Through exploiting decentralized data from multi-source Internet-of-Things (IoT) devices, federated learning (FL) can accomplish the training of deep neural network (DNN) models in a privacy-preserving manner to provide premium intelligent services. Due to portability considerations, most IoT devices are computing-constrained which cannot afford frequent DNN model training in FL. Existing approaches use model compression techniques to reduce computing cost of IoT devices, whereas accuracy degradation is inevitably incurred. To address this challenge, we propose an elastic federated learning collaboration framework, namely EFLCF, to accommodate limited computing resources of IoT devices. Specifically, we first design an FL-oriented elastic neural network model with multiple-width subnets, and couple it with an FL device-server collaboration framework to form EFLCF, thereby releasing computing cost pressure of IoT devices. We then develop a freezing-assisted wide-to-narrow training mechanism to realize efficient device-server distributed training and further reduce device computing cost. Finally, we design an entropy-based narrow-to-wide elastic inference mechanism to decrease computing cost of inference without compromising accuracy. Experiments demonstrate that compared to well-known benchmarks, our EFLCF can reduce up to 97.65% device computing cost and improve up to 48.3% accuracy in training, while reducing up to 42.5% computing cost in inference.

With the large-scale integration of renewable energy sources and widespread application of high-frequency power electronic devices, the issue of supraharmonics in power systems has become increasingly prominent, which imposes a significant challenge to power quality. To mitigate the impact of spectral leakage and the picket-fence effect on the accuracy of supraharmonic disturbance parameter measurements, and reduce the sampling pressure of supraharmonic signal, this article proposes a supraharmonic measurement method based on windowed compressed sensing (CS) and the orthogonal matching pursuit (OMP) algorithm. The four-term third-order Nuttall window function is integrated into CS technology, where a windowed sparse measurement matrix is constructed to enable windowed compressed sampling of supraharmonic signals. Subsequently, the OMP algorithm is used for frequency estimation of the supraharmonic signals, and the three-spectral-line interpolation technique is applied to reduce the picket-fence effect, improving the accuracy of supraharmonic disturbance frequency, amplitude, and phase measurements. Simulation experiments demonstrate that the proposed algorithm significantly addresses the spectral leakage and the picket-fence effect, effectively reduces the data required for supraharmonic detection, and improves the measurement accuracy while improving noise resistance. In practical experiments, the absolute errors in frequency and amplitude measurement for three sets of supraharmonic components are 0.55, 0.36, and 1.09 Hz and 0.0006, 0.0008, and 0.001 V, respectively, validating the effectiveness and practicality of the proposed method.

Over the past few years, the integration of mobile edge computing (MEC) and serverless computing, known as serverless MEC (SMEC), has garnered considerable attention. Despite abundant existing works on SMEC exploration, there remains an unaddressed gap in guaranteeing dependable application outputs due to ignoring the threat of both soft and bit errors on SMEC infrastructures. Furthermore, existing works fall short of accommodating the personalized requirements and approximate computation of Internet of Things (IoT) applications, thereby resulting in holistic quality-of-service (QoS) degradation of SMEC systems typically provisioned by limited edge resources. In this article, we investigate the reliability-aware personalized deployment of approximate computation IoT applications for QoS maximization in SMEC environments. To this end, we propose a hybrid methodology composed of offline and online optimization phases. At the offline phase, a decomposition-based function placement method is devised to accomplish function-to-server mapping by integrating convex optimization, cross-entropy method, and incremental control techniques. At the online phase, a lightweight reinforcement learning scheme based on proximal policy optimization (PPO) is developed to handle the inherent dynamicity of IoT applications. We also build a simulation platform upon the real-world base station distribution in Shanghai Telecom and the practical cluster trace in the Alibaba open program. Evaluations demonstrate that our hybrid approach boosts the holistic QoS by 63.9% compared with the state-of-the-art peer algorithms.

This letter focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability constraints. We propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: 1) offline Oracle generation and 2) online IL. In the offline phase, we design a fast-converging heuristic to generate near-optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize IL using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms.

In a microgrid with hierarchical control, a typical cyber–physical power system (CPPS), cyber failures may induce physical system damage in a cross-layer fashion. Particularly during extreme conditions, the reliability of communication facilities may be jeopardized, inducing a degraded quality of service (QoS) and subsequently disrupting frequency and voltage regulation in a cross-layer fashion. This article proposes a co-optimization technique on QoS, frequency regulation and voltage regulation that comprises: 1) a specialized CPPS model for quantitatively analyzing the cross-layer impact of resource allocation to physical states, specifically frequency and voltage; 2) a multiobjective formulation aimed at enhancing QoS while concurrently minimizing the impact on physical states regulation; and 3) a weight adjusted model reference adaptive search algorithm to narrow the search space by leveraging the characteristics of our CPPS model. Compared with state-of-the-art techniques that focus solely on optimizing QoS, our proposed technique achieves a reduction of frequency (13.74%) and voltage (4.57%) deviations, albeit with a minor compromise in QoS (0.34%).

Recommender systems play significant roles in business, especially in e-commerce. Nevertheless, users’ behaviors are usually mixing and drifting, which is hard to tackle. Current sequential methods of item-wise interest extracting suffer from the intricacy and sparsity of data. Inspired by that category-wise interests may be intrinsic drivers of user behaviors and heterogeneous actions may reveal certain behavior patterns, we introduce intention as a tuple of category and action to address the data issues. In this paper, an intention-aware Markov chain based sequential recommendation model (IMRec) is proposed. We model the overall preferences of users as the integration of long-term preferences and short-term intents. In particular, the matrix factorization method is adopted to extract the long-term user-item preference. For the modeling of the short-term intents transition, we adopt high-order Markov chain based methods. A factorized mixture transition distribution model for high-order Markov chain approximation is leveraged in this paper to reduce the algorithm complexity. An auxiliary loss on intention representation is utilized, which brings considerable performance improvements. Experiments on real-world datasets demonstrate that our model outperforms state-of-the-art baseline models in terms of three common metrics, and shows superior stability, scalability, and training efficiency. Note to Practitioners—In e-commerce, sequential recommenders are essential to facilitate decision-making and promote business. A big challenge is to capture the sequential patterns from the mixing and drifting user-item interaction sequences. Customer behaviors are often driven by their inherent intentions, which may present more stable and reliable patterns. Motivated by that, we propose a novel intention-aware next-item recommendation algorithm with high performance. Our method models the user-item preferences as the integration of long-term preferences and short-term intents. More specifically, long-term preferences show the general tastes of users, which are modeled by latent factors of users and items. Additionally, the intention is defined as a tuple of category and action. We model the short-term intents as the intention transition from the past intentions by a high-order Markov chain. We further leverage a factorization-based mixture transition distribution model for high-order Markov chain approximation and reduce the algorithm complexity. The proposed model is validated in 4 real-world datasets and shows good recommendation performance, performance stability, model scalability, and training efficiency. Our model provides an effective recommendation method at low computational costs for e-commerce companies.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

For distributed solid electricity thermal storage aggregators (DSETSA), the uncertainty of the marginal clearing price may lead to the problem of multibidding scenarios (including successful, part successful, and failed biddings) in the electricity spot market. Moreover, the marginal operating cost affecting the bidding revenue in the spot market is not considered in the existing methods, which challenge the bidding of the aggregators. To address the challenge, this article proposes an optimized operation framework for DSETSA. First, based on the incomplete information characteristics of the spot market, an optimal bidding model which incorporates marginal operating cost constraints for DSETSA under multibidding scenarios is proposed to increase the operation profit. Second, the DSETSA's multibidding scenario problem induced by the uncertainty of the marginal clearing price in the electricity spot market is cast into a probability distribution representation using the Bayesian incomplete information theory to increase the chances of winning bids. Finally, framework establishes the relationship between the bidding price, electricity demand, and public traded electricity in the spot market. The effectiveness of the proposed framework is demonstrated through simulations.

One of the fundamental tasks in autonomous driving is environment perception for pedestrian detection, where the fused pedestrian detection using camera and light detection and ranging (LiDAR) information imposes challenges since the data alignment, compensation, and fusion between different data modes are challenging and the simultaneous acquisition of data from two different modalities also increases the difficulty. This work addresses the above challenges from both of the hardware and software dimensions. First, a multimodal pedestrian data acquisition platform is designed and constructed using an RGB camera, sparse LiDAR, and data processing module including hardware connection and deployment, sensor distortion correction and joint calibration, and data acquisition synchronization. Pedestrian data from multiple scenes are then collected using this platform to produce and form a dedicated multimodal pedestrian detection dataset. Further, a two-branch multimodal multilevel fusion pedestrian detection network (MM-Net) is proposed, which includes a two-branch feature extraction module and a feature-level data fusion module. Extensive experiments are performed on the multimodal pedestrian detection dataset and KITTI dataset for the comparison with the existing models. The experimental results demonstrate the superior performance of MM-Net.

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

The network system deployed in hazardous environments is a key component of hazard-aware cyber-physical systems (CPSs) and its performance highly depends on surrounding environments. Due to the mobility of network nodes (e.g., portable IoT devices), frequently changeable network topology and links, as well as other external interferences such as electromagnetic interference, ensuring adaptivity and reliability of hazard-aware CPSs is of utmost importance. Meanwhile, the timeliness of message transmission is stringent in hazardous environments because the violation of timing requirements may lead to serious consequences. Last but not least, portable IoT devices are typically energy limited, thus ensuring a sustainable message transmission is highly necessary. In this paper, we aim at optimizing the reliability of hazard-aware CPSs while meeting the timing and energy constraints. To this end, we develop the first hazard-aware CPS model and study the impacts of surrounding environments (i.e., physical side) to the network infrastructure of a hazard-aware CPS (i.e., cyber side) with respect to reliability. We also propose a new scheme that adaptively tunes the fault tolerance strategies and admission strategies for real-time messages, to increase the reliability of hazard-aware CPSs under the energy constraint. Extensive simulation results demonstrate that our proposed scheme is capable of increasing system reliability by up to 4.21× with a lower deadline miss rate and runtime overhead compared with the state-of-the-art approaches.

Substation communication network (SCN) provides real-time, high-speed, and reliable data transmissions for the advanced monitoring and control functionalities, which are facing increasing cyberspace threats and attacks. Efficient threat perception and cyber situational awareness are essential to enhance secure and reliable SCN operations. This article explores multiscale SCN traffic pattern characteristics with holistic network traffic, separated network traffic for included devices (especially IoT devices) and separated network traffic of certain types of protocol. The proposed online traffic-oriented SCN traffic anomaly detection and cyber situational awareness models are designed for the network anomalies and cyber-attacks that could cause network traffic pattern variations. We leverage a fractional autoregressive integration moving average (FARIMA)-based dynamic threshold model to detect abnormal traffic patterns without sophisticated computations or deep packet inspection. The SCN real-time operation conditions are timely quantified through the statistical methods with the alliance of SCN topology and protocols. The cyber situational awareness model is further carried out to evaluate the most affected protocol and security risks of various devices in SCN using Grubbs’ test. The experiment results are carried out based on a real 110-kV intelligent power substation. The numerical results confirm the comparative low mean square error (MSE) and low complexity of the online traffic characterization when forecasting holistic network traffic and separated network traffics. Furthermore, the timely and quantified cybersecurity risk analysis is conducted based on the SCN traffic with varying scales to detect cyberspace threats and identify the high-risk SCN devices and the most affected protocol.

