Search

Spin-Transfer-Torque (STT) MRAM is configured with high access speed, readily hybrid integration and guaranteed endurance/retention, which becomes one of the most promising candidates for next generation memory. However, the major challenge of STT-MRAM is its high switching energy consumption, accompanied with unreliable writing operation. This work exploits two emerging design paradigms entitled approximation and speculation MRAM. A novel design scheme with timing speculation and approximate storage is proposed to improve energy efficiency in MRAM write operation by reducing write time. Simulation results show that latency and energy loss in entire write operation can be significantly reduced. The proposed technique obtains 84%/57.4% in the symmetric and unidirectional writing operations. Meanwhile, timing speculative method improves the failure/reliability in writing by re-write strategy. Related MRAM macro specification is applied to image processing, which obtains 10% (58.8%-67.2%) of the improvement of precision in image and higher image quality is obtained under the same accuracy rate.

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

The huge amount of data produced in the 6G networks not only brings new challenges to the reliability and efficiency of mobile devices but also drives rapid development of new storage techniques. With the benefits of fast access speed and high reliability, NAND flash memory has become a promising storage solution for the 6G networks. In this paper, we investigate a protograph-coded bit-interleaved coded modulation with iterative detection and decoding (BICM-ID) utilizing irregular mapping (IM) in the NAND flash-memory systems. First, we propose an enhanced protograph-based extrinsic information transfer (EPEXIT) algorithm to facilitate the analysis of protograph codes in the IM-BICM-ID systems. With the use of EPEXIT algorithm, a simple design method is conceived for the construction of a family of high-rate protograph codes, called irregular-mapped accumulate-repeat-accumulate (IMARA) codes, which possess excellent decoding thresholds and linear-minimum-distance-growth property. Furthermore, motivated by the voltage-region iterative gain characteristics of IM-BICM-ID systems, a novel read-voltage optimization scheme is developed to acquire accurate read-voltage levels, thus minimizing the decoding thresholds (in dB) of protograph codes. Analyses and simulations indicate that the proposed IMARA-aided IM-BICM-ID scheme and read-voltage optimization scheme remarkably improve the convergence and decoding performance of flash-memory systems. Thus, the proposed protograph-coded IM-BICM-ID can be viewed as a reliable and efficient storage solution for the new-generation mobile networks, such as Internet of Vehicles.

Advanced safe battery storage systems with health prognostic performance are vital for electric vehicles. Various faults of lithium-ion batteries are usually undetectable in their early stage due to their concealment and graduality. This paper presents a real-time fault diagnosis and isolation scheme for real-scenario batteries using the normalized discrete wavelet decomposition. The early frequency-domain features of the fault signals are extracted utilizing the high-frequency detail wavelet components, and a multi-level fault prognosis strategy is developed considering complex charging/driving characteristics under real-vehicle operating conditions. The verification results, implemented on loose wire connection batteries and real-scenario thermal runaway batteries, demonstrate that the proposed method can accurately extract and locate the hidden fault signals even under small magnitudes, as well as effectively detecting and isolating battery faults before thermal runaway. Furthermore, significant reliability and stability of the proposed method are verified on more real-vehicle operation data, enabling online monitorable and traceable of battery faults before triggering thermal runaway, safeguarding drivers and passengers in real-world vehicular operation.

Although Li-ion batteries are widely used, recent catastrophic accidents have seriously hindered their widespread application. In this study, a novel acoustic-signal-based battery fault warning and location method is proposed. This method requires only four acoustic sensors at the corners of the energy storage cabin. It captures the venting acoustic signal when a fault occurs in the cell and calculates the spatial location of the cell. The maximum spatial error is 0.1 m. Considering that the effective range of a fire-fighting facility is significantly greater than 0.1 m, such a location error is acceptable. Furthermore, a wavelet-transform-based anti-misjudgment method that ensures the reliability of the fault warning and location is proposed. Thus, a nonintrusive, timely, and effective solution to ensure the safety of the battery energy storage system is provided.

This paper evaluates outage probability (OP) and intercept probability (IP) of physical-layer security based MIMO networks adopting cooperative jamming (Coop-Jam). In the considered scenario, a multi-antenna source communicates with a multi-antenna destination employing transmit antenna selection (TAS)/ selection combining (SC), in presence of a multi-antenna eavesdropper using SC. One of jammers appearing near the destination is selected for generating jamming noises on the eavesdropper. Moreover, the destination supports the wireless energy for the chosen jammer, and cooperates with it to remove the jamming noises. We consider two jammer selection approaches, named RAND and SHORT. In RAND, the destination randomly selects the jammer, and in SHORT, the jammer, which is nearest to the destination, is chosen. We derive exact and asymptotic expressions of OP and IP over Rayleigh fading, and perform Monte-Carlo simulations to verify the correction of our derivation. The results present advantages of the proposed RAND and SHORT methods, as compared with the corresponding one without using Coop-Jam.

