WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded,the demand for multi-service transmission has gradually increased.The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks.However,it still cannot cope with situations such as wireless access point(AP)failure.To solve this problem,this paper combines orthogonal fre-quency division multiple access(OFDMA)technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails.First,under the premise of AP layout with strong coverage over the whole area,a faulty AP determination method based on beacon frames(BF)is designed.Then,the maximum signal-to-interference ratio(SINR)is used as the principle to select AP reconnection for the affected users.Finally,this paper designs a dynamic access selection model(DASM)for service frames of power Internet of Things(IoTs)and a schedul-ing access optimization model(SAO-MF)based on multi-frame transmission,which enables access optimization for differentiated services.For the above mechanisms,a heuristic resource allocation algorithm is proposed in SAO-MF.Simulation results show that the method can reduce the delay by 15%and improve the throughput by 55%,ensuring high-quality communication in power wireless networks.

An Optimal Cluster Head and Gateway Node Selection with Fault Tolerance

In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-tion for Cluster Head and Gateway Selection(NQCAFFFOCHGS)has the best network performance because it uses the Improved Weighted Clustering Algo-rithm(IWCA)to cluster the network and the FFO algorithm,which uses fuzzy-based network metrics to select the best CH and entryway.However,the major drawback of the fuzzy system was to appropriately select the membership func-tions.Also,the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway.When learning fuzzy sets,this algorithm employs a new Continuous Action-set Learning Automata(CALA)approach that correctly modifies and chooses the fuzzy membership functions.Despite the fact that it extends the network’s lifespan,it does not assist in the detection of defective nodes in the routing route.Because of this,a new Fault Tolerance(NQCAEFFFOCHGS-FT)mechanism based on the Distributed Connectivity Restoration(DCR)mechanism is proposed,which allows the net-work to self-heal as a consequence of the algorithm’s self-healing capacity.Because of the way this method is designed,node failures may be utilised to rebuild the network topology via the use of cascaded node moves.Founded on the fractional network information and topologic overhead related with each node,the DCR is suggested as an alternative to the DCR.When compared to the NQCAFFFOCHGS algorithm,the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay,energy consumption,Packet Loss Ratio(PLR),Normalized Routing Overhead(NRO),and Balanced Load Index(BLI).

SFSDA:Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private data.Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its availability.Recently,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et al.Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset.In this paper,firstly,an efficient attack with both theorems proving and experimentative verification is launched.We find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the SM.Secondly,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption process.In addition,fault tolerance is very important to aggregation schemes in practical scenarios.In most of the current schemes,once some SMs failed,then they will not work.As far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault tolerance.Finally,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their ciphertexts.Experiments show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.

Energy Efficient Unequal Fault Tolerance Clustering Approach

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

T-PBFT: An EigenTrust-Based Practical Byzantine Fault Tolerance Consensus Algorithm

Blockchain with these characteristics of decentralized structure, transparent and credible, time-series and immutability, has been considering as a promising technology. Consensus algorithm as one of the core techniques of blockchain directly affects the scalability of blockchain systems. Existing probabilistic finality blockchain consensus algorithms such as PoW, PoS, suffer from power consumptions and low efficiency;while absolute finality blockchain consensus algorithms such as PBFT, HoneyBadgerBFT, could not meet the scalability requirement in a largescale network. In this paper, we propose a novel optimized practical Byzantine fault tolerance consensus algorithm based on EigenTrust model, namely T-PBFT, which is a multi-stage consensus algorithm. It evaluates node trust by the transactions between nodes so that the high quality of nodes in the network will be selected to construct a consensus group. To reduce the probability of view change, we propose to replace a single primary node with a primary group. By group signature and mutual supervision, we can enhance the robustness of the primary group further. Finally, we analyze T-PBFT and compare it with the other Byzantine fault tolerant consensus algorithms. Theoretical analysis shows that our T-PBFT can optimize the Byzantine fault-tolerant rate,reduce the probability of view change and communication complexity.

Excellent Practical Byzantine Fault Tolerance

With the rapid development of blockchain technology,more and more people are paying attention to the consensus mechanism of blockchain.Practical Byzantine Fault Tolerance(PBFT),as the first efficient consensus algorithm solving the Byzantine Generals Problem,plays an important role.But PBFT also has its problems.First,it runs in a completely closed environment,and any node can't join or exit without rebooting the system.Second,the communication complexity in the network is as high as O(n2),which makes the algorithm only applicable to small-scale networks.For these problems,this paper proposes an Optimized consensus algorithm,Excellent Practical Byzantine Fault Tolerance(EPBFT),in which nodes can dynamically participate in the network by combining a view change protocol with a node's add or quit request.Besides,in each round of consensus,the algorithm will randomly select a coordination node.Through the cooperation of the primary and the coordination node,we reduce the network communication complexity to O(n).Besides,we have added a reputation credit mechanism and a wrong node removal protocol to the algorithm for clearing the faulty nodes in time and improving the robustness of the system.Finally,we design experiments to compare the performance of the PBFT and EPBFT algorithms.Through experimental,we found that compared with the PBFT algorithm,the EPBFT algorithm has a lower delay,communication complexity,better scalability,and more practical.