In the era of the Internet of Things (IoT), spatially distributed IoT devices collect and store data in a distributed fashion for computational efficiency. However, in IoT networks, due to the fragile device, harsh deployment environment, and unreliable transmission, the possibility of missing data is increasing, which may significantly affect subsequent data processing. Traditional approaches to impute missing data in IoT distributed data sets bring huge communication overheads. In this article, we develop an efficient architecture for distributed IoT data imputation based on a designed multidiscriminator conditional generative adversarial network. The architecture intelligently learns the characteristics of the distributed data sets to accurately impute missing values. Our experiments are performed using three data sets under two different data missing mechanisms. The experimental results demonstrate that using three data sets, the proposed imputation technique can drastically reduce the imputation error by up to 88.66%, 94.27%, and 95.53% at the premise of low transmission cost, respectively, compared to five state-of-the-art methods.

Smart manufacturing has become mainstream in the development of manufacturing industry, where Industrial Internet of Things plays a critical role. In this article, a systematic intelligent technique for procurement supply chain (PSC) optimization is proposed. In this technique, an integrated approach based on variational mode decomposition and long short-term memory network is used to predict the market price. Considering the factors, such as production plan and market fluctuation, a multiperiod dynamic purchasing model is built. A stacked autoencoder under bootstrap aggregation is then trained to evaluate suppliers automatically end-to-end based on various data. Finally, a multiobjective order allocation model is established considering the procurement costs and supplier scores, and solved by particle swarm optimization. The extensive experiments are performed using a realistic industrial application in a zinc smelter company. The experimental results demonstrate that the proposed technique greatly reduces labor costs, improves the efficiency of PSC, and reduces the procurement costs of the company.

With the rapid development of information technologies, the computing, networking, and physical elements in industrial environments are becoming tightly amalgamated with each other, resulting in the formation of the so-called Industrial Cyber-Physical Systems (ICPS). These systems forge the core of current real-world networked industrial infrastructures, having a cyber-representation of physical assets through digitalization of data across the enterprise, along the value stream and process engineering life cycle, along the digital thread, and along the supply chain. Typical applications of ICPS include smart grids, digital factory, cognitive and collaborative robots, freight transportation, process control, plant-wide systems, medical monitoring, etc. ICPS often operate in an unpredictable and challenging environment, where various disturbances, such as unplanned natural events, human faults or malicious behaviors, software and hardware failures, etc., may occur during the automation process at runtime. Moreover, ICPS can exhibit strong reconfigurability and evolve structurally for many purposes. During this evolution, new and unforeseen possibilities in the service-oriented business process may appear among various ICPS components. In particular, new “emergent” behaviors may arise that need to be monitored, understood, managed and controlled. When there are significant uncertainties, such emergent behaviors could make the evolved ICPS unstable and unable to meet the quality/performance targets, even resulting in hazards. Well-designed machine-learning techniques have the potential to effectively address the uncertainties and disturbances in the automation of ICPS. They can also facilitate the automated discovery of valuable underlying rules and patterns to improve the performance of ICPS in all phases of their life cycles.

Internet of Vehicles (IoV) has become an indispensable data sensing and processing platform in Internet of Things (IoT) for intelligent transportation. The mounted cameras on the vehicles along with the fixed roadside cameras are utilized to provide pictorial services for IoV users and law enforcement agencies. For such forensic services, ensuring the security and privacy of vehicles while guaranteeing the efficiency of data transmission among vehicles is important. In this paper, we propose a lightweight incentive authentication scheme (LIAS) for forensic services in IoV. LIAS is developed on a three-tier architecture containing cloud layer, fog layer, and user layer. LIAS uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to realize a secure anonymous authentication efficiently. We conduct correctness and security analysis, as well as performance analysis and evaluation to validate the high security and efficiency of LIAS. Experimental results reveal that, the communication and computation overheads as well as the message delay and packet loss of LIAS are much lower than those of state-of-the-art techniques. Note to Practitioners—This paper is motivated by the security and privacy issues of forensic services in IoV for intelligent transportation. Our goal is to improve the security and privacy of vehicles while guaranteeing the lightweight and incentive of data transmission among the vehicles. Fog-assisted IoV is introduced to fully utilize the capacities of near-user edge devices as well as the connections between fog nodes and devices. However, it still faces the difficulties in ensuring vehicles’ security and privacy. Moreover, vehicles’ information dissemination could be easily monitored because of the unavoidable defect of wireless communication. Thereby, it is essential to guarantee the security and privacy of vehicles while enhancing the efficiency of vehicles’ data transmission during the forensic service. To this end, this paper proposes a lightweight conditional anonymous authentication scheme for forensic services in IoV, which is developed based on the pairing-free technique to achieve secure anonymous authentication with high efficiency. This paper also designs a user tracing mechanism, incentive mechanism, and pseudonym update mechanism to realize safe and effective forensic service in IoV.

In real industrial processes, factors, such as the change in manufacturing strategy and production technology lead to the creation of multimode industrial processes and the continuous emergence of new modes. Although the industrial SCADA system has accumulated a large amount of historical data, which can be used for modeling and monitoring multimode processes to a certain extent, it is difficult for the model learned from historical data to adapt to emerging modes, resulting in the model mismatch. On the other hand, updating the model with data from new modes allows the model to continuously match the new modes, but it may cause the model to lose the ability to represent the historical modes, resulting in "catastrophic forgetting." To address these problems, this article proposed a jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updated the model by learning the data of new modes, so that the model can adaptively match the newly emerged modes. At the same time, a similarity metric was put forward to guarantee the representation ability of the proposed method for historical data. A numerical simulation experiment, the CSTH process experiment, and an industrial roasting process experiment indicated that the proposed JMSDL method can match new modes while maintaining its performance on the historical modes accurately. In addition, the proposed method significantly outperforms the state-of-the-art methods in terms of fault detection and false alarm rate.

Differential-private federated learning (DP-FL) has emerged to prevent privacy leakage when disclosing encoded sensitive information in model parameters. However, the existing DP-FL frameworks usually preserve privacy homogeneously across clients, while ignoring the different privacy attitudes and expectations. Meanwhile, DP-FL is hard to guarantee that uncontrollable clients (i.e., stragglers) have truthfully added the expected DP noise. To tackle these challenges, we propose a heterogeneous differential-private federated learning framework, named HDP-FL, which captures the variation of privacy attitudes with truthful incentives. First, we investigate the impact of the HDP noise on the theoretical convergence of FL, showing a tradeoff between privacy loss and learning performance. Then, based on the privacy-utility tradeoff, we design a contract-based incentive mechanism, which encourages clients to truthfully reveal private attitudes and contribute to learning as desired. In particular, clients are classified into different privacy preference types and the optimal privacy-price contracts in the discrete-privacy-type model and continuous-privacy-type model are derived. Our extensive experiments with real datasets demonstrate that HDP-FL can maintain satisfactory learning performance while considering different privacy attitudes, which also validate the truthfulness, individual rationality, and effectiveness of our incentives.

The CAN FD emerges as a promising CAN technology inside the ACPS due to its advantages of high data-phase bit-rate and message payload. HSM based security solution is recommended by auto industry to protect CAN FD from potential security attacks, but it induces new challenges on timing analysis of CAN FD messages, which is left open in the literature. This article develops the first security-aware system model to describe the processing of CAN FD messages, and presents a new WCRT analysis to bound the interference induced by security-critical messages. We give the theoretical proof that our WCRT analysis can upper bound the response time of CAN FD messages. Using a small message set, we show that the WCRT computed by our new analysis is only 14% percent higher than the true WCRT obtained from an exhaustive search based simulator. By comparing with existing method, the number of impacted messages increases along with the increasing number of security critical messages, and for the two typical CAN FD systems, the percentage of WCRT increase varies from 12.43% to 14.57% and 7.0% to 10.89%, respectively; the percentage of WCRT decrease varies from 3.29% to 6.04% and 4.13% to 7.93%, respectively.

Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it is often difficult for IIoT to collect enough tagged and highly reliable data, which further degrades the process monitoring performance and makes the monitoring results not trustworthy. In order to reduce the negative impact of these unreliable factors, a self-weighted dictionary learning process monitoring method is proposed. In particular, a label propagation classifier is implemented from the labeled data to unlabeled data to obtain a credible label prediction. Subsequently, considering the interference of low-quality data and label information, we reweight the classification loss and label-consistency constraints to enhance the trustworthiness of feature extraction. Finally, a novel iterative optimization algorithm that combines the block coordinate descent method with the alternating direction multiplier method is developed to ensure the convergence speed of the learned classifier and dictionary. Extensive experiments indicate that the proposed method can guarantee the trustworthiness of the process monitoring results.

Platform-based vehicle recognition is a critical task in logistics scenarios that facilitates the efficient management of resources. Although recent advances in the computer vision domain can be conveniently adopted to recognize the identities of vehicles and the occupations of platforms, the efficacy is significantly compromised by the severe interference and noise at the platforms of logistics industrial parks. This work tackles these difficulties through concentrating on the sequential characteristics of vehicles during arrival and departure. An innovative dynamic hidden Markov model (DHMM) is proposed to estimate the real sequence of vehicle states from the noisy observations. A dynamic Viterbi algorithm is also developed to solve the proposed DHMM method with high efficiency. The proposed method is evaluated against multiple baselines through experiments, where it can recognize the vehicle states with high accuracy and is demonstrated to significantly outperform the baselines when the interference is strong.

Recent advances in computation, communication, and control technologies have revolutionized the way that humans, smart things, and intelligent systems interact and exchange information. Intelligent cyber–physical systems (ICPSs), characterized by the deep complex intertwining process among cyber components for computation and control with intelligent technologies and the dynamic physical components, will fuel this revolution. Examples of ICPS include intelligent automotive and transportation systems, intelligent avionics systems, smart home, smart building, smart community, smart grid, smart healthcare systems, intelligent wearable systems, intelligent energy systems, robotic systems, etc. Bringing machine/deep-learning-based intelligence techniques into CPSs can improve the performance in many aspects. However, this also imposes new security, privacy, and trust challenges, which highlights the need to develop novel methodologies to tackle these challenges. In this special issue, we will publish the following articles.