To sustain the economic growth, infrastructure development is only of the essential aspects and also the power sector is the most important infrastructure elements. For the increment of energy demand, apart from the conventional energy sources, the wind, solar etc., of the necessary sources to produce the electrical energy to meet out the demand. The conventional sources of energy associated with many problems like, prices increased for petroleum products, environmental implications, safety from radiations, global warming etc. To overcome the difficulties in association with conventional energy sources, most of the countries including India have shifted the attention for improving renewable energy sources. In practical, the Generation Expansion Planning (GEP) problem is a large scale and complex. Also the nonlinear optimization problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. By the effective planning of power systems can be achieved by the accurate solution of the GEP problem. The aim of this paper is to apply the Differential Evolution (DE) algorithm to the GEP problem and find out the least cost expansion plan. It is applied for a test system of six and fourteen years planning horizons. As the wind energy composition in the system is continuously growing, the impact of the same has been studied. The different cost component with variations and also the reliability indices are studied.

We consider a hierarchical structure of a permissioned blockchain with three types of participant: providers, collectors, and governors. Providers forward transactions to collectors; collectors upload received transactions to governors after verifying and labeling them; and governors validate a portion of the labeled transactions they receive, pack valid transactions into a block, and append the block to the ledger. This model has various fields of application including data collection from the Internet-of-Things and second-hand markets. Our main contribution is to propose a reputation-based protocol to help governors evaluate the reliability of collectors. Specifically, given a transaction, each governor runs a softmax-based function to calculate a probability for each collector that sent and labeled this transaction. The probabilities, calculated using collectors' reputations as inputs, represent the likelihood of the lead governor selecting the labeled transaction from collectors to consider for further validation. After the lead governor verifies a transaction, all collectors' reputations are updated in line with the agreement of their labeling and the validity of the transaction as found by the lead governor. We show, both theoretically and empirically, that our protocol can significantly reduce governors' verification workloads while maintaining firm liveness and high incentives.

In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) experience. The visual content between different viewpoints is highly homogenized, suggesting the possibility of good compression and encoding. However, most fixed-structure LF coding schemes are difficult to adapt to the real-time requirements of different LF applications and best-effort network conditions causing packet loss. In this paper, we propose a dynamic adaptive LF video transmission scheme that can achieve high compression and yet provide near-distortion-free LF video when the network condition is stable. Additionally, for unstable network conditions a description scheduling algorithm is proposed, which can decode the LF video with the highest possible quality even if partial data cannot be received completely and/or timely. We achieve this by designing a Multiple Description Coding (MDC) based solution to transport the LF video compressed by a Graph Neural Network (GNN) model. Experimental results show that the scheduling algorithm can improve the quality of the decoding results by 3% to 15%. Compared with other similar schemes, our system greatly improves the reliability of the video streaming system against packet loss/error and supports heterogeneous receivers.

Cloud computing has become one of the most important platforms for various applications like artificial intelligence, big data, the Internet of Things, and others, especially demand for cloud computing has exploded in recent years. Because of the increased demand for cloud computing, it has become more complex, which can lead to software or hardware failure. Due to the advanced infrastructure, failure may not be detected and repaired at a given timeline and will end up costing higher. In addition, the advent of cloud computing has another advantage over the massive spread of scientific work. Scientific workflow refers to a series of computations that enable data analysis in a distributed and systematic way; because this work flow has a large number of works, energy consumption is a major problem. Besides these issues, it also suffers from system reliability; in the response to these issues, several researchers have designed their mechanism, however they failed to understand cloud complex environment. Therefore, here we have designed a mechanism that efficiently reduces energy consumption, improve the fault tolerance to achieve reliability, performs operations in very less time, and optimize the cost in the workflow model. We have also demonstrated efficient energy optimization techniques by reducing task loads.

In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have provided a promising solution to the acceleration of neural networks. However, stuck-at faults (SAFs) in the memristor devices significantly degrade the computing accuracy of NCS. Besides, the memristor suffers from the process variations, causing deviation of the actual programming resistance from its target resistance. In this paper, we propose a reliability-driven design framework for a memristive crossbar-based NCS in combination with general and chip-specific design optimizations. First, we design a general reliability-aware training scheme to enhance the robustness of NCS to SAFs and device variations; a dropconnect-inspired approach is developed to alleviate the impact of SAFs; a new weighted error function, including cross-entropy error (CEE), the l₂-norm of weights, and the sum of squares of first-order derivatives of CEE with respect to weights, is proposed to obtain a smooth error curve, where the effects of variations are suppressed. Second, given the neural network model generated by the reliability-aware training scheme, we exploit chip-specific mapping and re-training to further improve computation accuracy loss incurred by SAFs. Experimental results show that the proposed method can boost the computation accuracy of NCS and improve the NCS robustness.

The idea of the Internet of Things that everything is linked to anything and everything interacts with the other is a great idea, and that the topic of integrating these devices will add a remarkable change in this world and increase its prosperity, and this interconnection occurs through the exchange of data and information and facilitating our communication with the things around us. But questions about security and privacy remain. When this massive amount of data is exchanged, it will certainly be an attractive environment for attackers and hackers. In this paper, we will present the issues of security and privacy, their main requirements, potential risks and tools to defend these risks, and a detailed review of security attacks in order to be able to deter these attacks achieve confidentiality, security and reliability for users.

