GPU sharing is commonly employed in GPU clusters to improve utilization, with spatial sharing being one of the most widely adopted techniques. However, spatial sharing can lead to resource interference, making task execution times difficult to predict. Predictable execution times for each task are crucial in GPU cluster management and task scheduling. In this article, we propose a performance predictor for multi-DNN training tasks in GPU spatial sharing environments. We first conduct experiments on spatial sharing for multiple DNN workloads on a single GPU, demonstrating that concurrent execution of multiple tasks improves overall performance and GPU resource utilization compared to serial execution. By analyzing warp stall reasons collected during task execution, we investigate the interference for computation and memory resources under MPS on GPUs. Finally, we design a performance predictor that predicts the execution time of a target DNN training task when it runs concurrently with other tasks under GPU spatial sharing via MPS. The predictor is capable of predicting the execution time of each task for previously unseen combinations of DNN training tasks. Extensive evaluations on modern GPUs show that compared to other baseline methods, our approach exhibits higher prediction accuracy, as well as improved stability and robustness. Experiments on multiple GPU architectures, as well as at higher concurrency levels, further demonstrate that our method possesses strong generalization and scalability. We also conducted a performance analysis under diverse workload pattern and a case study to validate the practical applicability of our predictor in real scheduling environments.

The rapid growth of distributed energy resources has led to a shift toward consumer-centered electricity markets, where peer-to-peer (P2P) energy trading plays a key role. While distributed market clearing algorithms enable scalable P2P trading, they often rely on direct information exchange between all trading partners, which raises concerns about privacy and high communication overhead. To address this, we propose a fully distributed market clearing algorithm that is updated using only information from its communication neighbors, regardless of transactions relationships. To further preserve privacy, we design an enhanced version (PF-DMCA) by incorporating a lightweight privacy mechanism based on state perturbation and channel weakening. Simulation results show that both algorithms achieve convergence to a solution with less than 0.09% deviation from the optimal social welfare. Moreover, compared to existing methods, the PF-DMCA requires 85.8% of communication times to achieve convergence, while F-DMCA requires only 50.4%, demonstrating superior communication efficiency.

Training deep neural networks (DNNs) is a common task in GPU clusters. However, in practical cluster environments, multiple concurrent DNN training tasks often fail to fully leverage GPU resources, resulting in suboptimal GPU utilization. Furthermore, existing GPU sharing frameworks primarily rely on static scheduling and frequently overlook task deadlines, leading to task delays and inefficient scheduling. To address these issues, we propose a dynamic GPU sharing framework (DynGPU) that intercepts GPU kernel executions to perform resource scheduling in multi-task environments. DynGPU incorporates a dynamic task priority adjustment mechanism that adapts task priorities in real time based on task progress, historical data, and remaining time to deadlines. By guaranteeing resources for high-priority tasks while maximizing resource allocation for low-priority tasks, DynGPU reduces resource contention and improves system throughput, enabling more timely task completions. Experiments show that, compared to dedicated GPU execution, DynGPU can reserve up to 97.5 % of throughput for high-priority tasks. Compared to state-of-the-art baselines, DynGPU achieves up to an 8.4 % improvement in task completion time.

The Internet of Robotic Things (IoRT) merges the capabilities of robotics with the connectivity and computing power of Internet of Things (IoT) technologies, enabling seamless data collection, processing, and exchange. This integration enhances robotic systems with greater intelligence, mobility, and autonomy, unlocking significant potential across various applications, including sustainable agriculture. However, deploying IoRT systems in unpredictable environments poses challenges, such as network instability and hardware failures, which have not been thoroughly explored in the literature. To address these issues, this article introduces Mobility-as-a-Resilience Service (MaaRS), a model that leverages the mobility of active uncrewed aerial vehicles (UAVs), strategically relocating them to critical points of interest in response to potential data collection failures, optimizing resource allocation and enhancing system resilience, particularly in smart farm scenarios. Additionally, a robust multiagent deep deterministic policy gradient (RMADDPG) method is devised to enable efficient task allocation and system recovery in the presence of model uncertainty, observation noise, and reward uncertainty. Extensive simulations demonstrate that the proposed method achieved a significant boost in performance, efficiency, and stability over the state-of-the-art.

Feedback signal delays present a common challenge in the online decision-making processes of various real-world systems, such as real-time economic dispatch in power systems. These delays, primarily induced by limited computational capabilities or unknown parameter changes, can significantly hinder the performance or even effectiveness of online decision-making strategies. This study investigates distributed optimization over time-varying unbalanced networks with delayed feedback signals. We propose discrete-time distributed online algorithms for both constrained and unconstrained optimization problems. Each node in the network has access only to its individual time-varying local objective function with a delayed time sequence. At each decision-making step, each node selects the appropriate local online behavior, which depends on the information from its neighbor nodes, towards the minimization of global cumulative cost function value. We demonstrate that sublinear regrets can be ensured as long as the time-varying unbalanced communication networks maintain$\mathcal {B}$-strong connectivity. For a general convex or$\mu $-strongly convex local objective function, the network regret and individual regret grow as$\mathcal {O}((\ln (T))^{2})$and$\mathcal {O}(\sqrt {T})$, respectively, and are intrinsically related to the feedback delays and step sizes. To validate the proposed algorithms, numerical studies are carried out on constrained distributed time-varying economic dispatch problems.

By melding the capabilities of robotics with the agility of edge computing, Robotic Edge System (RES) exemplifies the next generation of Internet Intelligent Service Systems, delivering incredible efficiency and adaptability across diverse real world applications. Inevitably, RES is susceptible to mechanical disruptions of robots, particularly when some tasks are assigned to faulty ones, leading to uncertain failures and performance degradation. Due to communication and latency constraints, it is not always feasible to rely on edge/cloud computing infrastructure for system recovery. To address these issues, a UAV-as-a-Service (UAVaaS) approach is proposed that leverages the mobility of UAVs to enhance system resilience. Specifically, a Markov Decision Process (MDP) is utilized to assign tasks dynamically among active UAVs to achieve system recovery in a livestock monitoring scenario. Additionally, a Dual Noise Deep Deterministic Policy Gradient (DNDDPG)-based mechanism is proposed to minimize system recovery time and energy consumption. The proposed DNDDPG enhances exploration and decision-making during training by integrating parameter noise and behavioral noise into the classic Deep Deterministic Policy Gradient (DDPG) algorithm. The simulation results indicate that the proposed mechanism can achieve convergence within 100 episodes, thereby effectively minimizing the time and energy required for system recovery.