The development of industrial cyber-physical system (ICPS) provides a tight connection between the digital model and the industrial physical plant, which enables to use the data-driven methods to reflect the state of process running in the real world. However, due to the noisy and harsh industrial environment, the collected data are often corrupted to some extent. If the corrupted data are not detected in time, the data-driven model will inevitably degenerate and induce a poor process monitoring performance. In addition, the nonlinear characteristics between process variables due to the high complexities in physical plant bring challenges to the data-driven methods. In this article, a robust kernel dictionary learning method, which can overcome the negative influence of outliers and simultaneously extracts the nonlinear characteristics of industrial process, is proposed to address the above problems in ICPS. Our extensive experiments demonstrate that the proposed method has achieved significantly better and stable performance to deal with outlier detection and process monitoring in ICPS.Note to Practitioners—In order to mitigate the impacts due to process noise and outliers, a robust kernel dictionary learning method is proposed to improve the accuracy and stability of the process monitoring of industrial cyber-physical systems. This method considers the process noise, sparse outlier, as well as the nonlinear characteristic of industrial systems for improving the accuracy and stability of monitoring. Compared with many state-of-the-art methods, the proposed method can detect the outliers in the training dataset adaptively, which is more applicable to the real industrial system.

This article studies the robust deep stereo vision in autonomous driving systems and counteracting adversarial attacks. The autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract the practical attacks in autonomous systems, novel methods based on simulated attacks are proposed in this article. Univariate and multivariate functions are adopted to represent the relationships between the left and right input images and the deep stereo model. A stereo regularizer is proposed to guide the model to learn the implicit relationship between the images and characterize the loss function’s local smoothness. The attacks are generated by maximizing the regularizer term to break the linearity and smoothness. The model then defends the attacks by minimizing the loss and regularization terms. Two techniques are developed in this article. The first technique,SmoothStereo, explores the basic knowledge from the physical world and smoothness, while the second technique,SmoothStereoV2, improvesSmoothStereothrough leveraging the smooth activation functions during the defense.SmoothStereoV2can learn and utilize the gradient information concerning the attacks. The gradients of the smooth activation functions can handle attacks for improving the model robustness. Numerical experiments on KITTI datasets demonstrate that the proposed methods offer superior performance.

In-situ(InS) server systems are typically deployed in special environments to handleInSworkloads which are generated from environmentally sensitive areas or remote places lacking modern power supply infrastructure. This special operating environment ofInSservers urges such systems to be powered by renewable energy. In addition, theInSsystems are vulnerable to soft errors due to the harsh environments they deploy. This article tackles the problem of allocating harvested energy to renewable powered servers and assigning theInSworkloads to these servers for optimizing throughput of both the overall system and individual servers under energy and reliability constraints. We perform the energy allocation based on system state. In particular, for systems in low energy state, we propose a game theoretic approach that models the energy allocation as a cooperative game among multiple servers and derives a Nash bargaining solution. To meet the reliability constraint, we analyze the reliability optimality of assigning tasks to multiple servers and design a reliability-aware task assignment heuristic based on the analysis. Experimental results show that with a small time overhead, the proposed energy allocation approach achieves a high throughput from perspectives of both the overall system and individual servers, and the proposed task assignment approach ensures an increased system reliability.

Cloud storage services give users a great facility in data management such as data collection, storage and sharing, but also bring some potential security hazards. An utmost importance is how to ensure the integrity of data files stored in the cloud, particular for user groups without trusted managers. Existing literature focuses on integrity checking for groups with managers who have lots of permissions. To overcome the shortage of public auditing for non-manager user groups in clouds, we develop a novel framework IPANM that integrates$(t,n)$threshold technology, blinding technology, and incentive mechanism to realize an incentive privacy-preserving public auditing scheme. In IPANM, the data integrity is guaranteed by our$(t,n)$threshold signature based public auditing and the data privacy during public auditing is protected by the blinding technology. The generation of signatures can be accelerated by our blockchain-aided incentive mechanism that mobilizes the initiative of signers in the signature generation by rewarding the contributed signers. We formally prove the security of our IPANM and conduct numerical analysis and evaluation study to validate its high efficiency. The experimental results demonstrate that IPANM has lower overheads of storage, communication, and computation as compared to the state-of-the-art technique IAID-PDP and NPP.

The accurate time-frequency (TF) positioning of power quality (PQ) disturbances is the basis of dealing with PQ problems in power systems. To accurately detect PQ disturbances, this article proposes a Kaiser window-based S-transform (KST) that provides better time resolution at fundamental frequency to detect the amplitude information for voltage swell, sag, interrupt, flicker, and better frequency resolution at higher frequencies to detect the frequency of time-varying harmonics and oscillatory transient. Based on short-time Fourier transform and S-transform, KST uses a Kaiser window with the characteristic of inherent optimal energy concentration as the kernel function. The Kaiser window can be adjusted adaptively according to the detection demand of PQ disturbances by the designed control function. This allows KST to easily accommodate different detection requirements at different frequencies. The utilization of Fourier transform ensures that KST can be realized quickly. The complex TF matrix is generated after a signal is transformed by KST, where the column vector is expressed as the distribution of amplitude and phase with time at a certain frequency, and the row vector represents the distribution of amplitude and phase with frequency at a certain sampling time. Experimental results demonstrate that the proposed KST significantly outperforms the state-of-the-art techniques in TF analysis of PQ signals, especially for the energy concentration and the detection of fundamental wave.

Automotive cyber-physical systems need to be rigorously checked and tested under various physical conditions. Automakers aim to improve development efficiency of the automotive cyber-physical systems in the fierce market competition. However, the actual development process suffers from the challenges of long development cycle and poor scalability. To tackle these challenges, this article develops a digital twinning based adaptive development environment for automotive cyber-physical systems, which addresses two critical problems: each physical entity (i.e., electronic control unit, component, test source, etc.) needs to clone a corresponding digital twin; digital twins and the physical entities need to interact closely. The first problem is addressed through proposing an integrated digital twinning clone flow. The second problem is addressed through developing a smart digital twinning board. Our case study with the automotive body control system demonstrates that the adaptive development environment achieves a high adaptability with short development cycle, low complexity, low cost, high scalability, and high flexibility, which meet various automotive cyber-physical design requirements during the development process.

Heterogeneous multiprocessor system-on-chips (MPSoCs) are suitable platforms for real-time embedded applications that require powerful parallel processing capability as well as low power consumption. For such applications, soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are both key concerns. There have been several efforts in the literature oriented toward related reliability problems. However, most existing techniques only concentrate on improving one of the two reliability metrics, which are not suitable for embedded systems deployed in critical applications in need of a long lifetime as well as a reliable execution. This article develops a novel heterogeneous earliest-finish-time (HEFT)-based algorithm to maximize SER and LTR simultaneously under the real-time constraint for dependent tasks executing on heterogeneous MPSoC systems. More specifically, a new deadline-constrained reliability-aware HEFT algorithm, namely DRHEFT, is proposed, which seeks for the best SER–LTR tradeoff solutions through using fuzzy dominance to evaluate the relative fitness values of candidate solutions. The extensive experiments on real-life benchmarks as well as synthetic applications demonstrate that DRHEFT is capable of achieving better SER–LTR tradeoff solutions with higher hypervolume and less computation cost when compared with the state-of-the-art approaches.

Although Reinforcement Learning (RL) has been successfully applied in traffic control, it suffers from the problems of high average vehicle travel time and slow convergence to optimized solutions. This is because, due to the scalability restriction, most existing RL-based methods focus on the optimization of individual intersections while the impact of their cooperation is neglected. Without taking all the correlated intersections as a whole into account, it is difficult to achieve global optimization goals for complex traffic scenarios. To address this issue, this paper proposes a novel federated reinforcement learning approach named FedLight to enable optimal signal control policy generation for multi-intersection traffic scenarios. Inspired by federated learning, our approach supports knowledge sharing among RL agents, whose models are trained using decentralized traffic data at intersections. Based on such model-level collaborations, both the overall convergence rate and control quality can be significantly improved. Comprehensive experimental results demonstrate that compared with the state-of-the-art techniques, our approach can not only achieve better average vehicle travel time for various multi-intersection configurations, but also converge to optimal solutions much faster.

This special issue aims to provide a platform for the researchers, academia, and industry to present their novel solutions, applications, tools, software, hardware, and algorithms designed for addressing various sustainability challenges in CPS.The response from the CPS community was enthusiastic: the special issue received 34 manuscripts submitted by the authors from China, United States, India, Korea, Lebanon, and so on.According to the Journal of Software: Practice and Experience (SPE) review standards, this special issue accepted 14 high-quality research articles that cover a wide range of topics.These articles provide the software and hardware co-design solutions to improve dependability, energy efficiency, quality of service (QoS) of CPS, and also to introduce the methodologies for specific CPS applications.

Sensor drift is an intractable obstacle to practical temperature measurement in smart building. In this article, we propose a sensor spatial correlation model. Given prior knowledge, maximum a posteriori (MAP) estimation is performed to calibrate drifts. MAP is formulated as a nonconvex problem with three hyper-parameters. An alternating-based method is proposed to solve this nonconvex formulation. Cross-validation, Gibbs expectation-maximization (EM) and variational Bayesian EM (VB-EM) are further exploited to determine hyper-parameters. Experimental results on widely used benchmarks from the simulator EnergyPlus demonstrate that compared with state-of-the-art methods, the proposed framework can achieve a robust drift calibration and a better tradeoff between accuracy and runtime. On average, compared with state-of-the-art, the proposed framework can achieve about 3× accuracy improvement. In order to attain the same drift calibration accuracy with VB-EM, Gibbs EM needs 10 000 samples, which will incur a 30× runtime overhead.

As a promising method for central model training on decentralized device data without compromising user privacy, federated learning (FL) is becoming more and more popular in Internet-of-Things (IoT) design. However, due to limited computing and memory resources of devices that restrict the capabilities of hosted deep learning models, existing FL approaches for artificial intelligence IoT (AIoT) applications suffer from inaccurate prediction results. To address this problem, this article presents a collaborativeBig.Littlebranch architecture to enable efficient FL for AIoT applications. Inspired by the architecture of BranchyNet which has multiple prediction branches, our approach deploys deep neural network (DNN) models across both cloud and AIoT devices. OurBig.Littlebranch model has two branches, where the big branch is deployed on cloud for strengthened prediction accuracy, and the little branches are used to fit for AIoT devices. When AIoT devices cannot make the prediction with high confidence using local little branches, they will resort to the big branch for further inference. To increase both prediction accuracy and early exit rate ofBig.Littlebranch model, we propose a two-stage training and coinference scheme, which considers the local characteristics of AIoT scenarios. Comprehensive experiment results obtained from a real AIoT environment demonstrate the efficiency and effectiveness of our approach in terms of prediction accuracy and average inference time.

