy and security through a hybrid consensus algorithm combining permissionless proof-of-capacity and permissioned Byzantine fault tolerance (PoC-PBFT). Secure message delivery is achieved using SHA-256 and RSA cryptographic techniques. IoT device authentication, communication, and storage are safeguarded within the network. Distributed throughput management is implemented to monitor and adjust BC usage, ensuring it remains efficient. Qualitative evaluations demonstrate that LIGHT-SB effectively mitigates several security threats. [ABSTRACT FROM AUTHOR]

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services. [ABSTRACT FROM AUTHOR]

Aiming at the situation of unstable control and high error during recoil compensation control of gunship borne automatic weapon, the recoil compensation control algorithm of gunship borne automatic weapon is proposed. According to the specific firing conditions of airborne automatic weapons, combined with the internal and external factors that affect the recoil of weapons, the force analysis is carried out. Meanwhile, the attitude parameters of gunship, including coordinate parameters and attitude Angle parameters, are calculated. On this basis, the control model of gunship is established, corresponding controller is designed, nonlinear interference observer is introduced, and adaptive control law is designed. The recoil compensation control of airborne automatic weapon is realized. The experimental results show that the proposed recoil compensation control algorithm has small control delay, can respond to compensation control changes in time, and has small control error. The overall fault tolerance of the control algorithm has been improved. [ABSTRACT FROM AUTHOR]

Fault tolerance refers to the ability of a system to continue functioning even if some of its components fail. This concept is fundamental in several domains because reliability and continuous operation are essential in carrying out their functions. Graph embedding means mapping a graph into some other structure that preserves some of the properties or relations held by the original graph. Applications in machine learning, data mining, and network analysis make fault-tolerant embedding of graphs a crucial topic. In this paper, fault-tolerant embeddings are proposed in Archimedean tessellations. The path in the embeddings does not intersect or overlap. [ABSTRACT FROM AUTHOR]

Consortium blockchains, characterized by regulated blockchain technologies with limited authorization management, have gained popularity in various domains, including supply chain, Internet of Things (IoT), justice, and education, and have now become an integral part of production life. Ensuring information consistency in blockchains, the practical byzantine fault tolerance (PBFT) consensus algorithm is deemed more suitable for application in partially decentralized systems. However, existing consensus algorithms for consortium blockchains exhibit limited effectiveness. Particularly, with the number of nodes increasing, the performance of PBFT for consortium blockchains declines significantly, hindering the development of large-scale consortium blockchains. In this paper, we propose a novel consensus algorithm called reputation slice practical byzantine fault tolerance (RSPBFT) based on the reputation evaluation model. The RSPBFT adopts a slicing approach, achieving broad consensus through orchestrated micro-consensuses. Moreover, the reputation evaluation model assigns distinct reputation evaluation scores to nodes, with high-scoring nodes being preferred as leaders. A concept of "Gray-Area" is introduced to enhance the stability of the consensus model by distinguishing different node types. Simulation experiments demonstrate that in large-scale node deployment scenarios, RSPBFT outperforms other consensus algorithms. Furthermore, in consortium blockchain environments with an equal number of nodes as other consensus models, RSPBFT exhibits superior consensus stability. [ABSTRACT FROM AUTHOR]

As for a class of strict‐feedback nonlinear systems with simultaneous sensor and actuator faults, the article focuses on the problem of fixed‐time fault‐tolerant control. Both fixed‐time command filters and a compensation mechanism are utilized to cope with these issues of differential explosion and filter errors. Meanwhile, through the fusion of fixed‐time control, fuzzy logic systems, adaptive control and the backstepping technology, a n$$ n $$‐step design scheme is presented. Our proposed controller ensures the fixed‐time convergence of all the signals in the system even with sensor/actuator faults, and allows the designers to acquire the upper limit value of the fixed convergence time only by tuning the design parameters. And this scheme takes into account rapidity, adaptability and fault tolerance of the system simultaneously. Based on the numerical simulation analysis, it is shown that the better tracking performance can be achieved. So it is concluded that our strategy is effective. [ABSTRACT FROM AUTHOR]