The container selection processing performance analyses huge data with minimal resources and the lowest latency. The elastic management applications execute the containers with the specific hierarchy of virtual processing and machine management. The container that has performance degradation implicit provision of least expensive containers with minimal resources helps to increase the performance of containers. The specific data and stream processing prompts scrutinizing of data through the container selection process through different methodologies. To exterminate the bottleneck problem that selects efficient and required size, processing speed, and its reliability of guiding the batch processing of containers. The extensive neural learning handles container optimality involves a dynamic selection of appropriate containers in cloud service providers. The cloud service providers along with container selection contain batch processing and stream processing allocates efficient container-specific selection appropriately. In the huge data segregation of data processed data that emphasizes multiple data scrutinizing.

Over a last few decennary free space optical communication (FSO) have become more interesting alternative to the radio frequency for communication. The FSO is emanating technic that have been found the applications in different place of shorter & longer distance communication. The any technology, of FSO has been worked that much good in the several applications rather than other. In this paper we will examine FSO from different angles. The analyses this paper accompany of the technology has shown that FSO technologies inherent strengths and it lack the use of in ground cables which make it much expeditious & cheaper to installing, and the facts that will operates in the unlicensed spectrum, it will be removed & installed elsewhere and it has relatively high bandwidth. Specifically, this paper found that because FSO uses air as a communication medium, its performance & reliability are restricted by fog, rain, snow and dust. Maximum range of FSO may be in kilometers only. The applications that FSO technology seems most suited to is clear this pap rather and short distance connection establishment, like interrelation to broadband networks backbone & backbone links bet this papered building in the metropolitan area networks (MAN) or the campus area networks (CAN).

The technological innovation among a conscience as well as configured accessibility guidance conceptual model process has been combined mostly with digital technologies that generate integrated immediate transportation with adaptability shielding mechanism. The Intelligent as well as Adaptive Accessibility Benefits Interface updates and transmits roadway data collected by roadside sensors to a powerful guide known as a dynamic map (DM), which increases programmed portability control with high accuracy and reliability. To confirm the updating and establishment of roadway data to automatic motilities, we have built up a Wireless Electronic Traffic Mirror (WETM) that procures street data, perceives street items and moves acknowledgment data with a remote correspondence framework. Furthermore, to meet the security prerequisites, the AMAS should give the remote association most appropriate for portability. To give ideal remote availability, continuous remote channel checking, which quantifies the nature of remote channels with obstruction, is a significant assignment in the AMAS. The continuous peripheral distract surveillance implementation throughout combination with the generated WETM is mentioned in this report, and unambiguous confirmation including its exactness of every situation occurrence is revealed to explain the thinking beside how some the phenomenon including its pavement was improved.

Radio links in Wireless Body Area Networks (WBANs) suffer from both short-term and long-term variations due to the dynamic network topology and frequent blockage caused by body movements, making it challenging to achieve reliable, energy-efficient and real-time data communication. Through experiments with TelosB motes, we observe a strong positive relationship between the channel quality and the position of the sending node relative to the gateway. Motivated by this observation, we design Tuatara, a novel power-aware communication protocol that allows each sensor node to dynamically adjust its transmission power based on the channel status inferred from its instant position, aiming to save energy, reduce interference, and improve communication reliability. Based on a probabilistic model, power level selection is converted to calculate the optimal probability of selecting each power level at a given position, with the objective of minimizing the transmission cost. A reinforcement learning scheme is designed to adaptively update the power level selection probabilities, making Tuatara self-adaptable to changes in the signal propagation environment. Experimental results demonstrate that Tuatara outperforms the state-of-the-art protocols in various scenarios, with performance close to that of the optimal power selection solution even in scenarios where the packet rate is very low.

The term “replication” refers to the practice of keeping identical copies of data on multiple systems. Any distributed file system must have replication as one of its design requirements (DRS). For storing and processing data, Hadoop DFS is now used by the majority of academia and industry. The Hadoop distributed file system (HDFS) is a system for storage and processing huge amounts of information. In HDFS, inefficient replication is the main issue that contributes to a file system's performance degradation. Because of the system's robustness and dynamic features, the quantity of applications depending on Hadoop is rapidly growing. The HDFS, which is at the heart of Apache Hadoop, uses a static replication strategy to provide computation with reliability, scalability, and high availability. The admittance rate for each file in HDFS, however, is completely special due to the uniqueness of similar functions on various layers. As a result, using the similar replicating method for each file may have negative performance consequences. This paper proposes a method for dynamically replicating information files depending on predictive analysis after carefully considering the drawbacks of HDFS architecture. To address this issue, we propose an proficient data replication method in the Hadoop framework that increases the availability.

The modern electricity network has become smarter and more reliable because of the addition of information and communication technology. Dynamic thermal rating systems (DTR) allow existing transmission lines to be operated much closer to their limits. However, line outages after DTR implementation could threaten network security. Thus, system integrity protection schemes (SIPS), which are designed to ensure security, are useful to forestall impending contingencies that could arise from DTR implementation. Furthermore, synchrophasors can improve wide-area monitoring and protection capabilities. However, the reliability of these technologies considering their communication network availability is yet to be studied in a unified framework. Therefore, a novel framework of synchrophasor-based DTR and SIPS is presented while examining eight DTR network topologies and two SIPS schemes simultaneously. A sequential Monte Carlo simulation-based technique is proposed to simulate the effect of the topologies on system-wide reliability. This method is tested on the IEEE-RTS. Results show an improvement of 93.27% between DTR communication Schemes 1 and 8 in load curtailment. More importantly, the article reveals an improvement of 99.15% in the load curtailment value in scenarios where SIPS is deployed compared with the case where SIPS is not deployed, highlighting the benefits of DTR and SIPS hybrid implementation.