DC microgrids with battery energy storage systems are being widely implemented for integrating renewable energy. The convergence performance of the battery controller is an important index in the evaluation of the microgrids performance. However, the convergence time of existing finite-time control, fixed-time control, and predefined-time control cannot be preset explicitly. Moreover, existing state-of-charge (SoC)-equalization-based accelerating control algorithms will make the batteries suffer excessive voltage and current. To deal with these problems, this article presents a distributed prescribed-time control scheme embedded with a prescribed-time dynamic average consensus (DAC) algorithm for both discharging mode and charging mode. In discharging mode, the droop coefficient is designed such that all batteries keep the same relative SoC variation rate. With the proposed secondary control input, theoretical analysis shows that the voltage regulation and accurate current sharing can be obtained within any physically allowable user-preassigned time, which is independent of any other control parameters and initial states. SoC balancing is also achieved within the preassigned time. All batteries can keep the same relative SoC variation rate, which is more reasonable than simple SoC equalization. In charging mode, an SoC-based virtual resistance and a prescribed-time virtual voltage compensation control are proposed, with which the current sharing and the same relative SoC variation rate of each battery is achieved within the preassigned time. Simulation studies are conducted to demonstrate the effectiveness of the proposed control scheme

Incorporating edge and cloud computing with robotics provides extended options for robots to perform real-time sensing and actuation operations in various cyber–physical systems (CPSs), including smart farms. Such systems are prone to uncertain failures triggered by mechanical disruptions. Consequently, the overall system performance degrades, primarily when location-specific tasks are already assigned to a faulty robot and require immediate recovery. Using edge and cloud computing resources is not always feasible due to communication and latency constraints. Therefore, this article exclusively focuses on harnessing the mobility of robots to support the computation tasks affected by uncertain failures of previously assigned robots and ensure faster resiliency management by relocating active robots near task sources. The proposed mobility-as-a-resilience-service (MaaRS) is formulated using a Markov decision process (MDP). Later, an edge server proximal to the robots is trained using deep reinforcement learning (DRL) to assign tasks among the robots. Specifically, a multiple deep$Q$-network (MDQN)-based dynamic task allocation mechanism is proposed to converge to a solution exploring reward uncertainties with the best exploitation. Numerical evaluation using Python and TensorFlow validates the effectiveness of the proposed approach compared to other benchmarks.

Distributed optimization achieves a minimized objective function through collaboration among distributed agents. Considering limited communication capabilities and privacy concerns, this article proposes a dynamic event-triggered differentially private gradient-tracking algorithm for distributed optimization. The communication requirement is reduced by event triggering, while the$\epsilon$-differential privacy is guaranteed by perturbations on states and the tracking of the average gradient. The convergence point is uniquely determined by the noise injected to the tracking. Sufficient conditions for stepsizes are established theoretically to guarantee the convergence in mean and almost surely. Moreover, the theoretical privacy level is rigorously obtained and the positive effect of the event-triggered communication on the privacy is also discussed. Simulations are conducted for the classification of the dataset on the stability of a 4-node star power system to verify the theoretical findings.

BACKGROUND: The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. While it is possible to infer circadian gene regulatory relationships from time-series gene expression data, relying solely on correlation-based inference may not provide sufficient information about causation. Moreover, gene expression data often have high dimensions but a limited number of observations, posing challenges in their analysis. METHODS: In this paper, we introduce a new hybrid framework, referred to as Circadian Gene Regulatory Framework (CGRF), to infer circadian gene regulatory relationships from gene expression data of rats. The framework addresses the challenges of high-dimensional data by combining the fuzzy C-means clustering algorithm with dynamic time warping distance. Through this approach, we efficiently identify the clusters of genes related to the target gene. To determine the significance of genes within a specific cluster, we employ the Wilcoxon signed-rank test. Subsequently, we use a dynamic vector autoregressive method to analyze the selected significant gene expression profiles and reveal directed causal regulatory relationships based on partial correlation. CONCLUSION: The proposed CGRF framework offers a comprehensive and efficient solution for understanding circadian gene regulation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian activity. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research.

Abstract Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

Virtual network migration (VNM) helps improve network performance by remapping a subset of virtual nodes or links to physical infrastructure, aligning the resource allocation to the virtual network's changing conditions. However, existing VNM methods neglect integrating multiple objectives that affect network performance, such as energy, communication, migration, and service level agreement violation (SLAV). It is challenging to make VNM decisions to optimize the overall objective in a large-scale cloud environment. This article establishes a multi-objective optimization model and proposes a multi-objective VNM algorithm called MiOvnm. The MiOvnm employs the double deep$Q$-learning approach to cope with ample state space. It also applies an action selection method called actfilter to deal with large-scale action space. The MiOvnm finds the migration action with optimal potential reward from the candidate action set. Simulation results demonstrate the superiority of our MiOvnm to the state-of-the-art methods. More specifically, MiOvnm reduces average SLAV, communication cost, and total cost by 24.32%, 4.95%, and 12.45%, respectively. Furthermore, evaluation results in a real-world OpenStack platform reveal that making full use of computation and network resources, the MiOvnm reduces the completion time of computation- and network-intensive benchmarks by 11.35% and 10.31%, respectively, with a total cost reduction of 26.02%.

Neural dynamic surface control (NDSC) is an effective technique for the tracking control of nonlinear systems. The objective of this article is to improve closed-loop transient performance and reduce the number of learning parameters for a strict-feedback nonlinear system with unknown control gains. For this purpose, a predictor-based NDSC (PNDSC) approach is presented. It introduces Nussbaum functions and predictors into the traditional NDSC for nonlinear systems with unknown control gains. Unlike NDSC that uses surface errors to update the learning parameters of neural networks (NNs), the PNDSC employs prediction errors for the same purpose, leading to improved transient performance of closed-loop control systems. To reduce the number of learning parameters, the PNDSC is further embedded with the technique of the minimal number of learning parameters (MNLPs). This avoids the problem of the "explosion of learning parameters" as the order of the system increases. A Lyapunov-based stability analysis shows that all signals are bounded in the closed-loop systems under PNDSC embedded with MNLPs. Simulations are conducted to demonstrate the effectiveness of the PNDSC approach presented in this article.

Cyber-physical systems (CPSs) seamlessly integrate communication, computing, and control, thus exhibiting tight coupling of their cyber space with the physical world and human intervention. Forming the basis of future smart services, they play an important role in the era of Industry 4.0. However, CPSs also suffer from increasing cyber attacks due to their connections to the Internet. This article investigates resilient control for a class of CPSs subject to actuator attacks, which intentionally manipulate control commands from controllers to actuators. In our study, the supertwisting sliding-mode algorithm is adopted to construct a finite-time converging extended state observer (ESO) for estimating the state and uncertainty of the system in the presence of actuator attacks. Then, for the attacked system, a finite-time converging resilient controller is designed based on the proposed ESO. It integrates global fast terminal sliding-mode and prescribed performance control. Finally, an industrial CPS, permanent magnet synchronous motor control system, is investigated to demonstrate the effectiveness of the composite resilient control strategy presented in this article.