Within the intricate dynamics of a blockchain system, participants with bounded rationality may engage in dishonest behavior, thereby disrupting the consensus of the system in the absence of incentive mechanisms. This vulnerability is particularly common in voting-based consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT), yet existing analyses often overlook the rational perspectives of participants. In this paper, we introduce an evolutionary game-theoretic approach to extend the current incentive mechanisms, enhancing the system's stability and security. In permissioned blockchain environments, we developed a three-party evolutionary game model involving proposers, validators and auditors that dynamically assesses node strategies under various economic incentives and attack scenarios on an improved mechanism basis. Using replicator dynamic equations, we identified the conditions under which nodes converge to different evolutionarily stable strategies. Through simulation experiments, we validated the incentive mechanism's effectiveness in promoting honest participation and resisting bribery attacks. When dishonest nodes do not exceed 80%, the system can quickly evolve to the honest state and can cope with changes in the payoff for malfeasance, whereas the original mechanism could not reach the ideal state under any condition. This achievement is attributed to the design of the mechanism and the judicious adjustment of reward and punishment parameters. The research contributes valuable findings to the discourse on consensus security. It provides key insights for developing more robust and rational incentive mechanisms in future protocols. [ABSTRACT FROM AUTHOR]

Currently, Raft, as an mainstream consensus mechanism, has received widespread attention. Partition consensus can reduce the number of nodes involved in a single consensus and improve consensus efficiency. However, existing algorithms suffer from unreasonable partitioning and intolerance of Byzantine nodes. To address these problems, this paper proposes a novel Raft consensus algorithm combining comprehensive evaluation partitioning and Byzantine fault tolerance, CB-Raft. First, a comprehensive evaluation of nodes is conducted from the perspectives of consensus behavior and location, and the nodes are evenly divided based on the parity of the comprehensive ranking. Second, the leader is selected from the nodes with the top rankings in the comprehensive evaluation, and the nodes communicate with each other based on BLS signatures. Finally, a fast response mechanism based on cross-partition leader-follower communication is proposed to avoid the continued evil behavior of the leader, and a pipeline mechanism based on changeable signature thresholds is proposed to solve consensus blocking. The experimental results show that compared with the existing partitioning methods, the proposed partitioning scheme has significant advantages in terms of consensus latency, throughput, and the probability of partition success. Compared with the similar Raft algorithms, CB-Raft has high consensus performance and good resistance to Byzantine nodes. [ABSTRACT FROM AUTHOR]

Embedded artificial intelligence encompasses a diverse range of technologies, from advanced algorithms to highly specialized computing systems. Intelligent embedded systems are playing an increasingly crucial role in various industries such as automotive, aerospace, healthcare, and IoT. When considering the place that intelligent embedded systems take in our daily lives, it is very important to understand how critical their security is. In order to ensure their high performance, energy efficiency, and robustness, it is imperative to ensure rigorous task scheduling. We are interested in the problem of hard real-time fault-tolerant scheduling for periodic and independent preemptive tasks. This paper focuses on proposing a fault-tolerant scheduling algorithm for these systems. By using the Watchdog Timer, which allows intelligent embedded systems to be more autonomous by detecting processor errors and adopting the Earliest Deadline First (EDF) algorithm to allow our system to respect time constraints. The objective is to improve reliability and efficiency by ensuring the execution of critical tasks despite the presence of faults. Designing and implementing a fault-tolerant scheduling algorithm for embedded systems is a crucial aspect in various industries. This helps to improve the reliability and security of intelligent embedded systems, which is essential to ensure the smooth operation of the system. [ABSTRACT FROM AUTHOR]

Traditional voting schemes are often overwhelmed by problems such as deception, influence, and incompetence, which can be resolved by applying blockchain technology with transparency, decentralization, and immutability. This study proposes a safe and indisputable digital voting system with blockchain technology to maintain the integrity of the voting procedure. The reliability and privacy of the voting procedure are upheld with distributed ledger technology and cryptographic techniques. The essence of the proposed method is the immutability of the blockchain ledger, which ensures a tamper-proof record of each cast vote, promoting transparency and offering a way of audit for free verification. The proposed method employs cryptographic protocols to protect individual votes while preserving complete transparency and verifiability of the voting procedure. The InterPlanetary Filesystem (IPFS) is applied to ensure data integrity. Moreover, the practical Byzantine Fault Tolerance (pBFT) consensus algorithm is utilized to remove glitches in distributed settings. The proposed approach provides a decentralized platform where voters can cast their votes from anywhere without difficulty using an internet connection, eliminating the need for physical ballot papers and polling stations. Using immutable ledger and cryptographic security aspects in blockchain, the reliability of the voting procedure can be protected while maintaining voter anonymity and confidentiality. Finally, it is shown that the proposed scheme outweighs other existing approaches. [ABSTRACT FROM AUTHOR]