Analysis and modeling of resistive switching mechanism oriented to fault tolerance of resistive memory based on memristor

With the progress of the semiconductor industry, resistive memories, especially the memristor, have drawn increasing attention. The resistive memory based on memrsitor has not been commercialized mainly because of data error. Currently, there are more studies focused on fault tolerance of resistive memory. This paper studies the resistive switching mechanism which may have time-varying characteristics. Resistive switching mechanism is analyzed and its respective circuit model is established based on the memristor Spice model.

Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration.In Real-Time Systems(RTS),deadline is the key to successful completion of the program.If tasks effectively meet the deadline,it means the system is working in pristine order.However,missing the deadline means a systemic fault due to which the system can crash(hard RTS)or degrade inclusive performance(soft RTS).To fine-tune the RTS,tolerance is the critical issue and must be handled with extreme care.This article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in RTS.The backup method has been derived to prevent the system from being recursively migrating the same task.If any task migrates three times,this migrated task will get shifted to the backup queue.This backup queue assigns the task to a backup processor and is destined for final execution.For performance evaluation purposes,a relative graph between fault and failure rates,failure and total processor utilization along with other averages have been evaluated.Furthermore,these archived results are compared with fault-tolerant Earliest Deadline First(EDF)and Rate Monotonic Scheduling(RMS)algorithms independently in relatively similar conditions.These comparisons show better performance against overloading conditions.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

Fault Tolerance for Corporate Innovation:Evidence from the Reform of State-owned Enterprises

Innovation is the source of power for high-quality development,and a relaxed institutional arrangement is a necessity for promoting innovation in enterprises.This paper examines the impact of fault tolerance on corporate innovation,theoretically analyzes the positive effects of fault tolerance on stimulating innovation,and estimates the influence of fault tolerance on corporate innovation by using the panel data of listed manufacturing companies from 2007 to 2019,taking the quasi-natural experiment of fault-tolerance system in various places since the new round of stateowned enterprise(SOE)reform.This study finds that the fault-tolerance system has significantly improved the R&D investment and patent application of the pilot SOEs,but the incentive effect is mainly reflected in the utility model patents;fault-tolerance system mainly promotes corporate innovation by alleviating managers’career worries and improving governance ability.Furthermore,the fault-tolerance system is more effective for small and medium-sized SOEs with low market competition and can alleviate the restriction of agency cost on corporate innovation.In addition,the fault-tolerance system has not yet had a significant spillover effect on private enterprises.These results show that in the SOE reform,it is necessary to strengthen the institutional arrangement of fault tolerance and create a relaxed environment to encourage innovation;it is necessary to organically integrate fault-tolerancesystem design into executive measurement and corporate governance,promote faulttolerant system to be implemented in a wider range of enterprises,and enhance the incentive effect on high-quality innovation.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

On Fault Tolerance of 3-Dimensional Mesh Networks

In this paper, the concept of k-submesh and k-submesh connectivity fault tolerance model is proposed. And the fault tolerance of 3-D mesh networks is studied under a more realistic model in which each network node has an independent failure probability. It is first observed that if the node failure probability is fixed, then the connectivity probability of 3-D mesh networks can be arbitrarily small when the network size is sufficiently large. Thus, it is practically important for multicomputer system manufacturer to determine the upper bound for node failure probability when the probability of network connectivity and the network size are given. A novel technique is developed to formally derive lower bounds on the connectivity probability for 3-D mesh networks. The study shows that 3-D mesh networks of practical size can tolerate a large number of faulty nodes thus are reliable enough for multicomputer systems. A number of advantages of 3-D mesh networks over other popular network topologies are given. Compared to 2-D mesh networks, 3-D mesh networks are much stronger in tolerating faulty nodes, while for practical network size, the fault tolerance of 3-D mesh networks is comparable with that of hypercube networks but enjoys much lower node degree.

A Comprehensive Survey on Fault Diagnosis and Fault Tolerance of DC-DC Converters

DC-DC converters are becoming more commonly used in power conversion solutions for energy management purposes,being employed in an ever-increasing range of DC-based applications,such as LED lighting,electric vehicles,energy storage solutions,and consumer electronics(laptops,smartphones,etc.).In this context,efficiency and reliability are critical.The research efforts made in improving reliability of DC-DC converters are still quite narrow and scattered.Moreover,DC-DC converters take the shape of an endless number of topologies,with different functionalities and operation principles,thus complicating the task of improving reliability of all forms of DC-DC converters.Consequently,compiling the information about the main failure modes,corresponding fault diagnostic algorithms and fault tolerance strategies developed so far,in a single document,becomes increasingly necessary.Accordingly,this paper presents an up-to-date review of the recent achievements attained regarding the improvement of availability and reliability of DC-DC converters.