Advanced cyber-physical power systems (CPPS) has been put forward by the strong integration of energy networks and communication networks. While CPPS brings a promising solution with high efficiency, strong flexibility, great scalability, and improved reliability, it inevitably poses some security challenges. In order to address these challenges, it is essential to accurately describe the attack behavior and system security situation. In this article, a dynamic risk propagation evaluation approach is proposed for accurately predicting attacks and quantitatively analyzing system risk. It is equipped with a partitioned cellular automata model to deal with spatial heterogeneity in the partitioned system. The intentions of targeted attack are also considered for predicting attacks. Then, the cyber-to-physical risk is quantitatively identified from multiple dimensions. Finally, the verification of attack intention is designed to dynamically update and adjust the predicted result. The presented approach is demonstrated through a case study on a CPPS.

Due to the close relevance to the reliability and efficiency of power systems, network parameters such as branch admittance have been the target of various cyberattacks. However, existing attack models are generally based on the impractical assumption that attackers can directly modify the data of network parameter stored in well-secured control centers. This article proposes a practical attack model and designs an optimal strategy to detect malicious modification of critical network parameters. Specifically, the vulnerability of network parameter error processing is discovered and exploited to indirectly modify the data of network parameter without accessing to the well-secured control center. A model of false-data-injection-enabled network parameter modification is proposed, which significantly reduces the requirements on attackers’ capability and system information. An optimal detection strategy is designed based on the analysis of the minimal protection set at a single branch, which can significantly reduce the number of protected measurements in detecting malicious modification of critical network parameters. Finally, numerical simulations are carried out on the PJM 5-bus and the IEEE 118-bus test systems to validate the theoretical results.

Cyber attacks threaten the security of cyber-physical systems (CPSs) seriously. Resilient control has been studied to defend cyber attacks. However, existing resilient control schemes have not considered system structure changes caused by actuator attacks. Such structure changes are more destructive and harmful than the actuator attack scenarios investigated in the literature, demanding new resilient control strategies. They will be addressed in this article in cloud computing environments, which are increasingly deployed in large-scale CPSs. More specifically, a resilient control scheme is designed which consists of two controllers: a local resilient controller and cloud-based resilient controller. The local resilient controller withstands actuator attacks that simply tampers the actuator output to a large extent. The cloud-based resilient controller aims to resist the actuator attacks that destroy the system structure. Simulations are conducted on a permanent synchronous motor control system to demonstrate the proposed resilient control scheme.

Time synchronization utilizing the Global Navigation Satellite System (GNSS) is being increasingly investigated for vehicular networks. Due to GNSS signal blockages, the availability and accuracy of GNSS timing solutions in various road settings is a recognized challenge. With the recent improvement of Multi-GNSS technology and the increased capacity of consumer-grade receivers, the application of GNSS in vehicular environments has brightened up. This paper systematically analyzes the required time synchronization of vehicular networks and presents a GNSS-based time synchronization solution. It also experimentally demonstrates the availability and capabilities of GNSS time synchronization using commercial-grade GNSS receivers and off-the-shelf communication devices. Our experiments show that the timing accuracy of an individual vehicular node can be as good as ±2 microseconds, resulting in synchronization accuracy of sub-10 microseconds among nodes. A momentary complete outage of the GNSS time solution due to signal blockage on the road adds clock error, leading to synchronization inaccuracy of up to sub-20 microseconds. This level of inaccuracy still meets the desired requirement for most applications in vehicular communication.

In time series forecasting with outliers and random noise, parameter estimation in a neural network via minimizing the$l_{2}$loss is unreliable. Therefore, an adaptive rescaled lncosh loss function is proposed in this article to handle time series modeling with outliers and random noise. It overcomes the limitation of the single distribution of traditional loss functions and can switch among$l_{1}$,$l_{2}$, and the Huber losses. A tuning parameter in the loss function is estimated by using a “working” likelihood approach according to estimated residuals. From the proposed loss function, a robust adaptive rescaled lncosh neural network (RARLNN) regression model is developed for highly accurate predictions. In the training phase of the model, an iterative learning procedure is presented to estimate the tuning parameter and train the neural network in iterations. A new prediction interval construction method is also developed based on quantile theory. The proposed RARLNN model is applied to two groups of wind speed forecasting tasks. The results show that the proposed RARLNN model is more conducive to enhancing forecasting accuracy and stability from the perspectives of noise distribution and outliers.

Many problems in industrial smart manufacturing, such as process operational optimization and decision-making, can be regarded as distributed nonconvex optimization problems, whose goal is to utilize distributed nodes to cooperatively search for the minimal value of the global objective function. With the consideration of data transmission mode, transmission condition, and communication waste in industrial applications, it is meaningful to study the distributed nonconvex optimization problem with an event-triggered strategy over time-varying directed networks. To solve such a problem, a distributed nonconvex event-triggered algorithm is proposed in this article. Under some assumptions on local objective functions, gradients, and step sizes, the convergence of the proposed event-triggered algorithm to the local minimum is established theoretically. Moreover, it is obtained that the proposed distributed event-triggered algorithm has a convergence rate of$O(1/\ln (t))$. Finally, two examples of industrial systems are provided to validate the effectiveness of the proposed algorithm.

With the increasing integration of intermittent energy sources into the smart grid, distributed battery energy storage systems (DBESSs) are employed to balance power generation and demand. Power allocation among DBESSs plays an important role in maintaining the stability of energy systems. So far, the control of DBESSs has focused on either continuous-time control for continuous-time battery dynamics or discrete-time control for discretized battery dynamics. However, in realistic industrial applications, DBESSs have continuous-time dynamics in nature, and their control is implemented on digital controllers. To tackle this issue, a distributed multirate control system is designed in this article for continuous-time DBESSs. It allocates power by keeping the same relative State-of-Charge (SoC) variation rate for all DBESSs. For the accurate computation of the output/input power without global information, the control strategy consists of distributed multirate estimators each for a DBESS. The operating rate of the estimation algorithm is designed multiple times higher than the sampling rate of the measurement of battery and the sampling rate of the battery controller. With the proposed multirate estimator, a smaller estimation error is achieved. The same relative SoC variation rate and the stability of closed-loop system can be guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed control strategy.