With the widespread trend of transportation electrification, much research has been focused on switched reluctance motor (SRM) because of its robustness and wide speed range. Though the SRM has a promising electric fault tolerance, the rotor support bearings subjected to mechanical friction are vulnerable to brinelling and corrosion. Furthermore, traditional bearing fault diagnosis approaches adopted for conventional motors are not well suited for SRM bearings because of its high torque ripples and noisy operation. Aiming at offering an efficient solution, this work proposes a novel technique by fusing the Hilbert-Huang transformation of vibration signal with region based Convolutional Neural Network. The core idea of the method is to decompose the vibration signal and employ a new kind of offline region generation algorithm for the detection of fault characteristics. The effectiveness of the designed model is tested and evaluated by a series of experiments. The results show that the proposed method achieved a remarkable diagnosis accuracy of 99.6%. Conclusively, a systematic comparison of the proposed model with the other state of the art models is carried out. [ABSTRACT FROM AUTHOR]

The human brain facilitates information processing via generating and receiving temporal patterns of short voltage pulses, a.k.a. neural spikes. This approach simultaneously grants low‐power operation as well as a high degree of noise immunity and fault tolerance at a small footprint and simplistic structure of the neurons. To date, the latter two key features are critically missing from the toolbox of artificial spiking neural network hardware, hindering the development of scalable and sustainable artificial intelligence (AI) platforms. Here, a compact, gate‐tunable neuron circuit is demonstrated, and its potential as a functional leaky integrate‐and‐fire (LIF) neuron is explored. It relies on a single nanoscale three‐terminal (3T) memristor device, which has been downscaled by 30% compared to previous work, where the set voltage and, thereby, the spiking probability of the neuron circuit can be widely tuned by the low‐voltage operation of the gate electrode. The influence of the gate voltage on the two‐terminal (2T) current–voltage characteristics is measured, statistically analyzed, and further utilized in a custom‐built LTspice model. The circuit simulations account for the experimentally observed, adjustable set voltage. The presented results demonstrate the merits of 3T memristors as compact, tunable, and versatile artificial neurons for neuromorphic computing applications. [ABSTRACT FROM AUTHOR]

This paper investigates the incorporation of multiphase (MP) and variable flux (VF) permanent magnet motors to electric vehicles (EVs). A literature review is carried out first, which covers the characteristics, benefits and challenges of both motor configurations. MP motors, with their increased phase number, offer enhanced fault tolerance, reduced torque ripple, and improved efficiency, among other benefits. VF technology allows for dynamic adjustment of the magnetic field, optimising motor performance across various operating conditions. By integrating these technologies, this study aims to harness the benefits of both MP configurations and VF capabilities. For this reason, the Finite Element Method (FEM) is used with three-phase, MP, VF, and VF-MP motors. The main contribution is that both technologies have been implemented in a single motor to evaluate and quantify their impact together, obtaining higher torque and constant power values, lower torque ripples, and higher efficiencies in the whole working range. [ABSTRACT FROM AUTHOR]

In electric vehicle industry, rechargeable multicell battery packs commonly with fixed configurations are adopted. Such fixed battery configurations have several drawbacks, including limited fault tolerance during unusual operating situations, poor cell state variation management, etc. Thus, this article proposes a self‐reconfigurable battery pack design considering two scenarios: with and without active cell balancing. Herein, the issue of cell state variation is mostly solved by the proposed configuration mode, and further monitoring, control, and protection can be easily appended to accomplish other functionalities, for example, meeting dynamic voltage requirement. Each proposed design is validated by simulation results for a six‐cell polymer lithium‐ion battery pack. The proposed design can maximally utilize the battery's capacity and help to protect cells from over‐charging and over‐discharging as well. This research could be further extended to other application scenarios involving wide‐range dynamic voltage requirements. [ABSTRACT FROM AUTHOR]