In recent years, the investigations on cyber-physical systems (CPS) have become increasingly popular in both academia and industry. A primary obstruction against the booming deployment of CPS applications lies in how to process and manage large amounts of generated data for decision making. To tackle this predicament, researchers advocate the idea of coupling edge computing, or edge-cloud computing into the design of CPS. However, this coupling process raises a diversity of challenges to the quality-of-services (QoS) of CPS applications. In this article, we present a survey on edge computing or edge-cloud computing assisted CPS designs from the QoS optimization perspective. We first discuss critical challenges in service latency, energy consumption, security, privacy, and reliability during the integration of CPS with edge computing or edge-cloud computing. Afterwards, we give an overview on the state-of-the-art works tackling different challenges for QoS optimization, and present a systematic classification during outlining literature for highlighting their similarities and differences. We finally summarize the experiences learned from surveyed works and envision future research directions on edge computing or edge-cloud computing assisted CPS optimization.

In the past few years, the study on placing edge servers for response time optimization in mobile edge-cloud computing systems has become increasingly popular. Most of the existing schemes neglect two important aspects: one is the heterogeneity of edge/cloud servers and the other is the response time fairness of base stations, which may significantly degrade the system quality of services to mobile users. In this article, we conduct the study of deploying heterogeneous edge servers to optimize the expected response time of both the whole and individual base stations. We propose an approach consisting of offline and online stages. At the offline stage, the optimal placement strategy of heterogeneous edge servers is produced by using an integer linear programming technique. At the online stage, a mobility-aware game-theory-based method is developed to deal with the dynamic characteristic of user movement. Experimental results reveal that compared to benchmarking methods, our approach not only reduces system-expected response time by 47.37%, but also improves response time fairness of base stations by 71.60%.

This Special Section on ‘`Cloud-Edge Computing for Cyber-Physical Systems and Internet-of-Things’' is oriented to the dissemination of a few of those latest research and innovation results, covering many aspects of design, optimization, implementation, and evaluation of emerging cloud-edge solutions for CPS and IoT applications. The selected high-quality contributions cover a broad range of novel technologies and application scenarios in CPS and IoT. We hope that these accepted papers will produce long-lasting impacts, as well as stimulating and encouraging the international community to work on this exciting and impactful topic.

Vehicle design has entered a new stage, namely, Software Defined Vehicles (SDV), where functional safety is required to be guaranteed for risk control, and development cost needs to be optimized for profit maximization. This paper targets to optimize the development cost under the functional safety requirement for a safety-aware SDV, based on the automotive safety integrity level (ASIL) decomposition defined in ISO 26262. For this, a two-stage solution is proposed, which includes functional safety risk assessment and development cost optimization. The first stage develops a new fast risk assessment (FRA) algorithm to assess the functional safety risk, including the joint reliability risk and the real-time risk, of the SDV functionality. The second stage proposes a dual requirement guarantee (DRG) algorithm to optimize the development cost considering reliability and real-time requirements jointly. Our experiments demonstrate that the proposed two-stage solution guarantees the functional safety requirement while reducing the development cost by 20%-24%.

Co-verification of reliability and confidentiality is a necessary process for safety- and security-critical applications. While these two objectives are conflicting, preassignment has emerged as an effective and efficient verification solution. In this article, we propose two preassignment-based co-verification techniques, namely, Blocks-based Vulnerability Preassignment (BVP) and Reversed Blocks-based Time Preassignment (RBTP) for a parallel application in distributed CAN FD systems. BVP can significantly improve reliability under a vulnerability bound, while RBTP can reduce vulnerability over a reliability goal. Real case study with the parallel automotive application and parallelism study with two structures of high-parallelism and low-parallelism applications are demonstrated; the proposed BVP and RBTP can improve the verification acceptance ratio by 19 and 10 percent compared to the state-of-the-art Average Vulnerability Preassignment (AVP) and Average Time Preassignment (ATP) techniques, respectively.

Convolutional Neural Networks (CNNs) are becoming popular in Internet-of-Things (IoT) based object tracking areas, e.g., autonomous driving, commercial surveillance, and intelligent traffic management. However, due to limited processing power of embedded devices and network bandwidth, how to simultaneously guarantee fast object tracking with high accuracy and low energy consumption is still a major challenge, which makes IoT-based vision applications unreliable and unsustainable. To address this problem, this article proposes a collaborative edge-cloud architecture that resorts to cloud for object tracking performance enhancement. By properly offloading computations to cloud and periodically checking tracking status of edge devices through convolutional Siamese networks, our novel edge-cloud architecture enables interactive collaborations between edge devices and cloud servers in order to quickly and accurately rectify tracking errors. Comprehensive experimental results on well-known video object tracking benchmarks show that our architecture can not only significantly improve the performance of object tracking, but also can save the energy consumption of edge devices.

In this paper, we focus on studying robust deep stereo vision of autonomous driving systems and counteracting adversarial attacks against it. Autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract these attacks in autonomous systems, a novel defense method is proposed in this paper. A stereo-regularizer is proposed to guide the model to learn the implicit relationship between the left and right images of the stereo-vision system. Univariate and multivariate functions are adopted to characterize the relationships between the two input images and the object detection model. The regularizer is then relaxed to its upper bound to improve adversarial robustness. Furthermore, the upper bound is approximated by the remainder of its Taylor expansion to improve the local smoothness of the loss surface. The model parameters are trained via adversarial training with the novel regularization term. Our method exploits basic knowledge from the physical world, i.e., the mutual constraints of the two images in the stereo-based system. As such, outliers can be detected and defended with high accuracy and efficiency. Numerical experiments demonstrate that the proposed method offers superior performance when compared with traditional adversarial training methods in state-of-the-art stereo-based 3D object detection models for autonomous vehicles.

Cyber-physical systems (CPS) are characterized by deep and complex intertwining among cyber components and physical components. Due to the fast increase in system complexities, the operations of CPS involve sensing, processing and storage of massive amount of data. This nature of “big data” imposes fundamental challenges on the design and management of CPS in multiple aspects such as performance, energy efficiency, security, privacy, reliability, sustainability, fault tolerance, scalability and flexibility. Tackling these challenges necessitates innovative big data techniques for handling massive data in CPS. The articles in this special section include a few selected state-of-the-art research results on the topic of big data sensing, processing and storage for CPS, and stimulates a broad range of researchers to participate in the interdisciplinary CPS research in the future. This special issue has received a significant number of submissions while only a small portion of them are selected for publications. The selected papers showcase how interesting data analytics techniques can be leveraged to optimize different metrics in CPS, such as timing, efficiency, schedulability, power, reliability, and security, etc.

Fog computing is an emerging architectural paradigm for the implementation of the Internet of Things, where computation moves from cloud servers to network edges. Fog computing systems are with three characteristics: 1) low latency; 2) strong presence of real-time applications; and 3) reusability of end devices. Most existing designs of fog computing systems concentrate on reducing application processing latency, but neglect real-time requirements of applications and reusability of end devices, which may drastically degrade both functionality and quality-of-service (QoS) of applications. In this article, we investigate QoS optimization of real-time applications in fog computing systems equipped with reusable end devices and powered by hybrid energy of renewable generations and grid electricity. We propose a renewable-adaptive computation offloading approach. At the end device layer, local energy allocation schemes are designed at the application-level and component-level, where techniques of the cooperative game and mixed-integer linear programming (MILP) are leveraged, respectively. At the fog layer, the local energy allocation method is augmented to a local-remote scheduling solution by judiciously judging whether or not the computation offloading of an application needs to be triggered. The experimental results demonstrate that compared to benchmarking algorithms, our approach improves the overall and individual application QoS by up to 101.93% and 59.30%, respectively.

3-D multiprocessor system-on-chip (MPSoC) systems can offer higher integration density, lower interaction cost, better bandwidth, and greater performance. However, vertically stacked silicon layers and limited heat dissipation paths result in high peak temperature and large temperature variation, which incur reliability reduction, lifetime decay, and performance degradation. In this article, we propose an offline augmented cross-entropy (CE)-based task scheduling strategy to jointly optimize peak temperature and temperature variation under the constraint of timeliness. Specifically, based on the conventional CE method, a heuristic iterative sampling method is designed to explore task-to-core assignment for balanced heat distribution between the top-layer and the bottom-layer cores. Subsequently, thermal characteristics of 3-D MPSoC systems are used to judiciously swap tasks between the two layers to improve the conventional CE-based task assignment and accelerate the iterative process. The peak temperature of individual cores is further reduced via sequencing, splitting, and slacking task execution. The experimental results demonstrate that compared to the existing state-of-the-art methods, the proposed scheme can reduce peak temperature by up to 8.02 °C and temperature variation by up to 24.78% without violating the timeliness of tasks.

Millions of automobiles with the same model could be recalled due to a cascading effect if attackers acquire the execution flow of an automotive application. To mitigate the scaling across effects induced by attacks, several security-aware obfuscated priority assignments have been proposed recently. Although these techniques can handle low-payload controller area network (CAN) messages in a nonparallel automotive application, they are unsuitable for the next generation high-bandwidth in-vehicle network architecture and parallel execution of complex safety-critical applications. In this article, we develop a new security-aware obfuscated priority assignment approach which explores CAN with flexible data-rate (CAN FD) messages in a parallel automotive application. Specifically, we propose a fast sequence pruning (FSP) technique for exploring head-based sequence pruning (HSP) and tail-based sequence pruning (TSP). Experiments with real-life parallel automotive application show that FSP can efficiently obtain millions of obfuscated priority assignments, which significantly mitigates the scaling across effects.

Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients' tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.

State estimation plays a critical role in maintaining operational stability of a power system, which is however vulnerable to attacks. False data injection (FDI) attacks can manipulate the state estimation results through tampering the measurement data. In this paper, a cyberphysical model is proposed to defend against FDI attacks. It judiciously integrates a physical model which captures ideal measurements, with a generative adversarial network (GAN) based data model which captures the deviations from ideal measurements. To improve computation efficiency of GAN, a new smooth training technique is developed, and an online adaptive window idea is explored to maintain the state estimation integrity in real time. The simulation results on IEEE 30-bus system and IEEE 118-bus system demonstrate that our defense technique can accurately recover the state estimation data manipulated by FDI attacks. The resulting recovered measurements are sufficiently close to the true measurements, with the error lower than 1.5e-5and 2e-2p.u. in terms of voltage amplitude and phase angle, respectively.

Along with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.