Virtual power plants (VPPs) offer a cost-effective solution to incentivize coordination between different resources participating in joint energy and reserve markets. However, emerging technologies such as storage and demand response cannot deliver flexibility over long periods due to inherent energy limitations. In this article, we, therefore, inform the day-ahead scheduling of VPPs with forecast scenarios of the balancing stage, which are complemented with information on nonshiftable load, renewable generation, and electricity prices. These multivariate scenarios (incorporating both time and cross-variable dependencies) are obtained using a machine learning framework in which probabilistic forecasts are converted into time trajectories using a copula-based sampling. The model is enriched with a detailed representation of the intraday decision stage wherein all flexible resources are dynamically allocated over the daily horizon. To ensure the reliability of the solution, we take into full consideration the revenues and unit-specific costs related to the activation of reserves. Outcomes show that improving the representation of the intraday dispatch stage while relying on representative balancing uncertainties are two complementary components for increasing the quality of the VPP day-ahead strategy, which ultimately fosters its economic value.

In this paper, we study a novel service reliability augmentation problem in an Edge Computing network, where mobile users request network services with a service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant network function (VNF) instance placement in case their primary VNF instances fail. To this end, we first formulate a novel service reliability augmentation problem for each admitted request and show the NP-hardness of the problem. We then provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets, and then develop a randomized algorithm with a good approximation ratio and high probability for the service reliability augmentation problem, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We thirdly extend the proposed algorithm for a single request to the service reliability augmentation problem for a set of requests. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and superior to their analytical counterparts.

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different up-sampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods.

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages.

This article proposes a mathematical model to optimally locate a set of detectors in such a way that the expected number of casualties in a given threat area can be minimized. The reliability of a detector is often a function of the duration that the threat remains within the effective radius. To accurately detect any threat event and to avoid any false alarm, in this article, two sets of detectors, primary and secondary, are utilized; the primary detectors rely on the secondary set as reinforcement to confirm or deny that a threat is present. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch and bound algorithm. A number of sensitivity analyses are performed to illustrate the robustness of the model and to draw key managerial insights. Experimental results reveal that a two-layer detection system will significantly minimize the expected number of casualties in a given threat area as opposed to a one-layer detection system.

Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, better performance, and high reliability are required. Inspired by the parallel and large-scale information processing structures in the human brain, a shallow broad neural network model is proposed on a specially designed multi-order Descartes expansion operation. Such Descartes expansion acts as an efficient feature extraction method for the network, improve the separability of the original pattern by transforming the raw data pattern into a high-dimensional feature space, the multi-order Descartes expansion space. As a result, a single-layer perceptron network will be able to accomplish the classification task. The multi-order Descartes expansion neural network (MODENN) is thus created by combining the multi-order Descartes expansion operation and the single-layer perceptron together, and its capacity is proved equivalent to the traditional multi-layer perceptron and the deep neural networks. Three kinds of experiments were implemented, the results showed that the proposed MODENN model retains great potentiality in many aspects, including implementability, parallelizability, performance, robustness, and interpretability, indicating MODENN would be an excellent alternative to mainstream neural networks.

In recent years, the exploration on large-scale cyber-physical systems (CPSs) has become a fertile research field of significant impact. Large-scale CPS applications cover not only manufacturing and production areas but also daily living domains. Traditional solutions dedicated for large-scale CPSs mainly concentrate on the service latency or reliability optimization, but neglect the resultant negative impact on system lifetime. In this paper, we conduct the first study on jointly optimizing the service latency and system lifetime subject to the constraints of reliability, energy consumption, and schedulability for large-scale CPSs. We propose an edge intelligent solution composed of offline and online phases. At offline phase, the long short-term memory (LSTM) technique is leveraged to predict task offloading rates at individual user groups. Afterwards, multi-objective evolutionary algorithm with dual local search (DLS-MOEA) is exploited to determine optimal system static settings of computation offloading mapping and task replication number. At online phase, an affinity-driven scheme incurring minimal system dynamic overheads is designed to deal with the inherent mobility of terminal users. We also build an algorithm validation platform upon which extensive simulation experiments are carried out. Experimental results show that our offline and online schemes outperform the state-of-the-art benchmarking methods by 27.1% and 43.5%, respectively.

Software-defined wireless sensor networks (SDWSNs) can be functionally affected by malicious sensor nodes that perform arbitrary actions, e.g., message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in SDWSNs. In this paper, we design a hierarchical trust management scheme for SDWSNs (namely TSW) to detect potential threats inside SDWSNs while promoting node cooperation and supporting decision making in the forwarding process. TSW evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the SDWSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Furthermore, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communications and trust management threats such as black-hole, selective forwarding, denial of service, bad mouthing, and ON-OFF attacks.