The traditional cloud‐centric approach in IoT applications lack the speed and efficiency required for time‐critical tasks, resulting in network inefficiencies. To address this, the notions of Edge and Fog computing have emerged as alternatives. Fog computing facilitates the deployment of services and applications closer to the network's edge, lowering latency and allowing real‐time capabilities. It enhances reliability, fault tolerance, and connectivity in areas with spotty network coverage. Despite the fact that fog computing overcomes the limitations of cloud‐centric IoT processing, its adoption faces challenges like platform independence, interoperability, and portability. To tackle these challenges, the FogDEFT (Fog computing out of the box: Dynamic dEployment of Fog service containers with TOSCA) framework was developed. It complies to OASIS‐TOSCA standards and guarantees dynamic deployment of fog services on resource‐constrained devices while leveraging Docker containerization technology to ensure platform independence and interoperability. Due to its tight coupling with Docker Swarm, which is designed for medium‐sized deployments, the fogDEFT framework is constrained by Docker Swarm's limitations, hindering its ability to effectively manage large‐scale, automated, and resource‐efficient microservice deployments. To address these limitations, we propose FogDEFTKube, an extension of the FogDEFT architecture that incorporates Kubernetes for orchestration, Jenkins for continuous integration and deployment, and a comprehensive redefinition of the core capabilities of the FogDEFT architecture. This offers a promising solution that supports Kubernetes for handling scalable and highly available fog applications with ease while offering CI/CD. FogDEFTKube simplifies the modeling and deployment of fog services while abstracting the complexities of underlying fog networks. [ABSTRACT FROM AUTHOR]

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power p t of 20 dBm and IRS amplification power P A of 4 dBm. Furthermore, the system outage probability is carried out with change in p t and amplification gain a. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements N , p t and P A . In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed. [ABSTRACT FROM AUTHOR]

Considering the fault tolerance of EMB (Electro-Mechanical-Brake) braking failure and anti-rollover control at the same time is one of the urgent problems to be solved in the driving safety of X-by-wire vehicles. Accurate rollover index is a key part of anti-rollover control. Aiming at the problem that the traditional rollover index reflects that the unsprung mass of the vehicle is insufficiently affected by road excitation, a tripped vehicle rollover dynamic model is established based on single-wheel braking failure, and a rollover evaluation index NLTR (New Load-Transfer-Rate) suitable for braking failure is proposed. In order to improve the lateral safety of the vehicle when the line control fails, a hierarchical anti-rollover controller based on road adhesion coefficient identification, SM-ABS (Sliding-Mode-ABS) control and DBR (Differential-Braking-Redistribution) control is designed. Taking the rollover index threshold as the controller trigger condition, the controller effects under NLTR, traditional RI2 and standard LTR indicators are compared respectively. Simulation and HIL test show that the traditional index controller has failure risk under extreme conditions. The designed NLTR index controller can accurately evaluate the rollover risk of the vehicle, control the vehicle in time, and improve the vehicle stability by 68.18% under Fish-Hook condition. [ABSTRACT FROM AUTHOR]

Field Programmable Gate Arrays are extensively used in space, military, and commercial sectors due to their reprogrammable nature. In high-safety environments, ensuring fault tolerance is crucial to improving the performance of electronic and computational systems. Common fault-tolerant methods include time redundancy, double modular redundancy, triple modular redundancy, hardware redundancy, self-checking, self-repairing, and Operand Width Aware Hardware Reuse. This paper introduces a novel approach based on error correction and detection techniques, aimed at reducing hardware complexity while optimizing for low power consumption, high speed, and minimal transistor count. The proposed technique is applicable to various arithmetic circuits. Fault-correcting and fault-detecting adders were designed using a combination of components, including a full adder (comprising one XOR gate, one NOT gate, and two 2:1 multiplexers), three XOR gates, three XNOR gates, one functional unit, two inverters, and two multiplexers (2:1 MUX). To assess the fault tolerance of the design, the technique was applied to an adder circuit, with its performance in terms of power consumption, hardware usage, energy efficiency, and delay simulated using Cadence Virtuoso @90 nm technology. Pre-layout and post-layout simulation results showed a 98% reduction in power consumption, 82% energy savings, 36.5 and 55.95% transistor savings, and a 215% reduction in area overhead compared to existing fault-tolerant adders. Additionally, multiplier designs were tested to validate the fault-correcting adder design. [ABSTRACT FROM AUTHOR]