Data-driven industrial manufacturing services are proliferating. They use large amounts of data generated from Industrial-Internet-of-Things (IIoT) devices for intelligent services to end-service-users. However, cloud data centers hosting these services consume a huge amount of energy, resulting in a high operational cost. To address this issue, an energy-efficient resource allocation framework is proposed in this article for cloud services. It operates in two phases. First, a multithreshold-based host CPU utilization classification scheme is developed to classify hosts into four groups for improved CPU resource allocation. It is designed through analyzing CPU utilization data by using the least median squares regression technique. Thereby, the scheme limits search space, thus reducing time complexity. In the second phase, with a metaheuristic search, an energy- and thermal-aware resource allocation method is developed to find an energy-efficient host for allocating resources to services. From real data center workload traces, extensive experiments show that our framework outperforms existing baseline approaches with 6.9%, 33.75%, and 34.1% on average in terms of temperature, energy consumption, and service-level-agreement violation, respectively.

Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and also designs resilient controllers based on complex nonlinear system models caused by unknown actuator and sensor attacks. This increases the difficulty in the analysis, computation, and control of CPSs under attacks. To address this issue, this article introduces an idea to deal with both actuator attacks and sensor attacks together with feedback linearization control. This simplifies the mathematical modeling of attacked CPSs, thus reducing the difficulty of resilient controller design. Then, from the simplified modeling, a composite controller is designed to enhance system resilience. It ensures the dynamic and steady-state performance of CPSs under attacks. Simulation studies are undertaken to demonstrate the effectiveness of the proposed method.

The use of cloud services to process a large amount of data is growing and demands for scalable, reliable, and highly available services in cloud environments are raising. The demands and the urge for developing these facilities have made parallel computing more appealing. Cloud providers offer various types of Virtual Machines (VMs) that are compatible with parallel processing and the clients should pay for their hourly usage. The price varies based on the type, the number and the hiring time of the VMs. A daily price fluctuation timetable has been proposed and scaling the number of VMs on that helps to schedule the computations to meet both deadline and cost minimization goals. It becomes critical to select appropriate VMs and distribute workload efficiently across them. Therefore, the computations and the VMs require being well-managed, scheduled and monitored to meet the deadline while minimizing the total hiring cost. To address these concerns, this paper formulated the problem to calculate the total hiring cost before and during the computations. The execution time and the total cost are calculated based on the application's input size and the required type and the number of VMs. We worked on two applications as sample benchmarks to identify the best approach to choose and manage the VMs to compute them. Our results indicate that among different available approaches for hiring VMs, identifying the most affordable approach leads to minimizing the cost signiflcantly.

With the rapid development of functional requirements in the emerging Industry 4.0 era, modern industrial control systems (ICSs) are no longer isolated islands, making them more vulnerable to various cyberattack threats. Cyberattacks on ICSs may have disruptive consequences, such as significant social and economic losses. To proactively address the security issue of ICSs, this article presents a unified architectural approach from the perspectives of cyberthreats on ICSs, security-related ICS technologies, and methods for ICSs. It incorporates secure networks, secure control systems, secure physical processes, and their interactions seamlessly into a unified framework. To increase the resistance of ICSs against intrusions, the network security in our architectural approach is to secure the data in motion through the integration of secure network architecture, secure industrial network protocols, and secure end-to-end communications. The protection of control systems in our architectural approach is risk-based and hierarchical and encompasses prevention- and tolerance-centric defenses. It provides a layer-by-layer defense so that an acceptable level of cybersecurity risk is achieved and maintained. Aiming to maintain the stable operation of physical ICS processes, the secure control in our architectural approach implements a security process against process-aware attacks through a resilient safety control scheme. The global and systematic architectural approach presented in this article for the ICS cybersecurity will help facilitate the design and implementation of cyberattack-resilient ICSs in the networked world. For further development of ICS security technologies, emerging challenges are identified and discussed to motivate future research efforts.

Formation tracking is a critical issue in the consensus control of multiagent systems (MASs). This article presents a time-varying formation tracking strategy with predefined performance for a class of uncertain nonaffine nonlinear MASs connected through a directed topology. The nonaffine nonlinear MASs are transformed into affine nonlinear ones with uncertainties via the idea of active disturbance rejection control (ADRC). The uncertainties in the MASs are approximated and compensated by extended state observers (ESOs) in real time. Tracking differentiators (TDs) are introduced to reduce the complexity in the computation of the derivatives of virtual control variables. Employing funnel variables, our strategy guarantees the formation of tracking errors to stay within the desired ranges, thus improving the control performance of the closed-loop system. It is proved that all signals of the system are bounded and the formation errors can be made arbitrarily small within a residue around the origin by appropriate choices of control parameters. Case studies are carried out to demonstrate the effectiveness of the proposed control strategy.Note to Practitioners—The motivation of this article is to present a time-varying formation tracking strategy with predefined performance for a class of uncertain nonaffine nonlinear MASs within a directed topology. To simplify the process of solving the formation tracking problem, the presented strategy incorporates ADRC with the backstepping technique. Employing ADRC, our strategy approximates the uncertainties of the MAS followers via ESOs. The uncertainties are then compensated through real-time estimations of extended states. Moreover, with ADRC, TDs are used to estimate the derivatives of complex nonlinear functions, eliminating the requirement of the operations of higher order derivatives of virtual control variables. It provides a feasible strategy for industrial applications.

Consensus control of multiagent systems (MASs) has applications in various domains. As MASs work in networked environments, their security control becomes critically desirable in response to various cyberattacks, such as denial of service (DoS). Efforts have been made in the development of both time- and event-triggered consensus control of MASs. However, there is a lack of precise calculation of control input during the attacking periods. To address this issue, a distributed secure consensus control with event triggering is developed for linear leader-following MASs under DoS attacks. It is designed with a dual-terminal event-triggered mechanism, which schedules information transmission through two triggered functions for each follower: one on the measurement channel (sensor-to-controller) and the other on the control channel (controller-to-actuator). To deal with DoS attacks, the combined states in the triggered functions are replaced by their estimations from an observer. Sufficient conditions are established for the duration and frequency of DoS attacks. To remove continuous monitoring of the measurement errors, a self-triggered secure control scheme is further developed, which combines the system states and other information at past triggered instants. Theoretical analysis shows that the followers in MASs under DoS attacks are able to track the leader and meanwhile the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of our distributed secure consensus control of MASs.

Voltage regulation is imperative for the successful operation of electricity distribution networks, especially with a high penetration level of photovoltaic (PV) systems. Power compensation control (PCC) that uses both reactive power compensation and active power curtailment has shown promising results in alleviating voltage rise problems. It crucially relies on real-time communications among distributed PV systems. However, the transmission of state measurements and control signals in PCC is hampered by inevitable communication delays. Therefore, it is important to not only estimate the maximum tolerable communication delay (MTCD) but also develop an alternative technique for PCC under abnormal communication delay (ACD) conditions. This article presents a delay-tolerant predictive PCC for voltage regulation in distribution feeders. After estimating the MTCD based on voltage and power mutation, it uses normal PCC for effective operation when communication delay is within MTCD, or switches to predictive PCC under ACD conditions. An accurate prediction is achieved using a double neural network with online adjustment of weights and samples. Simulations on a sample distribution network demonstrate the effectiveness of our presented approach.

Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors.

Multimicrogrid (MMG) systems play an increasingly important role in the smart grid. They come with various potential cyberattacks, which may cause power supply interruption or even human casualties. Therefore, decision-making for timely mitigation of cyberattack risks is highly desirable in the security protection of power systems. However, there is a lack of effective decentralized decision-making strategies that are able to deal with MMG scenarios through distributed consensus. To address this issue, a decentralized consensus decision-making (DCDM) approach is proposed in this article for the security of MMG systems. It achieves decentralized consensus without the need of a trusted authority or central server, making it distinct from existing consensus methods. Meanwhile, it guarantees the consistency and nonrepudiability of consensus results, which are stored on the blockchain in sequence. In each of the distributed agents, the approach consists of a fuzzy static Bayesian game model (FSB-GM) to determine the optimal security strategy and a hybrid consensus algorithm to achieve consensus. The FSB-GM considers the fuzzy preferences of different types of attackers and defenders. The hybrid consensus algorithm is implemented by the fusion improvement of two consensus mechanisms in the blockchain. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

Containment control is an important issue in the consensus problem of multiagent systems (MASs). This article presents an output-based containment control strategy for a class of nonaffine nonlinear MASs with uncertainies and directed topology. With the help of differential homeomorphism transform and the idea of active disturbance rejection control (ADRC), a nonaffine nonlinear MAS is transformed into an affine one with uncertainties. Two filters with extended states in each follower are designed with the idea of extended state observer to reconstruct the states of the transformed MAS. Then, uncertainties of the MAS are compensated with the help of the estimations of the extended states. Moreover, the derivative of the virtual control signal in the backstepping technique is replaced by a tracking differentiator (TD), overcoming the so-called “explosion of complexity” problem. By means of Lyapunov theory, the containment errors of the followers are proven to converge to a small neighborhood around the origin via an appropriate choice of parameters. Simulation examples are provided to demonstrate the proposed control strategy.

The 3GPP has provided its first standard specifications for network slicing in the recent Release 15. The fundamental principles are specified which constitute the standard network slicing framework. These specifications, however, lack the session continuation mechanisms among slices, which is a fundamental requirement to achieve inter-slice mobility. In this paper, we propose three solutions which enable session continuation among slices in the current 3GPP network slicing framework. These solutions are based on existing, well-established standard mechanisms. The first solution is based on the Return Routability/Binding Update (RR/BU) procedure of the popular Internet standard, Mobile IPv6 (MIPv6). The second solution is based on the 3GPP standard GPRS Tunnelling Protocol User Plane (GTPv1-U), which establishes a GTP tunnel between previous and new slice for session continuation. The third solution is a hybrid solution of both MIPv6-RR/BU and GTPv1-U protocols. We compare the performance of all these solutions through analytical modelling. Results show that the GTPv1-U based and the hybrid MIPv6/GTPv1-U solutions promise lower service disruption latency, however, incur higher resource utilization overhead compared to MIPv6-RR/BU and 3GPP standard PDU Session Establishment process.

Neighborhood area networks (NANs) are essential communication infrastructure in smart grid. They support communications for various applications including time-critical ones. A typical NAN communication scenario is to send commands from a control center simultaneously to a large number of nodes, demanding low-latency broadcast communications. This is challenging due to the limited bandwidth and large number of nodes in wireless NANs. While some broadcast schemes, e.g., opportunistic flooding, have been developed for general wireless sensor networks, they are not optimized for smart grid NANs with unique characteristics and low-latency requirements for time-critical applications. Therefore, a constrained broadcast scheme with minimized latency (CBS-ML) is presented in this paper for low-latency NAN communications. To avoid traffic congestion, it constrains the broadcast to a small number of core nodes. Theoretical developments are presented to show how to select core nodes based on network topology and link reliability. Simulations are conducted to demonstrate the proposed CBS-ML.

Industrial control systems (ICSs) in networked environments face severe cyber-security risks and challenges. A timely response to cyber-attacks is of paramount importance for mitigating risks. However, the security policy developed for an ICS may be conflicting with the ICS's safety policy, on which much attention has been paid for a long time in industrial control. An inappropriate enforcement of the security policy may deteriorate the ICS performance or even result in severe unexpected consequences. To tackle this problem, a risk-based security task scheduling approach is presented for ICSs with consideration of the safety policy. It ensures a timely response to cyber-attacks without compromising safety. More specifically, the approach reconciles security tasks and safety tasks according to a designed resolution policy, so as to acquire contradiction-free security and safety (S&S) tasks. Then, a real-time risk assessment method is developed to characterize the subtle change of the system risk with the implementation of the reconciled S&S tasks. After that, a task scheduling method is designed with the risk as the optimization objective, i.e., it searches the optimal task scheduling scheme by minimizing the risk posture. The resulting scheduling scheme ensures the smooth implementation of the S&S policy, which reflects the optimal recovery process against the risk. Finally, case studies on a hardware-in-the-loop testbed are conducted to demonstrate the effectiveness of the proposed approach.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

MapReduce is a major computing model for big data solutions through distributed virtual computing environment. Cloud container environment is one of the platforms to compute MapReduce tasks. However, a new challenge lies on the lack of resource provisioning for containerized MapReduce computations with deadline requirements. There are two major resource provisioning strategies to solve this challenge: static and dynamic, but neither of them can satisfactorily solve it. This paper presents a resource provisioning framework, integrating semi-static and dynamic strategies, to address this challenge. The framework includes a performance model to estimate minimum resource requirements under deadline limitation, and a scheduler to adjust resource allocation. Experimental results show that the proposed semi-static framework can complete the MapReduce computation with less resource utilization and meeting the given deadline. However, proposed dynamic resource provisioning is not suitable for our scenario caused by resource overhead and late completion.

The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.