Hydraulic systems are a class of typical complex nonlinear systems, which have been widely used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent fault diagnosis problem of hydraulic systems has received increasing attention for it can increase operational safety and reliability, reduce maintenance cost, and improve productivity. However, because of the high nonlinear and strong fault concealment, the fault diagnosis of hydraulic systems is still a challenging task. Besides, the data samples collected from the hydraulic system are always in different sampling rates, and the coupling relationship between the components brings difficulties to accurate data acquisition. To solve the above issues, a deep learning model with multirate data samples is proposed in this article, which can extract features from the multirate sampling data automatically without expertise, thus it is more suitable in the industrial situation. Experiment results demonstrate that the proposed method achieves high diagnostic and fault pattern recognition accuracy even when the imbalance degree of sample data is as large as 1:100. Moreover, the proposed method can increase about 10% diagnosis accuracy when compared with some state-of-the-art methods.

Satisfying the quality of service (QoS) requirements of users in the form of channel availability and service retainability within the resource limited environment has been a major problem in cognitive radio networks. In this connection, several research studies have been carried out in the literature to improve the QoS of users by proposing dynamic channel reservation algorithms. However, the studies have a number of limitations in their conceptual and mathematical modeling of channel availability and service retainability, which render their performance evaluation unreliable. In this paper, we address these limitations for leading to more realistic, reliable, and practically valid modeling. For conceptual modeling, we use connection availability in place of channel availability, motivated by the fact that the latter does not necessarily lead to a successful establishment of connection and, thus, is not a suitable performance indicator. For instance, obtaining a channel for transmission is of no avail if the intended receiver is inaccessible. Similarly, we consider service retainability with accessibility/inaccessibility of the intended receiver incorporated. For mathematical modeling, we use CTMC and the resultant closed from expressions to include all the required states of channel availability yet unsuccessful connection establishment. Additionally, we derive closed form equations for channel availability and service retainability that are in exact conformance with the CTMC model. Results confirm that considering the impact of receiver’s accessibility leads to performance difference for the channel availability and service retainability presented in the sate-of-the-art.

Power routing (PR) provides a good choice to improve the modular converter reliability by equalizing the lifetime among multi-cells. The existing PR strategies just equalize the lifetime of power semiconductor devices (PSDs) by routing lighter loads for aged cells. However, the neglect of capacitor may mislead the multi-cell lifetime convergence. Besides, the light load allocation of the aging cell does not always mean the lifetime extension considering topologies and operation principles. Hence, this paper proposed a capacitor lifetime-based PR for input-series-output-parallel (ISOP) connected dual active bridge (DAB) converter. The proposed strategy can achieve the same accuracy as existing PR strategies, greatly reducing computation time and the complexity of the hardware circuit. Moreover, the non-monotonic relationship between the damage growth rate and load of each cell is revealed and a damage growth rate constraint is developed to ensure the PR feasibility. Simulation is performed to verify the validity of the proposed strategy and constraints. Finally, a scaled down prototype has been developed to verify the PR feasibility and obtain the non-monotonic relationship, verifying the necessity of proposed constraints. In addition, the effect of PR on the thermal stress and cumulative damage of capacitors is verified by a temperature test.

Traffic forecasting has attracted considerable attention due to its importance in proactive urban traffic control and management. Scholars and engineers have exerted considerable efforts in improving the performance of traffic forecasting algorithms in terms of accuracy, reliability, and efficiency. Spatial feature representation of traffic flow is a core component that greatly influences traffic forecasting performance. In previous studies, several spatial attributes of traffic flow are ignored due to the following issues: a) traffic flow propagation does not comply with the road network, b) the spatial pattern of traffic flow varies over time, and c) single adjacent matrix cannot handle the complex and hierarchical urban traffic flow. To address the abovementioned issues, this study proposes a novel traffic forecasting algorithm called traffic transformer, which achieves great success in natural language processing. The multihead attention mechanism and stacking layers enable the transformer to learn dynamic and hierarchical features in sequential data. Two components, namely, global encoder and global-local decoder, are proposed to extract and fuse the spatial patterns globally and locally. Experimental results indicate that the proposed traffic transformer outperforms state-of-the-art methods. The learned dynamic and hierarchical features of traffic flow can help achieve a better understanding of spatial dependency of traffic flow for effective and efficient traffic control and management strategies.

Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations between truths over time. To solve this problem, we propose an edge computing based privacy-preserving truth discovery mechanism, PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths and workers reliabilities. A truth-dependent budget recycle mechanism is designed for each edge server to adaptively determine the perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based perturbation mechanism is proposed to reduce the perturbation magnitude based on worker's reliability. We theoretical analyze the data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy w-event (,)-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches.