Internet of Things (IoT) devices, such as intelligent road side units and video-based detectors, are being deployed in emerging applications like sustainable and intelligent transportation systems. The primary obstacles against the development of these IoT devices are various security threats and huge energy consumption. In this article, we study the problem of scheduling tasks onto a heterogeneous multiprocessor system on a chip (MPSoC) deployed in IoT for optimizing quality of security under energy, real-time, and task precedence constraints. We first provide a mixed-integer linear programming (MILP) formulation for allocating and scheduling dependent tasks with energy and real-time constraints on a heterogeneous MPSoC system to maximize system quality of security. In order to efficiently solve the formulated MILP, we then propose an analysis-based two-stage scheme that determines the allocation, operating frequency, and security service of tasks to maximize system quality of security while satisfying the design constraints. We finally carry out extensive simulation experiments to validate our proposed two-stage scheme and MILP approach. Simulation results demonstrate that the proposed two-stage scheme outperforms a number of representative existing approaches in saving energy and improving system quality of security. The results also show that the proposed MILP approach can achieve the best performance and the proposed two-stage scheme has a close performance to the MILP approach.

Software Defined Networking (SDN) provides flexibility and programmability for network management by using a layered structure composed of data plane, control plane, and application plane. A key enabling technique for the sustainability of SDN-based network infrastructure is the modeling of power consumed by SDN control planes. However, power modeling of control planes is not extensively investigated yet, and no generic methods have been developed for performance and power comparison of sustainable SDN control planes. In this paper, we propose analytical performance and power models for different network controllers by using queuing theory, and design a generic framework for performance and power evaluation of different sustainable SDN control planes. Extensive simulation results show that the proposed solution can precisely model the power and performance of the concerned SDN control planes such that different control planes can be benchmarked under a general framework, which enables the identification of suitable control planes for various SDN network applications.

CMOS scaling has greatly increased concerns for both lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the user's concern is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. This paper introduces a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we tackle the problem of maximizing availability for multicore real-time systems with consideration of permanent and transient faults. A framework is proposed to solve the system availability maximization problem. Experimental results on a hardware board and simulation results of synthetic tasks show that our scheme significantly improves system MTTF (and hence availability) compared with existing techniques.

In recent years, the Internet of Things (IoT) has promoted many battery-powered emerging applications, such as smart home, environmental monitoring, and human healthcare monitoring, where energy management is of particular importance. Meanwhile, there is an accelerated tendency toward mobility of IoT devices, either being transported by humans or being mobile by itself. Existing energy management mechanisms for battery-powered IoT fail to consider the two significant characteristics of IoT: 1) the approximate real-time computation and 2) the mobility of IoT devices, resulting in unnecessary energy waste and network lifetime decay. In this paper, we explore mobility-aware network lifetime maximization for battery-powered IoT applications that perform approximate real-time computation under the quality-of-service (QoS) constraint. The proposed scheme is composed of offline and online stages. At offline stage, an optimal mobility-aware task schedule that maximizes network lifetime is derived by using mixed-integer linear programming technique. Redundant executions due to mobility-incurred overlapping of a single task on different IoT devices are avoided for energy savings. At online stage, a performance-guaranteed and time-efficient QoS-adaptive heuristic based on cross-entropy method is developed to adapt task execution to the fluctuating QoS requirements. Extensive simulations based on synthetic applications and real-life benchmarks have been implemented to validate the effectiveness of our proposed scheme. Experimental results demonstrate that the proposed technique can achieve up to 169.52% network lifetime improvement compared to benchmarking solutions.

With the advent of heterogeneous multiprocessor architectures, efficient scheduling for high performance has been of significant importance. However, joint considerations of reliability, temperature, and stochastic characteristics of precedence-constrained tasks for performance optimization make task scheduling particularly challenging. In this paper, we tackle this challenge by using an affinity (i.e., probability)-driven task allocation and scheduling approach that decouples schedule lengths and thermal profiles of processors. Specifically, we separately model the affinity of a task for processors with respect to schedule lengths and the affinity of a task for processors with regard to chip thermal profiles considering task reliability and stochastic characteristics of task execution time and intertask communication time. Subsequently, we combine the two types of affinities, and design a scheduling heuristic that assigns a task to the processor with the highest joint affinity. Extensive simulations based on randomly generated stochastic and real-world applications are performed to validate the effectiveness of the proposed approach. Experiment results show that the proposed scheme can reduce the system makespan by up to 30.1% without violating the temperature and reliability constraints compared to benchmarking methods.

EtherCAT has become one of the leading real-time Ethernet solutions for networked industrial systems, where a reliable communication infrastructure is needed due to highly error-prone environments. However, existing work on EtherCAT mainly focuses on clock synchronization and timeliness improvement. The reliability of EtherCAT-based networked systems has largely been ignored. In this paper, we present a proportional integral derivative (PID)-based feedback control scheme that aims at enhancing reliability of networked systems under timing and system resource constraints. Instead of retransmitting data upon error detection, we use forward error control technique based on inequality of arithmetic and geometric means to achieve the required system reliability at a low deadline miss rate of messages. We further optimize the forward error control technique and design a fast and fair error resilient mechanism by using a cooperative game. In addition to reliability enhancement, our PID-based error control scheme can also improve the stability of a system in terms of deadline miss rate in the presence of burst errors. Simulation results show that the proposed scheme can achieve reliability enhancement of up to 91% compared to benchmarking methods.

Multiprocessor system-on-chip (MPSoC) has been widely used in many real-time embedded systems where both soft-error reliability (SER) and lifetime reliability (LTR) are key concerns. Many existing works have investigated them, but they focus either on handling one of the two reliability concerns or on improving one type of reliability under the constraint of the other. These techniques are thus not applicable to maximize SER and LTR simultaneously, which is highly desired in some real-world applications. In this paper, we study the joint optimization of SER and LTR for real-time MPSoCs. We propose a novel static task scheduling algorithm to simultaneously maximize SER and LTR for real-time homogeneous MPSoC systems under the constraints of deadline, energy budget, and task precedence. Specifically, we develop a new solution representation scheme and two evolutionary operators that are closely integrated with two popular multiobjective evolutionary optimization frameworks, namely NSGAII and SPEA2. Extensive experimental results on standard benchmarks and synthetic applications show the efficacy of our scheme. More specifically, our scheme can achieve significantly better solutions (i.e., LTR-SER tradeoff fronts) with remarkably higher hypervolume and can be dozens or even hundreds of times faster than the state-of-the-art algorithms. The results also demonstrate that our scheme can be applied to heterogeneous MPSoC systems and is effective in improving reliability for heterogeneous MPSoC systems.

Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

Indoor localization has become popular in recent years due to the increasing need of location-based services in mobile cyber-physical systems (CPS). The massive deployment of light emitting diodes (LEDs) further promotes the indoor localization using visual light. As a key enabling technique for mobile CPS, accurate indoor localization based on visual light communication remains nontrivial due to various non-idealities such as attenuation induced by unexpected obstacles. The anomalies of localization can potentially reduce the dependability of location-based services. In this article, we develop a novel indoor localization framework based on relative received signal strength. Most importantly, an efficient method is derived from the triangle inequality to automatically detect the abnormal LED lamps that are blocked by obstacles. These LED lamps are then ignored by our localization algorithm so that they do not bias the localization results, which improves the dependability of our localization framework. As demonstrated by the simulation results, the proposed techniques can achieve superior accuracy over the conventional approaches, especially when there exist abnormal LED lamps.

Human physical activity recognition is widely used in medical diagnosis, well-being management, and rehabilitation treatment. In spite of various Internet of Things (IoT) designs available in the literature, power resources often limit the lifetime of IoT. Regarding this weakness, this paper develops a new motion sensor system in wearable IoT (WIoT) for human physical activity recognition without any signal conditioning circuits. The triboelectricity-based physical model is explored in designing the motion sensor. It enables to collect motion signals caused by physical activities without any power supply. In addition, the triboelectric structure can be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion and a relatively stable voltage when involving continuous activities. Such a new design lays the foundations for constructing the next generation self-powered WIoT systems. Our new design has been extensively evaluated, where most common activities including sitting and standing, walking, climbing upstairs and downstairs, and running are used. The experimental results demonstrate that our system can achieve similar comparable performance as the state of the art for physical activity recognition at an average successful accuracy of over 80%. At the same time, our system reduces more than 25% energy consumption of the entire sensing hardware system which includes the sensor, microcontroller, and corresponding circuits.

Cyber-physical systems (CPSs) are characterized by the seamless integration and close interaction of cyber components (e.g., sensors, computation nodes, communication networks) and physical processes (e.g., mechanical devices, physical environment, humans). The cyber components monitor, analyze, and control the physical processes, and react to their changes through feedback loops. A classic example of CPSs is autonomous vehicles. These vehicles collect information of the surrounding physical environment via heterogeneous sensors such as cameras, radar, and LIDAR; process and analyze the multi-modal information at real time with advanced computing devices such as GPUs, application-specific SoCs and multicore CPUs; automatically make planning and control decisions; and continuously actuate the corresponding mechanical components. The cyber components of autonomous vehicles are much more intelligent and complex than those of traditional vehicles, and interact more directly and closely with the physical environment.

Physical unclonable function (PUF) is an advanced hardware security technology. Most conventional encryption approaches rely on the secure keys stored in nonvolatile memory, which are vulnerable to physical attacks. In contrast, PUF exploits the hardware fabrication variations to generate secure keys. As there are significant fabrication induced variations in carbon nanotube (CNT)-based circuits, they are natural candidates for building highly secure PUFs. However, existing PUFs are reported to be vulnerable to machine learning modeling attacks. In this paper, we develop a novel CNT PUF design through leveraging Lorenz chaotic system. Lorenz chaotic system magnifies the differences among responses of similar challenges. This salient feature gives the superior security performance, making the PUF design resistant to machine learning modeling attacks. Our experimental results demonstrate that these attacking methods can achieve very high bit-wise prediction rates for the standard CNT PUF. In contrast, when hacking the proposed Lorenz chaotic system-based CNT PUF, they become much less effective, where only up to 55% bit-wise prediction rates can be achieved.

The information exchange between the utility company and the smart community is crucial to the smart home cyber-physical systems. Yet the interaction between the two parties is vulnerable to many potential cyberattacks, among which the most striking ones are pricing cyberattacks and energy theft. Coordinated cyberattacks have emerged as an advanced attacking scheme with both pricing attack and energy theft applied in the cooperative manner, which can induce significant impact to smart home systems even if each attack is applied with only moderate strength. Such attacks cannot be effectively detected since the existing techniques are designed for detecting either pricing attack or energy theft without considering the impact due to coordinated attacks. This paper aims at developing the detection framework for coordinated cyberattacks considering coordinated impacts of various attacking strategies using an advanced continuous state partially observable Markov decision process. Handling coordinated attacks induces drastic increase in time complexity, which motivates us to propose innovative cross entropy state sampling and Fourier belief state approximation for the solving of developed detection framework. Our simulation results demonstrate that the coordinated cyberattack can reduce his/her electricity bill by 32.65%. In addition, the proposed detection technique can better capture coordinated attacks than the conventional detection technique, resulting in 10.31% increase in the hacker's bill.