Industrial control systems (ICSs) are facing more and more cybersecurity issues, leading to increasingly severe risks in critical infrastructure. To mitigate risks, developing an appropriate security strategy is of paramount importance. However, existing efforts on decision making in ICSs inherit some limitations, such as the lack of consideration of the strategy for securing both cyber and physical domains and a tradeoff between security and system requirements. To overcome these limitations, a decision-making approach is presented in this paper for intrusion response in ICSs. Aiming to determine the optimal security strategy against attacks promptly, it tries to secure the most “dangerous” attack paths and respond to functional failures. In this approach, measures that cover both cyber and physical domains are designed with in-depth analysis of attack propagation. They ensure the completeness of candidate security strategy space. A number of Pareto optimal solutions are determined from the strategy space through multiobjective optimization. The objective is to maximize the objective vector composed of security benefit, system benefit, and state benefit. Then, these solutions are prioritized by using a distance-based evaluation method, which pursues the optimal protection ability by making the objective vector of the selected strategy closest to the ideal one. The effectiveness of the proposed approach is demonstrated with a case study on a simulated process control system.

Multimicrogrid (MMG) systems have the potential to play an increasingly important role in the transformation of existing power grid to smart grid. However, the open and distributed connectivity of MMGs exposes the systems into various cyber-attacks, which may cause serious failures or physical damages, such as power supply interruption and human casualties. Therefore, ensuring the security of MMGs is of paramount importance. To address this issue, a new collaborative intrusion detection (CID) approach using blockchain is proposed in this paper for MMG systems in smart grid. Due to the consensus mechanism of blockchain, the approach is designed without the need of a trusted authority or central server while improving the accuracy of intrusion detection in a collaborative way. It is equipped with a proposal generation method that combines periodic and trigger patterns to generate the detection target of CID, i.e., a proposal. From the generated proposals together with the correlation model of MMGs, a CID is achieved by using the consensus mechanism. The final detection results of CID are stored on blockchain in sequence. The use of an incentive mechanism motivates a single microgrid to participate in consensus. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

Modern power grid is increasingly integrated with battery energy storage systems (BESSs). This paper deals with the problem of state-of-charge (SoC) balance control for multiple distributed BESSs in smart grid. The BESSs are expected to work cooperatively to not only fulfil the overall power requirement but also meet the constraints of the same relative SoC variation rate. To achieve this objective, a distributed SoC balance control approach is presented with event-triggered signal transmissions. It is designed with the dynamic average consensus (DAC) mechanism for parameter estimations. The DAC enables distributed control of each BESS through communicating with its neighboring BESSs. Different from traditional periodic signal transmission, the event-triggered signal transmission embedded in our approach allows each BESS to transmit signal to its neighboring BESSs only when needed, thus reducing the communication traffic. Theoretical lower bounds are established for consecutive interevent intervals such that the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of the presented approach.

In smart grid, demand-side communications play a significant role in real-time applications, such as demand response and advanced metering. With recent progresses on Internet-of- Things (IoTs), different communication technologies are available for the demand side, such as ZigBee, Bluetooth, Wi-Fi, etc. To improve the inter-operability of various IoTs in demand side of smart grid, ZigBee and Homeplug Alliances have jointly developed the Smart Energy Profile 2.0 (SEP 2.0)as the upper-layer communication protocol, which has been approved as an international standard, namely IEEE 2030.5. Despite of its promising application in demand side, the development on constrained IoT devices is challenging. To address this issue, an upper middleware for SEP 2.0 standard is developed based on the SimpleLink Wi- Fi technology of Texas Instruments (TI). It enables SimpleLink Wi-Fi devices to support SEP 2.0-based communications in the demand side of smart grid. In the end, an experimental system is built to demonstrate the effectiveness of the presented upper middleware.

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy efficiency improvement for data centers. Among various methods to solve the VM placement problem, genetic algorithm (GA) has been well accepted for its quality of solutions. However, GA is also computationally demanding, particularly in its fitness, limiting further improvement in energy efficiency of data centers in the scenarios where a fast solution is required. To address this issue, this paper formulates the VM placement problem for energy efficiency as a constrained optimization problem. Then, employing GA to solve the optimization, it presents an approach for efficient computation of GA fitness function. The improved computational efficiency is achieved through a new data structure design, which reduces the complexity of the computation from quadratic to linear, to the input size of the problem. Experimental studies show a huge computation time saving from our approach over the existing technique, which is basically Brute-force.

Quality of Service (QoS) is a new issue in cloud-based MapReduce, which is a popular computation model for parallel and distributed processing of big data. QoS guarantee is challenging in a dynamical computation environment due to the fact that a fixed resource allocation may become under-provisioning, which leads to QoS violation, or over-provisioning, which increases unnecessary resource cost. This requires runtime resource scaling to adapt environmental changes for QoS guarantee. Aiming to guarantee the QoS, which is referred as to hard deadline in this work, this paper develops a theory to determine how and when resource is scaled up/down for cloud-based MapReduce. The theory employs a nonlinear transformation to define the problem in a reverse resource space, simplifying the theoretical analysis significantly. Then, theoretical results are presented in three theorems on sufficient conditions for guaranteeing the QoS of cloud-based MapReduce. The superiority and applications of the theory are demonstrated through case studies.

With the evolution of information, communications, and technologies, modern industrial control systems (ICSs) face more and more cybersecurity issues. This leads to increasingly severe risks in critical infrastructure and assets. Therefore, risk analysis becomes a significant yet not well investigated topic for prevention of cyberattack risks in ICSs. To tackle this problem, a dynamic impact assessment approach is presented in this paper for risk analysis in ICSs. The approach predicts the trend of impact of cybersecurity dynamically from full recognition of asset knowledge. More specifically, an asset is abstracted with properties of construction, function, performance, location, and business. From the function and performance properties of the asset, object-oriented asset models incorporating with the mechanism of common cyberattacks are established at both component and system levels. Characterizing the evolution of behaviors for single asset and system, the models are used to analyze the impact propagation of cyberattacks. Then, from various possible impact consequences, the overall impact is quantified based on the location and business properties of the asset. A special application of the approach is to rank critical system parameters and prioritize key assets according to impact assessment. The effectiveness of the presented approach is demonstrated through simulation studies for a chemical control system.

With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs. Dynamic cybersecurity risk assessment plays a vital role in ICS cybersecurity protection. However, it is difficult to build a risk propagation model for ICSs due to the lack of sufficient historical data. In this paper, a fuzzy probability Bayesian network (FPBN) approach is presented for dynamic risk assessment. First, an FPBN is established for analysis and prediction of the propagation of cybersecurity risks. To overcome the difficulty of limited historical data, the crisp probabilities used in standard Bayesian networks are replaced in our approach by fuzzy probabilities. Then, an approximate dynamic inference algorithm is developed for dynamic assessment of ICS cybersecurity risk. It is embedded with a noise evidence filter in order to reduce the impact from noise evidence caused by system faults. Experiments are conducted on a simplified chemical reactor control system to demonstrate the effectiveness of the presented approach.