In this article, we consider the H∞ proportional-integral (PI) state estimation (SE) problem for discrete-time T-S fuzzy systems subject to transmission delays, external disturbances, and redundant channels. Multiple redundant communication channels are utilized between the sensors and the remote estimator to enhance the reliability of data transmissions. In order to characterize the transmission delays in network-based communication, a family of random variables with partly known probabilities, which are independent and identically distributed, is adopted to describe the random behavior of the transmission delays with the redundant channels. The objective of this work is to put forward a PI state estimator such that the dynamics of the estimation error is exponentially mean-square stable and satisfies the prescribed H∞ performance index of the disturbance attenuation/rejection. By employing the stochastic analysis approach, the error dynamics of the SE under the proposed state estimator is analyzed and sufficient conditions are obtained to ensure the existence of the required PI state estimator. Furthermore, the desired estimator parameters are derived by solving a nonlinear optimization problem. Finally, two simulation examples are exploited to demonstrate the validity of the proposed SE scheme.

Wireless Body Area Networks (WBAN) can provide continuous monitoring of patients’ health. Such monitoring can be a decisive factor in health and death situations. Fault management in WBANs is a key reliability component to make it socially acceptable and to overcome pertained challenges such as unpredicted faults, massive data streaming, and detection accuracy. Failures in fault detection due to hardware, software, and network issues may put human lives at risk. This paper focuses on detecting and predicting faults in sensors in the context of a WBAN. A framework is proposed to manage AI-based prediction models and fault detection using thresholds where four Machine learning techniques: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Support Vector Machines (SVM), and Decision Trees (DT), are used. The framework also provides alarm notifications, prediction model deployment, version control, and sensing node profiling. As a proof of concept, a fault management prototype is implemented and validated. The prototype classifies faults, manages automation of sensing node profiling, training, and validation of new models. The obtained experimental results show an accuracy greater than 96% for detecting faults with an inferior false alarm rate.

In this paper, an improved dynamic state estimation (DSE) based protection (DSEBP) scheme is proposed for transmission lines in MMC-HVDC grids. Firstly, a dynamic model of the protection zone (DC line of interest) is constructed using differential and algebraic equations. The impact of frequency dependent line parameters is mitigated through the utilization of the line mode transmission line model. Then, a novel Runge-Kutta (RK) discretization scheme is proposed to consider the dynamics of the control variables during electromagnetic transients, yielding reduced discretization error as compared to the conventional RK scheme. Finally, the batch mode regression framework based DSE is developed to formulate the protection logic considering errors of both measurements and historical estimates. Numerical experiments have shown that the proposed DSEBP scheme can dependably detect and trip internal faults, and securely ignore the external faults. The proposed scheme achieves improved reliability and operational speed as compared with the existing DSEBP schemes. The proposed scheme only requires a typical sampling rate of 10k samples per second, has the robustness to measurement noises, and the calculation burden is low enough for practical implementations.

Low power consumption, real-time computing, and high reliability are three key requirements/design objectives of real-time embedded systems. The standby-sparing technique can improve system reliability while it might increase the temperature of the system beyond safe limits. In this paper, we propose a thermal-aware standby-sparing (TASS) technique that aims at maximizing the Quality of Service (QoS) of soft real-time tasks, which is defined as a function of the finishing time of running tasks. The proposed technique tolerates permanent and transient faults for multicore real-time embedded systems while meeting the Thermal Safe Power (TSP) as the core-level power constraint, which avoids thermal emergencies in on-chip systems. Our TASS proposed method provides an opportunity to remove the overlaps of the execution of main and backup tasks to prevent extra power consumption due to applying the fault-tolerant technique. Meanwhile, in order to maximize the QoS, we employ a heterogeneous platform to execute the main tasks as soon as possible on high-performance cores with more power budget. Experiments show that our proposed method improves QoS up to 39.78% (on average by 18.40%) and reduces the peak power consumption and temperature by up to 40.21% and 15.47C (on average 28.31% and 13.60C), respectively, at runtime, while keeping the system reliability at the required level.

The Mobile Edge Computing (MEC) mitigates the bandwidth limitation between the edge server and the remote cloud by directly processing the large amount of data locally generated by the network of the internet of things (IoT) at the edge. To reduce redundant data transmission, this paper proposes a data collection scheme that only gathers the necessary data from IoT devices along a trajectory. Instead of using and transmitting location information (to preserve location anonymity), a virtual coordinate system called "distance vector of hops to anchors" (DV-Hop) is used. The proposed trajectory encoding algorithm uses ellipse and hyperbola constraints to encode the position of interest (POI) and the trajectory route to the POI. Sensors make routing decisions only based on the geometric constraints and the DV-Hop information, both of which are stored in their memory. The proposed DV-Hop updating algorithm enables the users to collect data in an IoT network with mobile nodes. The experiments show that in heterogeneous IoT networks, the proposed data collection scheme outperforms two other state-of-the-art topology-based routing protocols, called ring routing, and nested ring. The results also show that the proposed scheme has better latency, reliability, coverage, energy usage, and provide location privacy compared to state-of-the-art-schemes.

This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.

With the objective to eliminate the input current sensor in a totem-pole boost power factor corrector (TPFC) for its low-cost design, a novel discretized sampling-based robust control scheme is proposed in this work. The proposed control methodology proves to be beneficial due to its ease of implementation and its ability to support high frequency operation, while being able to eliminate one sensor, and thus enhancing reliability and cost-effectiveness. In addition to that, detailed closed loop stability analysis is carried out for the controller in discrete domain to ascertain brisk dynamic operation when subjected to sudden load fluctuations. To establish the robustness of the proposed control scheme, a detailed sensitivity analysis of the closed loop performance metrics with respect to undesired changes and inherent uncertainty in system parameters is presented in this paper. A comparison with the state-of-the-art (SOA) methods is provided, and conclusive results in terms of better dynamic performance are also established. To verify and elaborate on the specifics of the proposed scheme, a detailed simulation study is conducted, and the results show 25% reduction in response time as compared to SOA approaches. A 500W boost PFC prototype is developed and tested with the proposed control scheme to evaluate the steady state and dynamic performance.