As the conventional silicon-based CMOS technology marches toward the sub-10nm region, the problem of high power density becomes increasingly serious. Under this circumstance, the carbon-nanotube field effect transistors (CNFETs) emerge as a promising alternative to the conventional silicon-based CMOS devices. However, they experience a much larger variation than the silicon-based CMOS devices, which results in a large circuit delay variation and hence, a significant timing yield loss. One of the main variation sources is the carbon-nanotube (CNT) density variation. However, it shows a special property not existing for silicon-based CMOS devices, namely the asymmetric spatial correlation. In this work, we propose novel global placement algorithms to reduce the timing yield loss caused by the CNT density variation. To effectively reduce the statistical circuit delay, we first develop a statistical delay measure for a segment of gates. Based on this measure, we further develop a segment-based strategy and a path-based placement strategy to reduce the delays of the statistically critical paths. Experimental results demonstrated that both of our approaches effectively improve the timing yield.

With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has become a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interactions between user requests and the cloud service provider. Thus, the law of supply and demand in marketing is not fully explored in these pricing models. In this paper, we propose a dynamic pricing model based on the concept of user perceived value that accurately captures the real supply and demand relationship in the cloud service market. Subsequently, a profit maximization scheme is designed based on the dynamic pricing model that optimizes profit of the cloud service provider without violating service-level agreement. Finally, a dynamic closed loop control scheme is developed to adjust the cloud service price and multiserver configurations according to the dynamics of the cloud computing environment such as fluctuating electricity and rental fees. Extensive simulations using the data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model and the dynamic profit maximization scheme. Our algorithm can achieve up to 31.32 percent profit improvement compared to a state-of-the-art approach.

The papers from this special section addresses the topic of sustainable cyber-physical systems (CPSs). The research on CPSs addresses the close interactions between the cyber computational components and the physical components spanning from mechanical components, energy systems, human activities, to surrounding environment. CPS is expected to play a major role in the development of next-generation smart energy systems and data centers. Innovative computational methodologies such as green and energy efficient cyber-physical system design have become critical to enable the sustainable development of such systems. These technologies can be used to tackle various sustainability challenges, such as the reduction of energy induced from the large scale data center computing infrastructures, the improvement of computational efficiency in smart energy systems and connected vehicle systems, and the exploration of the renewable energy resources to mitigate classical energy usages.

High performance data centers serve as the backbone of the prevailing cloud computing paradigm. Among data centers with different operational structures, multi-tenant data centers (MTDCs) are increasingly popular among various internet service providers for the ease of deployment. Despite the offered benefits, MTDCs are vulnerable to various cyberattacks. An important cyberattack is energy theft which can be launched by malicious tenants to reduce monetary cost of the electricity consumption. It can be achieved through attacking smart meters in the data center to undercount the energy usage of the attacker. Since the attackers could consume an excessive amount of energy without incurring elevated utility cost, energy theft discourages frugality in terms of energy consumption, which is highly undesirable in the era of sustainable computing. Despite fruitful research results on MTDCs, none of existing works address energy theft. When energy theft occurs, it might be necessary for the data center operator to examine smart meter of all tenants to find the compromised ones which could induce excessive labor cost. Localization of energy theft detection is an effective way to limit the labor cost in detecting energy thefts in MTDCs. It can be facilitated through deploying Digital Protective Relays (DPR) in the data center where a DPR is a device for fault detection and event logging in the power system. In this paper, an anomaly rate range based dynamic programming algorithm is proposed for inserting minimal DPRs into the data center, where the anomaly rate range is computed using Minimum Covariance Determinant (MCD) algorithm. To the best of our knowledge, this is the first work addressing the energy theft issue in multi-tenant data centers. The simulation results demonstrate that our algorithm inserts 19.2 percent less DPRs into the data center compared to a natural baseline algorithm. Meanwhile, in an attempt to identify all energy theft cases, our DPR insertion solution requires 12.8 percent less tenants to be checked compared with the baseline algorithm. More importantly, we demonstrate that using MCD alone cannot achieve accurate detection while using DPR alone cannot handle collusive energy theft. In contrast, integrating DPR with MCD can achieve a high detection accuracy (of 97.6 percent) for collusive energy theft.

Petroleum industry has started to embrace the advanced Petroleum Cyber-Physical System (CPS) technologies. Offshore petroleum CPS is particularly difficult to build, mainly due to the challenge in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically used for leak detection, enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and large computational overhead. In this work, a cross entropy optimization based leak detection technique is proposed to detect the oil leak, which also facilitates the understanding of the oil leak induced marine pollution. Experimental results on a real Penglai oil spill event demonstrate that the proposed technique can effectively identify the sources of oil spills with accuracy of up to 90.78%.

In a petroleum cyber-physical system (CPS), interwell connectivity estimation is critical for improving petroleum production. An accurately estimated connectivity topology facilitates reduction in the production cost and improvement in the waterflood management. This paper presents the first study focused on computer-aided design for a petroleum CPS. A new CPS framework is developed to estimate the petroleum well connectivities. Such a framework explores an innovative water/oil index integrated with the advanced cross-entropy optimization. It is applied to a real industrial petroleum field with massive petroleum CPS data. The experimental results demonstrate that our automated estimations well match the expensive tracer-based true observations. This demonstrates that our framework is highly promising.

A cyber-physical system (CPS) is an integration of computation with physical processes whose behavior is defined by both computational and physical parts of the system. In this paper, we present a view of the challenges and opportunities for design automation of CPS. We identify a combination of characteristics that define the challenges unique to the design automation of CPS. We then present selected promising advances in depth, focusing on four foundational directions: combining model-based and data-driven design methods; design for human-in-the-loop systems; component-based design with contracts, and design for security and privacy. These directions are illustrated with examples from two application domains: smart energy systems and next-generation automotive systems.

No abstract available.

A smart community is a distributed system consisting of a set of smart homes which utilize the smart home scheduling techniques to enable customers to automatically schedule their energy loads targeting various purposes such as electricity bill reduction. Smart home scheduling is usually implemented in a decentralized fashion inside a smart community, where customers compete for the community level renewable energy due to their relatively low prices. Typically there exists an aggregator as a community wide electricity policy maker aiming to minimize the total electricity bill among all customers. This paper develops a new renewable energy aware pricing scheme to achieve this target. We establish the proof that under certain assumptions the optimal solution of decentralized smart home scheduling is equivalent to that of the centralized technique, reaching the theoretical lower bound of the community wide total electricity bill. In addition, an advanced cross entropy optimization technique is proposed to compute the pricing scheme of renewable energy, which is then integrated in smart home scheduling. The simulation results demonstrate that our pricing scheme facilitates the reduction of both the community wide electricity bill and individual electricity bills compared to the uniform pricing. In particular, the community wide electricity bill can be reduced to only 0.06 percent above the theoretic lower bound.

Petroleum Cyber-Physical System (CPS) marks the beginning of a new chapter of the oil and gas industry. Combining vast computational power with intelligent Computer Aided Design (CAD) algorithms, petroleum CPS is capable of precisely modeling the flow of fluids over the entire petroleum reservoir and leveraging the massive field data remotely collected at the production wells. It provides field operators with valuable insights into the geological structure and remaining reserves of the reservoir for optimizing their operational strategies. Despite such benefits, petroleum CPS is vulnerable to various cyberattacks that jeopardize the integrity of the field data collected at production wells. Given manipulated field data, CAD software would generate an inaccurate reservoir model which misleads the field operators.

The papers in this special section focus on next generation urbanization that incorporates smart city development. The development of smart cities is viewed as the key to the next generation urbanization process for improving the efficiency, reliability, and security of a traditional city. The concept of smart city includes various aspects such as environmental sustainability, social sustainability, regional competitiveness,natural resources management, cybersecurity, and quality of life improvement. With the massive deployment of networked smart devices/sensors, an unprecedentedly large amount of sensory data can be collected and processed by advanced computing paradigms, which are the enabling techniques for smart city. For example, given historical environmental, population, and economic information, salient modeling and analytics are needed to simulate the impact of potential city planning strategies, which will be critical for intelligent decision-making.

The research on cyber-physical systems (CPSs) addresses the close interactions between the embedded cyber components and the dynamic physical components that could involve mechanical components, energy systems, human activities, and surrounding environment. Some example CPSs include automotive systems, energy systems, robot systems, and cyber-physical biochip systems.

In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing cyberattacks. Our simulation results demonstrate that the pricing cyberattack can reduce the cyberattacker's bill by 34.3 percent at cost of the increase of others' bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.

The concept of smart home has recently gained significant popularity. Despite that it offers improved convenience and cost reduction, the prevailing smart home infrastructure suffers from vulnerability due to cyberattacks. It is possible for hackers to launch cyberattacks at the community level while causing a large area power system blackout through cascading effects. In this paper, the cascading impacts of two cyberattacks on the predicted dynamic electricity pricing are analyzed. In the first cyberattack, the hacker manipulates the electricity price to form peak energy loads such that some transmission lines are overloaded. Those transmission lines are then tripped and the power system is separated into isolated islands due to the cascading effect. In the second cyberattack, the hacker manipulates the electricity price to increase the fluctuation of the energy load to interfere the frequency of the generators. The generators are then tripped by the protective procedures and cascading outages are induced in the transmission network. The existing technique only tackles overloading cyberattack while still suffering from the severe limitation in scalability. Therefore, based on partially observable Markov decision processes, a hierarchical detection framework exploring community decomposition and global policy optimization is proposed in this work. The simulation results demonstrate that our proposed hierarchical computing technique can effectively and efficiently detect those cyberattacks, achieving the detection accuracy of above 98%, while improving the scalability.

Electron beam lithography (EBL) is a promising, maskless solution for the technology beyond 14nm logic nodes. To overcome its throughput limitation, industry has proposed character projection (CP) technique, where some complex shapes (characters) can be printed in one shot. Recently, the traditional EBL system was extended into a multi-column cell (MCC) system to further improve the throughput. In an MCC system, several independent CPs are used to further speed-up the writing process. Because of the area constraint of stencil, the MCC system needs to be packed/planned carefully to take advantage of the characters. In this article, we prove that the overlapping aware stencil planning (OSP) problem is NP-hard. Then we propose E-BLOW, a tool to solve the MCC system OSP problem. E-BLOW involves several novel speedup techniques, such as successive relaxation and dynamic programming. Experimental results show that, compared with previous works, E-BLOW demonstrates better performance for both the conventional EBL system and the MCC system.

Despite the increasing popularity of the smart home concept, such a technology is vulnerable to various security threats such as pricing cyberattacks. There are some technical advances in developing detection and defense frameworks against those pricing cyberattacks. However, none of them considers the impact of net metering, which allows the customers to sell the excessively generated renewable energy back to the grid. At a superficial glance, net metering seems to be irrelevant to the cybersecurity, while this paper demonstrates that its implication is actually profound.