Time synchronization is critical for the operation of distributed systems in networked environments. It is also demanded in vehicular ad-hoc networks (VANETs), which, as a special type of wireless networks, are becoming increasingly important for emerging cooperative intelligent transport systems. Global navigation satellite system (GNSS) is a proven technology to provide precise timing information in many distributed systems. It is well recognized to be the primary means for vehicle positioning and velocity determination in VANETs. However, GNSS-based time synchronization is not well understood for its role in the coordination of various tasks in VANETs. To address this issue, this paper examines the requirements, potential benefits, and feasibility of GNSS time synchronization in VANETs. The availability of GNSS time synchronization is characterized by almost 100% in our experiments in high-rise urban streets, where the availability of GNSS positioning solutions is only 80%. Experiments are also conducted to test the accuracy of time synchronization with 1-PPS signals output from consumer-grade GNSS receivers. They have shown 30-ns synchronization accuracy between two receivers of different models. All these experimental results demonstrate the feasibility of GNSS time synchronization for stringent VANET applications.

Continuous-time semi-Markovian jump neural networks (semi-MJNNs) are those MJNNs whose transition rates are not constant but depend on the random sojourn time. Addressing stochastic synchronization of semi-MJNNs with time-varying delay, an improved stochastic stability criterion is derived in this paper to guarantee stochastic synchronization of the response systems with the drive systems. This is achieved through constructing a semi-Markovian Lyapunov-Krasovskii functional together as well as making use of a novel integral inequality and the characteristics of cumulative distribution functions. Then, with a linearization procedure, controller synthesis is carried out for stochastic synchronization of the drive-response systems. The desired state-feedback controller gains can be determined by solving a linear matrix inequality-based optimization problem. Simulation studies are carried out to demonstrate the effectiveness and less conservatism of the presented approach.

The dramatically increasing energy consumption of data centers is an important issue and one of the most efficient ways to tackle the issue is through server consolidation. The basic idea of server consolidation is to move all virtual machines (VMs) to as few energy efficient servers as possible, and then switch off unused servers. Many efficient server consolidation approaches have been proposed and one of the most efficient approaches is to use a Genetic Algorithm (GA) to find an optimal or near-optimal solution to the server consolidation problem. Aiming at reducing the computation time and the number of VM migrations incurred by server consolidation, this paper proposes a Decrease- and-Conquer Genetic Algorithm (DCGA). This DCGA adopts a decrease-and-conquer strategy to decrease the problem size and to decrease the number of VM migrations without significantly compromising the quality of solutions. The DCGA is compared with a classical GA and the most popular approach, namely FFD, for the server consolidation problem by experiments and the experimental results show that the DCGA can find a solution very close to the solution generated by the classical GA with much shorter computation time and incur much less VM migrations for all the test problems, and that the DCGA can generate a much better solution than the FFD.

Reliable data transmissions are challenging in industrial wireless sensor networks as channel conditions change over time. Rapid changes in channel conditions require accurate estimation of the routing path performance and timely update of the routing information. However, this is not well fulfilled in existing routing approaches. Addressing this problem, this paper presents combined global and local update processes for efficient route update and maintenance, and incorporates them with a hierarchical proactive routing framework. While the global process updates the routing path with a relatively long period, the local process with a shorter period checks potential routing path problems. A theoretical modeling is developed to describe the processes. Through simulations, the presented approach is shown to reduce end-to-end delay up to 30 times for large networks, while improving packet reception ratio (PRR) in comparison with hierarchical and proactive routing protocols ROL/NDC, DSDV, and DSDV with IPv6 Routing Protocol for Low-Power and Lossy Networks' Trickle algorithm. Compared with reactive routing protocols AODV and Ad Hoc On-demand Multipath Distance Vector, it provides similar PRR while reducing end-to-end delay over 15 times.

Mobile applications are being increasingly deployed on a massive scale in various mobile sensor grid database systems. With limited resources from the mobile devices, how to process the huge number of queries from mobile users with distributed sensor grid databases becomes a critical problem for such mobile systems. While the fundamental semantic cache technique has been investigated for query optimization in sensor grid database systems, the problem is still difficult due to the fact that more realistic multidimensional constraints have not been considered in existing methods. To solve the problem, a new semantic cache scheme is presented in this paper for location-dependent data queries in distributed sensor grid database systems. It considers multidimensional constraints or factors in a unified cost model architecture, determines the parameters of the cost model in the scheme by using the concept of Nash equilibrium from game theory, and makes semantic cache decisions from the established cost model. The scenarios of three factors of semantic, time, and locations are investigated as special cases, which improve existing methods. Experiments are conducted to demonstrate the semantic cache scheme presented in this paper for distributed sensor grid database systems.

Wide Area Control is a new technology to maintain the power system stability; however, it requires the support of short-latency communication. The existing communication technology does not have the abilities to provide this support. It highlights the demand for new and short-latency communication technologies. The aim of this paper is to provide communication challenges of the wide area control system, to understand the communication requirements of the future energy system and to realize the existing network technologies that are applicable to energy management.

As one of the most widely used wireless network technologies, IEEE 802.11 wireless local area networks (WLANs) have found a dramatically increasing number of applications in soft real-time networked control systems (NCSs). To fulfill the real-time requirements in such NCSs, most of the bandwidth of the wireless networks need to be allocated to high-priority data for periodic measurements and control with deadline requirements. However, existing quality of service (QoS)-enabled 802.11 medium access control (MAC) protocols do not consider the deadline requirements explicitly, leading to unpredictable deadline performance of NCS networks. Consequentially, the soft real-time requirements of the periodic traffic may not be satisfied, particularly under congested network conditions. This paper makes two main contributions to address this problem in wireless NCSs. A deadline-constrained MAC protocol with QoS differentiation is presented for IEEE 802.11 soft real-time NCSs. It handles periodic traffic by developing two specific mechanisms, a contention-sensitive backoff mechanism and an intra-traffic-class QoS differentiation mechanism. A theoretical model is established to describe the deadline-constrained MAC protocol and evaluate its performance of throughput, delay, and packet-loss ratio in wireless NCSs. Numerical studies are conducted to validate the accuracy of the theoretical model and to demonstrate the effectiveness of the new MAC protocol.

The requirement of distributed computing of all-to-all comparison (ATAC) problems in heterogeneous systems is increasingly important in various domains. Though Hadoop-based solutions are widely used, they are inefficient for the ATAC pattern, which is fundamentally different from the MapReduce pattern for which Hadoop is designed. They exhibit poor data locality and unbalanced allocation of comparison tasks, particularly in heterogeneous systems. The results in massive data movement at runtime and ineffective utilization of computing resources, affecting the overall computing performance significantly. To address these problems, a scalable and efficient data and task distribution strategy is presented in this paper for processing large-scale ATAC problems in heterogeneous systems. It not only saves storage space but also achieves load balancing and good data locality for all comparison tasks. Experiments of bioinformatics examples show that about 89% of the ideal performance capacity of the multiple machines have be achieved through using the approach presented in this paper.