Benefiting from the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment.

While a number of network systems are running under certain stress with a limited lifetime, it is still unknown how to predict the lifetime–stress relation of complex systems. We develop a percolation-based approach to build an operational lifetime–stress model for complex networks, which captures the spatial and temporal reliability characteristics of the system. In this article, the general analytical expression for the entire operational lifetime–stress relation has been presented, which suggests that the load stress and the number of nodes in the network impact the operational lifetime in the same manner. The size effect found here in the lifetime–stress relation is observed for the first time, to our best knowledge. For 2-D lattices, we show that the lifetime–stress relation can be regarded as the combination of two parts—an approximately linear region for small stress, and nonlinear region for large stress. We also analyze the lifetime–stress function of Beijing road network and the western United States power grid. Our article might help to develop acceleration testing methods, which will facilitate better design of reliable complex systems.

Travel time variability (TTV) is the key indicator used in assessing the service quality of bus transit system. This study explores the most appropriate model to describe the day-to-day TTV of bus section. By investigating a 7-month travel time data for 10 bus routes in Klang Valley, Malaysia, this study demonstrates that Burr distribution is the most promising model in describing bus TTV. Bus TTV is found to be sensitive to both temporal and spatial effect. This means that TTV service varies for weekdays and weekends (temporal). Also, it differs for the five operating environments (spatial) investigated in this study. The Burr regression analysis conducted in the second part of this study further confirmed that bus section length and traffic signal density are the major contributing factors to bus TTV. However, both factors have varying levels of impact under different spatiotemporal effect. For example, in the suburban and residential areas, these factors cause higher TTV on weekends but lesser during weekdays, while a vice versa impact is observed in the Central Business District. This distinguishes from earlier studies which purely assumed normality in the regression analysis while not emphasizing the importance of spatiotemporal factors on TTV. Thus, this study serves as an analysis tool that could be used in the planning of bus routes and schedules under varying bus operating environments and operation times.

This article proposes a supervised learning approach for centralized localization of faults in microgrids with radial configuration that can operate in grid-connected or islanded mode. The key concept is to identify faults by learning and analyzing the features of voltage and current disturbances detected by a microgrid central protection unit. Two algorithms are developed that determine fault indices and transient features for simulation-based training, classification, and backup protection purposes. The proposed data-driven approach is effective under high penetration of distributed energy resources (DERs), and is robust under microgrid topology variations. Moreover, it requires few sensors (two for radial structure) and is easy to implement, yet provides high degree of reliability. The performance and accuracy of the developed approach are verified through extensive simulations of low-voltage microgrids that incorporate different types of DERs, including wind and solar photovoltaic systems.

The location information of road and lane lines is the supremely important thing for the automatic drive and auxiliary drive. The detection accuracy of these two elements dramatically affects the reliability and practicality of the whole system. In real applications, the traffic scene can be very complicated, which makes it particularly challenging to obtain the precise location of road and lane lines. Commonly used deep learning-based object detection models perform pretty well on the lane line and road detection tasks, but they still encounter false detection and missing detection frequently. Besides, existing convolution neural network (CNN) structures only pay attention to the information flow between layers, while it cannot fully utilize the spatial information inside the layers. To address those problems, we propose an attention-based spatial segmentation network for traffic scene understanding. We use the convolutional attention module to improve the network's understanding capacity of spatial location distribution. Spatial CNN (SCNN) obtains through the information flow within one single convolutional layer and improves the spatial relationship modeling ability of the network. The experimental results demonstrate that this method effectively improves the neural network's application ability of the spatial information, thereby improving the effect of traffic scene understanding. Furthermore, a pixel-level road segmentation dataset called NWPU Road Dataset is built to help improve the process of traffic scene understanding.

Todays large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-organized that energy consumption accounts for a large part of a computing systems total cost, and timeliness and reliability are two important service indicators. This article studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.

A cloud–fog computing system is modeled as a network topology where each edge represents a transmission line, and each node represents a hub, an Internet of Things (IoT) device, a fog server, or a cloud server. Such a system with several capacities at each edge is called a stochastic-flow cloud–fog computing network (SCCN). There are three phases of transmitting data: the first is to transmit data from the IoT devices to the fog servers. The second and third phases are transmitting a part of the data from the fog servers to the cloud servers for the further operations and back to the IoT devices for the immediate operations, respectively. Based on the transmission mechanisms, two demands, including initial demands and processed demands, should be satisfied simultaneously, where an initial demand (a processed demand) is outing from an IoT device (a fog server). An algorithm is developed to evaluate reliability, the probability of successfully transmitting the data through the SCCN, by elucidating the flow relationship among the IoT devices, edge servers, and cloud servers. Furthermore, another approach to adjust the flow assignment is also proposed for the case that demand may not be an integer. A large real case is provided to validate the applicability and scalability of the proposed methodology.