A cyber-physical system (CPS) is a composition of independently interacting components, including computational elements, communications and control systems. Applications of CPS institute at different levels of integration, ranging from nation-wide power grids, to medium scale, such as the smart home, and small scale, e.g. ubiquitous health care systems including implantable medical devices. Cyber-physical systems primarily transmute how we interact with the physical world, with each system requiring different levels of security based on the sensitivity of the control system and the information it carries. Considering the remarkable progress in CPS technologies during recent years, advancement in security and trust measures is much needed to counter the security violations and privacy leakage of integration elements. This paper focuses on security and privacy concerns at different levels of the composition and presents system level solutions for ensuring the security and trust of modern cyber-physical systems.

Demand response (DR) is an effective mechanism in improving power system efficiency and reducing energy cost for customers. However, DR processes might be vulnerable to cyber attacks from the usage of advanced metering infrastructure and wide-area network to exchange information. In this paper, we study potential attacks for a proactive demand participation scheme we recently proposed and for a conventional passive demand response scheme, particularly focusing on guideline price manipulation attacks. Our experiment results demonstrate that 1) guideline price manipulations may significantly lower the attacker's own electricity consumption cost while increasing other customers' cost, for both proactive and passive schemes; 2) such impact is less severe in the proactive scheme, i.e., the proactive demand participation scheme is more robust with respect to guideline price manipulation than the conventional DR.

Microfluidic biochip technology integrates miniaturized components into a chip that can perform traditional biochemical laboratory procedures. Commercial impact is highlighted by the recent acquisition of Advanced Liquid Logic by Illumina Inc., a leader in DNA sequencing and biomolecular analysis. Due to the inherent variability involved in many biochemical processes, uncertainties manifest themselves in many ways in microluidics. Cyber-physical integration of on-chip sensors permits feedback-driven monitoring in-real time to detect and correct errors, along with other benefits such as adaptive control and dynamic re-synthesis. This paper overviews low-based and digital (droplet-based) microluidic biopchips, and discusses the state-of-the-art in microluidic device fabrication, the interplay between sensor feedback and adaptive control software, and practical experiences relating to biochip cyber-physical integration. It demonstrates the connections between the many fundamental principles of chip design and engineering, and the needs of the biochip community.

The advanced metering infrastructure (AMI) has become indispensable in a smart grid to support the real time and reliable information exchange. Such an infrastructure facilitates the deployment of smart meters and enables the automatic measurement of electricity energy usage. Inside a community of networked smart homes, the total electricity bill is computed based on the community-wide energy consumption. Thus, the coordinated energy scheduling among smart homes is important since the energy consumptions from some customers can potentially impact bills of others. Given a community of networked smart homes, this paper analyzes the energy theft cyberattack, which manipulates the energy usage metering for bill reduction and develops a detection technique based on Bollinger bands and partially observable Markov decision process (POMDP). Due to the high complexity of the POMDP-solving process, a probabilistic belief-state-reduction-based adaptive dynamic programming technique is also designed to improve the detection efficiency. Our simulation results demonstrate that the proposed technique can successfully detect 92.55% energy thefts on an average while effectively mitigating the impact to the community. In addition, our probabilistic belief-state-reduction-based adaptive dynamic programming technique can reduce the runtime by up to 55.86% compared to that without state reduction.

Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A countermeasure technique which uses support vector regression and impact difference for detecting anomaly pricing is then proposed. These pricing cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyber-attacks in the smart home context. Our simulation results demonstrate that the pricing cyberattack can reduce the attacker's bill by 34.3% at the cost of the increase of others' bill by 7.9% on average. In addition, the pricing cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results show that the proposed countermeasure technique can effectively detect the electricity pricing manipulation.

Building automation is the key to sustainable, safe and comfortable buildings as well as to the integration of buildings into smart grids and with other external applications such as cloud computing. In a typical smart community scenario, various household appliances of multiple residential users are connected via a Home Area Network. HANs are further connected to the local power distribution network via smart meters, forming a LAN, where also renewables communicate. Methods are needed to design and integrate networks with hundred thousands of nodes in a cost-efficient way. The key to providing improved services in building automation is to process complex scenarios in an adequate way. Furthermore, building automation systems must be seen as dependable systems covering both safety and security aspects. The main objective of this Special Section is to bring the ideas of the worldwide research community into a common platform, to present the latest advances and developments.

For the last several technology generations, VLSI designs in new technology nodes have had to confront the challenges associated with reduced scaling in wire delays. The solution from industrial back-end-of-line process has been to add more and more thick metal layers to the wiring stacks. However, existing physical synthesis tools are usually not effective in handling these new thick layers for design closure. To fully leverage these degrees of freedom, it is essential for the design flow to provide better communication among the timer, the router, and different optimization engines. This work proposes a new algorithm, CATALYST, to perform congestion- and timing-aware layer directive assignment. Our flow balances routing resources among metal stacks so that designs benefit from the availability of thick metal layers by achieving improved timing and buffer usage reduction while maintaining routability. Experiments demonstrate the effectiveness of the proposed algorithm.

Voltage partitioning on functional units/blocks targeting peak power minimization has been demonstrated to be effective for energy reduction considering voltage island shutdown impact. However, the existing technique can only solve this NP-hard problem efficiently on small designs. In this paper, a much faster linear time approximation scheme is proposed, which can approximate the optimal voltage partitioning solution within a factor 1 + ε, for any 0 < ε < 1, and runs in O(n + 1/εO(1)) time, where n is the number of functional units. There are multiple ingredients in such a surprisingly low time complexity algorithm. It first categorizes all the functional units into big functional units and small functional units using an ε related threshold. Subsequently, a rounding based dynamic programming procedure is performed to handle big functional units and a linear programming based algorithm is performed to handle small functional units, which is followed by the discretization of the continuous linear programming solution. Moreover, through the exploration of the unique nature of our problem, a greedy algorithm is proposed to optimally solve the linear programming formulation in a combinatorial fashion. Further, since patching a salient partitioning solution of big functional units with that of small functional units could lead to a much worse combined solution, a highly efficient enumeration process running in time independent of n is proposed. The experimental results demonstrate that our algorithm runs very fast. It needs only 0.3 second to partition a testcase with 5000 functional units which is more than 10000X faster than the existing algorithm while still reducing the peak power by 7.4%.

As VLSI technology enters the nanoscale regime, design reliability is becoming increasingly important. A major design reliability concern arises from electromigration which refers to the transport of material caused by ion movement in interconnects. Since the lifetime of an interconnect drastically depends on the current flowing through it, the electromigration problem aggravates with increasingly growing thinner wires. Further, the current-density-induced interconnect thermal issue becomes much more severe with larger current. To mitigate the electromigration and the current-density-induced thermal effects, interconnect current density needs to be reduced. Assigning wires to thick metals increases wire volume, and thus, reduces the current density. However, overstretching thick-metal assignment may hurt routability. Thus, it is highly desirable to minimize the thick-metal usage, or total wire cost, subject to the reliability constraint. In this paper, the minimum cost reliability-driven routing, which consists of Steiner tree construction and layer assignment, is considered. The problem is proven to be NP-hard and a highly effective iterative rounding-based integer linear programming algorithm is proposed. In addition, a unified routing technique is proposed to directly handle multiple current levels, which is critical in analog VLSI design. Further, the new algorithm is extended to handle blockage. Our experiments on 450 nets demonstrate that the new algorithm significantly outperforms the state-of-the-art work [CHECK END OF SENTENCE] with up to 14.7 percent wire reduction. In addition, the new algorithm can save 11.4 percent wires over a heuristic algorithm for handling multiple currents.

With advancing technology, large dynamic power consumption has significantly limited circuit miniaturization. Minimizing peak power consumption, which is defined as the maximum power consumption among all voltage partitions, is important since it enables energy saving from the voltage island shutdown mechanism. In this paper, we prove that the peak power driven voltage partitioning problem is NP-complete and propose an efficient provably good fully polynomial time approximation scheme for it. The new algorithm can approximate the optimal peak power driven voltage partitioning solution in O(m2(mn/ϵ4)) time within a factor of (1 + ϵ) for sufficiently small positive e, where n is the number of circuit blocks and m is the number of partitions which is a small constant in practice. Our experimental results demonstrate that the dynamic programming cannot finish for even 20 blocks while our new approximation algorithm runs fast. In particular, varying e, orders of magnitude speedup can be obtained with only 0.6% power increase. The tradeoff between the peak power minimization and the total power minimization is also investigated. We demonstrate that the total power minimization algorithm obtains good results in total power but with quite large peak power, while our peak power optimization algorithm can achieve on average 26.5% reduction in peak power with only 0.46% increase in total power. Moreover, our peak power driven voltage partitioning algorithm is integrated into a simulated annealing based floorplanning technique. Experimental results demonstrate that compared to total power driven floorplanning, the peak power driven floorplanning can significantly reduce peak power with only little impact in total power, HPWL, estimated power ground routing cost, level shifter cost and runtime. Further, when the voltage island shutdown is performed, peak power driven voltage partitioning can lead to over 10% more energy saving than a greedy frequency based voltage partitioning when multiple idle block sequences are considered.

It is increasingly challenging to analyze present day large-scale power delivery networks (PDNs) due to the drastically growing complexity in power grid design. To achieve greater runtime and memory efficiencies, a variety of preconditioned iterative algorithms has been investigated in the past few decades with promising performance, while incremental power grid analysis also becomes popular to facilitate fast re-simulations of corrected designs. Although existing preconditioned solvers, such as incomplete matrix factor-based preconditioners, usually exhibit high efficiency in memory usage, their convergence behaviors are not always satisfactory. In this work, we present a novel hierarchical support-graph preconditioned iterative algorithm that constructs preconditioners by generating spanning trees in power supply networks for fast power grid analysis. The support-graph preconditioner is efficient for handling complex power grid structures (regular or irregular grids), and can facilitate very fast incremental analysis. Our experimental results on IBM power grid benchmarks show that compared with the best direct or iterative solvers, the proposed support-graph preconditioned iterative solver achieves up to 3.6X speedups for DC analysis, and up to 22X speedups for incremental analysis, while reducing the memory consumption by a factor of four.

This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences.

Decoupling capacitor (decap) has been widely used to effectively reduce dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient (CG) methods, which can be prohibitively expensive for large-scale decap budgeting problems and cannot be easily parallelized. In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. To achieve the high efficiency, a sensitivity-guided cross-entropy (SCE) algorithm is introduced which integrates CE with a partitioning-based sampling strategy to effectively reduce the solution space in solving the large-scale decap budgeting problems. Compared to improved CG method and conventional CE method, SCE with Latin hypercube sampling method (SCE-LHS) can provide 2× speedups, while achieving up to 25% improvement on power supply noise. To further improve decap optimization solution quality, SCE with sequential importance sampling (SCE-SIS) method is also studied and implemented. Compared to SCE-LHS, in similar runtime, SCE-SIS can lead to 16.8% further reduction on the total power supply noise.