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity can be achieved in multiple machines through using the approach presented in this paper.

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

An Application Specific Instruction-set Processor (ASIP) is a specialized processor tailored to run a particular application/s efficiently. However, when there are multiple candidate applications in the application's domain it is difficult and time consuming to find optimum set of applications to be implemented. Existing ASIP design approaches perform this selection manually based on a designer's knowledge. We help in cutting down the number of candidate applications by devising a classification method to cluster similar applications based on the special-purpose operations they share. This provides a significant reduction in the comparison overhead while resulting in customized ASIP instruction sets which can benefit a whole family of related applications. Our method gives users the ability to quantify the degree of similarity between the sets of shared operations to control the size of clusters. A case study involving twelve algorithms confirms that our approach can successfully cluster similar algorithms together based on the similarity of their component operations.

Real-time networked control systems (NCSs) over data networks are being increasingly implemented on a massive scale in industrial applications. Along with this trend, wireless network technologies have been promoted for modern wireless NCSs (WNCSs). However, popular wireless network standards such as IEEE 802.11/15/16 are not designed for real-time communications. Key issues in real-time applications include limited transmission reliability and poor transmission delay performance. Considering the unique features of real-time control systems, this paper develops a conditional retransmission enabled transport protocol (CRETP) to improve the delay performance of the transmission control protocol (TCP) and also the reliability performance of the user datagram protocol (UDP) and its variants. Key features of the CRETP include a connectionless mechanism with acknowledgment (ACK), conditional retransmission and detection of ineffective data packets on the receiver side.

This correspondence paper addresses the problem of output feedback stabilization of control systems in networked environments with quality-of-service (QoS) constraints. The problem is investigated in discrete-time state space using Lyapunov's stability theory and the linear inequality matrix technique. A new discrete-time modeling approach is developed to describe a networked control system (NCS) with parameter uncertainties and nonideal network QoS. It integrates a network-induced delay, packet dropout, and other network behaviors into a unified framework. With this modeling, an improved stability condition, which is dependent on the lower and upper bounds of the equivalent network-induced delay, is established for the NCS with norm-bounded parameter uncertainties. It is further extended for the output feedback stabilization of the NCS with nonideal QoS. Numerical examples are given to demonstrate the main results of the theoretical development.

Wireless network technologies, such as IEEE 802.11 based wireless local area networks (WLANs), have been adopted in wireless networked control systems (WNCS) for real-time applications. Distributed real-time control requires satisfaction of (soft) real-time performance from the underlying networks for delivery of real-time traffic. However, IEEE 802.11 networks are not designed for WNCS applications. They neither inherently provide quality-of-service (QoS) support, nor explicitly consider the characteristics of the real-time traffic on networked control systems (NCS), i.e., periodic round-trip traffic. Therefore, the adoption of 802.11 networks in real-time WNCSs causes challenging problems for network design and performance analysis. Theoretical methodologies are yet to be developed for computing the best achievable WNCS network performance under the constraints of real-time control requirements. Focusing on IEEE 802.11 distributed coordination function (DCF) based WNCSs, this paper analyses several important NCS network performance indices, such as throughput capacity, round trip time and packet loss ratio under the periodic round trip traffic pattern, a unique feature of typical NCSs. Considering periodic round trip traffic, an analytical model based on Markov chain theory is developed for deriving these performance indices under a critical real-time traffic condition, at which the real-time performance constraints are marginally satisfied. Case studies are also carried out to validate the theoretical development.

Discrete event-driven simulations of digital communication networks have been used widely. However, it is difficult to use a network simulator to simulate a hybrid system in which some objects are not discrete event-driven but are continuous time-driven. A networked control system (NCS) is such an application, in which physical process dynamics are continuous by nature. We have designed and implemented a hybrid simulation environment which effectively integrates models of continuous-time plant processes and discrete-event communication networks by extending the open source network simulator NS-2. To do this a synchronisation mechanism was developed to connect a continuous plant simulation with a discrete network simulation. Furthermore, for evaluating co-design approaches in an NCS environment, a piggybacking method was adopted to allow the control period to be adjusted during simulations. The effectiveness of the technique is demonstrated through case studies which simulate a networked control scenario in which the communication and control system properties are defined explicitly.

This paper investigates the robustHinfincontrol for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delay. By employing a new and tighter integral inequality and constructing an appropriate type of Lyapunov functional, delay-dependent stability criteria are derived for the control problem. Because neither any model transformation nor free weighting matrices are employed in our theoretical derivation, the developed stability criteria significantly improve and simplify the existing stability conditions. Also, the maximum allowable upper delay bound and controller feedback gains can be obtained simultaneously from the developed approach by solving a constrained convex optimization problem. Numerical examples are given to demonstrate the effectiveness of the proposed methods.

This paper proposes a simulated annealing based approach to determine the optimal or near-optimal path quickly for a mobile robot in dynamic environments with static and dynamic obstacles. The approach uses vertices of the obstacles to define the search space. It processes off-line computation based on known static obstacles, and re-computes the route online if a moving obstacle is detected. The contributions of the work include the employment of the simulated annealing algorithm for robot path planning in dynamic environments, and the development of a new algorithm planner for enhancement of the efficiency of the path planning algorithm. The effectiveness of the proposed approach is demonstrated through simulations under typical dynamic environments and comparisons with existing methods.

In this paper we show how to use a computer processor's time stamp counter register to provide a precise and stable time reference, via a high-precision relative clock synchronization protocol. Existing clock synchronization techniques, such as the network time protocol, were designed for wide-area networks with large propagation delays, but the millisecond-scale precision they offer is too coarse for local-area applications such as instrument monitoring systems, high-quality digital audio systems and sensor networks. Our new clock synchronization technique does not require specialized hardware but instead uses the Time stamp counter already available in the widely-used Intel Pentium processor. Experimental results show that we can achieve a synchronization precision in the order of 10 microseconds in a small-scale local area network using TSC registers, which is much higher than can be achieved by using a computer processor's time-of-day clock.

CPNs has been a useful tool not only to approximate a discrete system but also to model a continuous process. In this paper, CPNs are augmented with maximal and minimal firing speeds, and Interval speed CPNs (ICPNs) is defined. The enabling and firing of transitions of ICPNs are discussed, and the enabling of continuous transitions is classified into three levels: 0-level, 1-level and 2-level. Some rules to calculate the instantaneous firing speeds are also developed. In addition, illustrative examples are presented.