From practical considerations, crisp sensor measurements are associated with tolerance value. Thus, the sensor measurements lead to intervals. Researchers proposed M, F, Ω, and N functions for the fusion of interval-valued sensor measurements. We proved that the F-function provides the worst-case optimal interval estimate given the number of faulty sensors. We generalize the F-function to multi-dimensional intervals. We also propose new results on the attack resilience of fusion functions. Thus, this research paper provides new insights into robust sensor fusion. [ABSTRACT FROM AUTHOR]

Achieving increased fault tolerance in an electric motor requires decisions to be made about the type and specifications of the motor machine and its appropriate design. Depending on the type of motor, there are generally three ways to achieve an increased resistance of the drive system to tolerate resulting faults. The simplest way is to select the right motor and design it appropriately. Switched reluctance motors (SRMs) have a high tolerance for internal faults (in the motor windings). Failure tolerance can be improved by using parallel paths. The SRM 24/16 solution has been proposed, which allows for operation with four parallel paths. In this paper, a mathematical model designed to analyse the problem under consideration is provided. Based on numerical calculations, the influence of typical faults (open and partial short circuit in one of the paths) on the electromagnetic torque generated as well as its ripple and (source and phase) currents were determined. The higher harmonics of the source current (diagnostic signal) were determined. Laboratory tests were performed to verify the various configurations for the symmetric and emergency operating states. The feasibility of SRM correct operation monitoring was determined from an FFT analysis of the source current. [ABSTRACT FROM AUTHOR]

The current teaching mode in geometry mainly focuses on two-dimensional levels, and the teaching tools utilized are static and not interactive. This paper proposes the use of a smart pen for three-dimensional geometry experimental teaching and a multimodal intention understanding and human-computer collaboration algorithm for the smart pen. The primary innovations of this paper lie in the development of a smart pen and a virtual platform for geometry education tailored for geometry experimental instruction. This system can promptly perceive and comprehend user behavior in real-time. Furthermore, a standardized topological equivalence model is proposed as the basis for a point selection strategy. By establishing correspondence between the modeled point selection model and the actual operation scene, the behavioral intent imposed on the model is applied to the operation object of the actual scene. Additionally, CNN-based and information entropy-based multimodal fusion intention understanding models are proposed for different input modalities to capture the operational intention of users by fusing their multimodal input data. The algorithm further improves the accuracy rate through an error correction mechanism based on implicit interaction to achieve better human-computer collaboration. The algorithm proposed in this paper has resulted in a 0.47-second improvement in point selection average time and achieved an intent understanding accuracy of 98.21%. This improvement leads to better fault tolerance and fluency during human-computer interaction, reduces the cognitive load on the user, and improves the overall user experience. [ABSTRACT FROM AUTHOR]

Let r ≥ 0 be a fixed integer. If F is an edge set of a connected graph G satisfying the minimum degree of G − F being at least r , then F is a conditional faulty edge set of order r. The graph G is called F -strongly Menger-edge-connected if each pair of vertices u and v are connected by min { deg G − F (u) , deg G − F (v) } edge-disjoint paths in G − F , where deg G − F (u) and deg G − F (v) are the degrees of u and v in G − F , respectively. A graph G is m -strongly Menger-edge-connected of order r if G is F -strongly Menger-edge-connected of order r for every F ⊂ E (G) with | F | ≤ m and F is a conditional edge fault set of order r , and the maximum value of m is written as s m λ r (G). Hamming graph K L n has been widely concerned by researchers due to its excellent properties such as good connectivity, scalability, symmetry and iterativity. This paper considers various sufficient conditions of K L n to be F -strongly Menger-edge-connected of order r and determine the exact value of s m λ (L − 1) r (K L n) by studying the edge-disjoint paths in K L n with edge faults, s m λ (L − 1) r (K L n) = (L − 1) L r (n − r) − (L − 1) n , where 0 ≤ r ≤ n − 2 and n ≥ 3. [ABSTRACT FROM AUTHOR]