Fault Tolerance Mechanism in Chip Many-Core Processors

As semiconductor technology advances, there will be billions of transistors on a single chip. Chip many-core processors are emerging to take advantage of these greater transistor densities to deliver greater performance. Effective fault tolerance techniques are essential to improve the yield of such complex chips. In this paper, a core-level redundancy scheme called N+M is proposed to improve N-core processors’ yield by providing M spare cores. In such architecture, topology is an important factor because it greatly affects the processors’ performance. The concept of logical topology and a topology reconfiguration problem are introduced, which is able to transparently provide target topology with lowest performance degradation as the presence of faulty cores on-chip. A row rippling and column stealing (RRCS) algorithm is also proposed. Results show that PRCS can give solutions with average 13.8% degradation with negligible computing time.

The superior fault tolerance of artificial neural network training with a fault/noise injection- based genetic algorithm

Artificial neural networks （ANNs） are powerful compu- tational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance （FT）, an important property of ANNs, ensures their reliability when signifi- cant portions of a network are lost. In this paper, a fault/ noise injection-based （FIB） genetic algorithm （GA） is proposed to construct fault-tolerant ANNs. The FT per- formance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algo- rithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR （XOR） problem and the overlapping clas- sification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore Indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.

Fault Tolerance and Recovery for Group Communication Services in Distributed Networks

Group communication services （GCSs） are becoming increasingly important as a wide field of promising applications has emerged to serve millions of users distributed across the world.However,it is challenging to make the service fault tolerance and scalable to fulfill the voluminous demand of users in a distributed network （DN）.While many reliable group communication protocols have been dedicated to addressing such a challenge so as to accommodate the changes in the network,they are often costly or require complicated strategies to handle the service interruptions caused by node departures or link failures,which hinders the service practicability.In this paper,we present two schemes to address the challenges.The first one is a location-aware replication scheme called NS,which makes replicas in a dispersed fashion that enables the services on nodes to gain immunity of failures with different patterns （e.g.,network partition and single point failure） while keeping replication overhead low.The second one is a novel failure recovery scheme that exploits the independence between service recovery and structure recovery in time domain to achieve quick failure recovery.Our simulation results indicate that the two proposed schemes outperform the existing schemes and simple alternative schemes in service success rate,recovery latency,and communication cost.

A comprehensive study on fault tolerance in stream processing systems

Stream processing has emerged as a useful technology for applications which require continuous and low latency computation on infinite streaming data.Since stream processing systems(SPSs)usually require distributed deployment on clusters of servers in face of large-scale of data,it is especially common to meet with failures of processing nodes or communication networks,but should be handled seriously considering service quality.A failed system may produce wrong results or become unavailable,resulting in a decline in user experience or even significant financial loss.Hence,a large amount of fault tolerance approaches have been proposed for SPSs.These approaches often have their own priorities on specific performance concerns,e.g.,runtime overhead and recovery efficiency.Nevertheless,there is a lack of a systematic overview and classification of the state-of-the-art fault tolerance approaches in SPSs,which will become an obstacle for the development of SPSs.Therefore,we investigate the existing achievements and develop a taxonomy of the fault tolerance in SPSs.Furthermore,we propose an evaluation framework tailored for fault tolerance,demonstrate the experimental results on two representative open-sourced SPSs and exposit the possible disadvantages in current designs.Finally,we specify future research directions in this domain.

Cross-Rack Updates in Vertical Array Codes Storage Systems with High Fault Tolerance

Vertical array codes have less computational complexity and update complexity in comparison with horizontal array codes.However,the fault tolerance of the existing vertical array codes is in general lower than that of horizontal array codes.In addition,the cross-rack bandwidth is often the bottleneck of the update performance in erasure-coded storage systems.In this paper,we propose a cross-rack update(CRU)mechanism for vertical array codes intended to improve both the fault tolerance and update performance of erasure-coded storage systems.CRU builds on three parts:(i)stripe encoding,which can improve the fault tolerance of vertical code by encoding multiple sub-stripe;(ii)node grouping,which filters out the best combination of nodes to minimize cross-rack update traffic;(iii)selective logging,which can selectively log based on the location of data sub-blocks and parity sub-blocks to reduce disk I/O and cross-rack traffic.We evaluate CRU via trace-driven analysis and local cluster experiments.Evaluations show that CRU can significantly reduce cross-rack update traffic and improve system update throughput.

Fault Tolerance for Reformers

Government officials in southwest China’s Chongqing Municipality are being given the benefit of the doubt for misdemeanors.A recently issued regulation states that officials who have made mistakes and incurred