Reliability evaluation of multiprocessor systems is of great significance to the design and maintenance of these systems. As two generalizations of traditional edge connectivity, extra edge connectivity and component edge connectivity are two important parameters to evaluate the fault-tolerant capability of multiprocessor systems. Fast identifying the extra edge connectivity and the component edge connectivity of high order remains a scientific problem for many useful multiprocessor systems. In this article, we determine the $h$ -extra edge connectivity of the 3-ary $n$ -cube $Q_n^3$ for $h\in [1, \frac{3^n-1}{2}]$ . Specifically, we divide the interval $[1, \frac{3^n-1}{2}]$ into some subintervals and characterize the monotonicity of $\lambda _h(Q_n^3)$ in these subintervals and then deduce a recursive closed formula of $\lambda _h(Q_n^3)$ . Based on this formula, an efficient algorithm with complexity $O(\log _3\,N)$ is designed to determine the exact values of $h$ -extra edge connectivity of the 3-ary $n$ -cube $Q_n^3$ for $h\in [1, \frac{3^n-1}{2}]$ completely. Moreover, we also determine the $g$ -component edge connectivity of the 3-ary $n$ -cube $Q_n^3(n\geq 6$ ) for $1\leq g\leq 3^{\lceil \frac{n}{2}\rceil }$ .

One of the most important part of a stand-alone photovoltaic system is energy management. This part uses several sensors to measure solar radiations, load power and battery energy to determine operation of the system components. Reduction the number of sensors and optimization of energy management system are two key steps to improve the efficiency and reliability of the system without additional cost. Hence, in this paper, a comprehensive energy management algorithm along with a complementary algorithm to estimate radiation under uniform and partial shading conditions is proposed. According to the system state, the energy management algorithm commands to activate/ deactivate a part or entire of photovoltaic system which can increase the system lifetime and thus increase the system reliability. Another feature of the proposed algorithm is a new mode in the absence of radiation for parallelizing and sharing the battery’s current to the load by unidirectional and bidirectional converters. Furthermore, a sensor-less complementary algorithm to determine the amount of radiation and also the partial shading condition is proposed. To prove the feasibility of the proposed algorithms, the system was simulated and constructed. The simulation and experimental results are found closely comparable and confirm the effectiveness of the proposed methods.

For interconnection network losing processors, it is considerable to calculate the number of vertices in the maximal component in the surviving network. Moreover, the component connectivity is a significant indicator for reliability of a network in the presence of failing processors. In this article, we first prove that when a set $M$ of at most $3n-7$ processors is deleted from an $n$ -star graph, the surviving graph has a large component of size greater or equal to $n!-|M|-3$ . We then prove that when a set $M$ of at most $4n-9$ processors is deleted from an $n$ -star graph, the surviving graph has a large component of size greater or equal to $n!-|M|-5$ . Finally, we also calculate the $r$ -component connectivity of the $n$ -star graph for $2\leq r\leq 5$ .

High voltage DC (HVDC) grids have raised high-speed operation and high reliability requirement for relay protection. This paper aims at proposing a protection scheme with high operation speed, high sensitivity, and high reliability. Firstly, the symbolic expressions of the line-mode fault component traveling wave in case of the internal and external faults are derived, which consist of the traveling wave propagation term and the DC line boundary term. It is theoretically demonstrated that (i) the index of the traveling wave propagation term in case of internal faults is greater than that for the external faults, and (ii) the index is independent of fault impedance. Secondly, the Levenberg-Marquardt (LM) algorithm is introduced to extract the index and an index-based non-unit protection method as well as the protection scheme is proposed. The proposed method is theoretically robust and independent of fault impedance and fault type. In the validation, the fitting results have indicated that the symbolic expressions are correct and the extracted indices are effective to characterize the location of the faults. Numerous fault cases of different fault distances, fault impedances, and fault types have demonstrated that the proposed protection scheme has high sensitivity, high operation speed, and high reliability.

Energy management strategy (EMS) of hybrid energy storage systems has an essential mission of ensuring safety, enhancing reliability and improving system efficiency. This paper focuses on optimizing sizing of HESS and parameters of EMS simultaneously. Firstly, an improved model is employed in adaptive predictive model control (AMPC). Secondly, in order to minimize the cost of supercapacitors and the capacity degradation of batteries at the same time, the multiple objective optimization problems are solved by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The nine variables are decided including seven parameters of AMPC and series-parallel number of supercapacitors. Subsequently, the support vector machine combined with the gray wolf optimization algorithm (GWO-SVM) is employed to enhance the applicability of EMS. Finally, in order to prove the superiority of the proposed EMS, a combined cycle and a real cycle are performed for validation. The results illustrate that the proposed method can improve the efficiency of the system and enhance the life span of the battery. Comparing with AMPC-only, the total energy loss can be reduced by at least 17.9%. A reduction of not less than 8.8% in RMS of battery current is obtained. The Ah-throughput can be reduced by at least 2.5%. In further discussions, the superiority of MOEA/D and GWO-SVM is proved by comparing with other methods.