Decoupling capacitor (decap) placement has been widely adopted as an effective way to suppress dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient methods, which can be prohibitively expensive for large-scale decap budgeting problems. We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. To achieve high efficiency, a sensitivity-guided cross entropy (SCE) algorithm is proposed which integrates CE with a partitioning-based sampling strategy to effectively reduce the dimensionality in solving the large scale decap budgeting problems. Extensive experiments on industrial power grid benchmarks show that the proposed SCE method converges 2X faster than the prior methods and 10X faster than the standard CE method, while gaining up to 25% improvement on power grid supply noise. Importantly, the proposed SCE algorithm is parallel-friendly since the simulation samples of each SCE iteration can be independently obtained in parallel. We obtain up to 1.9X speedup when running the SCE decap budgeting algorithm on a dual-core-dual-GPU system.

Increasing transistor density in nanometer integrated circuits has resulted in large on-chip power density. As a high-level power optimization technique, voltage partitioning is effective in mitigating power density. Previous works on voltage partitioning attempt to address it through minimizing total power consumption over all voltage partitions. Since power density significantly impacts thermal-induced reliability, it is also desired to directly mitigate peak power density during voltage partitioning. Unfortunately, none of the existing works consider this. This paper proposes an efficient optimal voltage partitioning algorithm for peak power density minimization. Based on novel algorithmic techniques such as implicit power density binary search, the algorithm runs in O(n log n + m2log2n) time, where n refers to the number of functional units and m refers to the number of partitions/voltage levels. Our experimental results on large testcases demonstrate that large amount of (about 9.7×) reduction in peak power density can be achieved compared to a natural greedy algorithm, while the algorithm still runs very fast. It needs only 14.15 seconds to optimize 1M functional units.

In a complete physical synthesis flow, optimization transforms, that can improve the timing on critical paths that are already well-optimized by a series of powerful transforms (timing driven placement, buffering and gate sizing) are invaluable. Finding such a transform is quite challenging, to say nothing of efficiency. This work explores innovative cloning (gate duplication) techniques to improve timing-closure in a physical synthesis environment.

In this paper, a new document image watermarking method based on a secure partitioning scheme is proposed and tested. In the method, a document image is securely divided into weight-invariant partitions, followed by selectively modifying characters to embed watermarks. The high security of a watermark results from applying a probabilistic metaheuristic algorithm, namely the Ant Colony System (ACS), to approximate the involved Bottleneck Hamiltonian Path Problem to generate key-dependent image partitions. For better efficiency, the farthest point heuristic and the multi scale strategy are introduced into the ant colony system. Our experimental results demonstrate that the proposed watermarking scheme is secure, efficient, and robust to common attacks. The proposed secure partition scheme could serve as a general framework to introduce high security to prevailing watermarking techniques.

The gap between VLSI technology and fabrication technology leads to strong refractive effects in lithography. Consequently, it is a huge challenge to reliably print layout features on wafers. The quality and robustness of lithography directly depend on layout patterns. It becomes imperative to consider the manufacturability issue during layout design such that the burden of lithography process can be alleviated. In this paper, three algorithms, namely, cell flipping algorithm, single row optimization approach and multiple row optimization approach, are proposed to tune any existing cell placement to be lithography friendly. These algorithms are based on dynamic programming and graph theoretic approaches, and can provide different tradeoff between critical dimension (CD) variation reduction and wirelength increase. Using lithography simulations, our experimental results demonstrate that over 15% CD variation reduction can be obtained in post-OPC stage by the new approaches while only less than 1% additional wire is introduced.

As VLSI technology enters the nanoscale regime, interconnect delay has become the bottleneck of the circuit timing. As one of the most powerful techniques for interconnect optimization, buffer insertion is indispensable in the physical synthesis flow. Buffering is known to be NP-complete and existing works either explore dynamic programming to compute optimal solution in the worst-case exponential time or design efficient heuristics without performance guarantee. Even if buffer insertion is one of the most studied problems in physical design, whether there is an efficient algorithm with provably good performance still remains unknown.

As VLSI technology reaches 45nm technology node, leakage power optimization has become a major design challenge. Threshold voltage (vt) assignment has been extensively studied, due to its effectiveness in leakage power reduction. In contrast to the efficiently solvable continuous vt assignment problem, the discrete vt assignment problem is known to be NP-hard. All of the existing techniques are heuristics without performance guarantee due to the NP-hardness nature of the problem. It is still not known whether there is any rigorous approximation algorithm for the discrete vt assignment problem.

As VLSI technology moves to the 65nm node and beyond, interconnect delay greatly limits the circuit performance. As a critical component in interconnect synthesis, layer assignment manifests enormous potential in drastically reducing wire delay. This is due the fact that wires on thick metals are much less resistive than those on thin metals. Nevertheless, it is not desired to assign all wires to thick metals and the right strategy is to only use minimal thick-metal routing resources for meeting the timing constraints. This timing driven minimum cost layer assignment problem is NP-Complete, and a fast algorithm with provable approximation bound is highly desired.

With increasing time-to-market pressure and shortening semiconductor product cycles, more and more chips are being designed with library-based methodologies. In spite of this shift, the problem of discrete gate sizing has received significantly less attention than its continuous counterpart. On the other hand, cell sizes of many realistic libraries are sparse, for example, geometrically spaced, which makes the nearest rounding approach inapplicable as large timing violations may be introduced. Therefore, it is highly desirable to design an effective algorithm to handle this discrete gate-sizing problem. Such an algorithm is proposed in this paper. The algorithm is a continuous-solution-guided dynamic-programming-like approach. A set of novel techniques, such as locality-sensitive-hashing-based solution pruning, is also proposed to accelerate the algorithm. Our experimental results demonstrate that 1) the nearest rounding approach often leads to large timing violations and 2) compared to the well-known Coudert's approach, the new algorithm saves up to 21% in area cost while still satisfying the timing constraint.

As VLSI technology enters the nanoscale regime, interconnect delay becomes the bottleneck of circuit performance. Compared to gate delays, wires are becoming increasingly resistive which makes it more difficult to propagate signals across the chip. However, more advanced technologies (65 nm and 45 nm) provide relief as the number of metal layers continues to increase. The wires on the upper metal layers are much less resistive and can be used to drive further and faster than on thin metals. This provides an entirely new dimension to the traditional wire sizing problem, namely, layer assignment for efficient timing closure. Assigning all wires to thick metals improves timing, however, routability of the design may be hurt. The challenge is to assign minimal amount of wires to thick metals to meet timing constraints. In this paper, the minimum cost layer assignment problem is proven to be NP-Complete. As a theoretical solution for NP-complete problems, a polynomial time approximation scheme is proposed. The new algorithm can approximate the optimal layer assignment solution by a factor of 1 + isin in O(mlog logmldrn3/isin2) time for 0 < isin < 1, where n is the number of nodes in the tree and m is the number of routing layers. This work presents the first theoretical advance for the timing-driven minimum cost layer assignment problem. In addition to its theoretical guarantee, the new algorithm is highly practical. Our experiments on 500 test cases demonstrate that the new algorithm can run 2times faster than the optimal dynamic programming algorithm with only 2% additional wire.

As technology scaling advances beyond 65 nanometer node, more devices can fit onto a chip, which implies continued growth of design size. The increased wire delay dominance due to finer wire widths makes design closure an increasingly challenging problem. Interconnect synthesis techniques, such as buffer insertion/sizing and wire sizing, have proven to be the critical part of a successful timing closure optimization tool.

Abstract—With increasing time-to-market pressure and short-ening semiconductor product cycles, more and more chips are being designed with library-based methodologies. In spite of this shift, the problem of discrete gate sizing has received significantly less attention than its continuous counterpart. On the other hand, cell sizes of many realistic libraries are sparse, for example, geo-metrically spaced, which makes the nearest rounding approach inapplicable as large timing violations may be introduced. There-fore, it is highly desirable to design an effective algorithm to handle this discrete gate-sizing problem. Such an algorithm is pro-posed in this paper. The algorithm is a continuous-solution-guided dynamic-programming-like approach. A set of novel techniques, such as locality-sensitive-hashing-based solution pruning, is also proposed to accelerate the algorithm. Our experimental results demonstrate that 1) the nearest rounding approach often leads to large timing violations and 2) compared to the well-known Coudert’s approach, the new algorithm saves up to 21 % in area cost while still satisfying the timing constraint. Index Terms—Discretization, dynamic programming (DP), gate sizing, pruning, sparse cell library. I.

Twisted differential line structure can effectively reduce crosstalk noise on global bus, which foresees a wide applicability. However, measured performance based on fabricated circuits is much worse than simulated performance based on the layout. It is suspected that the via resistance variation is the cause.

VLSI design is increasingly sensitive to variations which often degrade the parametric yield. Post-silicon tuning techniques can compensate for specific variations on the die and thus significantly improve the yield. Previous works on adaptivity optimization for post-silicon tuning focus on either logic signal tuning or clock signal tuning. This paper proposes the first unified adaptivity optimization on logical and clock signal tuning, which enables us to significantly save resource. In addition, it does not need any assumption on variation distributions. Our unified optimization is based on a novel linear programming formulation which can be efficiently solved by an advanced robust linear programming technique. Due to the discrete nature of the problem, the continuous solution obtained from linear programming is then efficiently discretized. This procedure involves binary search accelerated dynamic programming, batch based optimization, and Latin Hypercube sampling based fast simulation. Our experimental results demonstrate that up to 50% area cost reduction can be obtained by the unified optimization compared to optimization on logic or clock alone. In addition, the proposed discretization approach significantly outperforms the alternatives in terms of solution quality and runtime.

When VLSI technology scales toward 45nm, the lithography wavelength stays at 193nm. This large gap results in strong refractive effects in lithography. Consequently, it is a huge challenge to reliably print layout features on wafers and the printing is more susceptible to lithographic process variations. Although resolution enhancement techniques can mitigate this manufacturability problem, their capabilities are overstretched by the continuous shrinking of VLSI feature size. On the other hand,the quality and robustness of lithography directly depend on layout patterns. Therefore, it becomes imperative to consider the manufacturability issue during layout design such that the burden of lithography process can be alleviated.

As a prevalent constraint, sharp slew rate is often required in circuit design, which causes a huge demand for buffering resources. This problem requires ultrafast buffering techniques to handle large volume of nets while also minimizing buffering cost. This problem is intensively studied in this paper. First, a highly efficient algorithm based on dynamic programming is proposed to optimally solve slew buffering with discrete buffer locations. Second, a new algorithm using the maximum matching technique is developed to handle the difficult cases in which no assumption is made on buffer input slew. Third, an adaptive buffer selection approach is proposed to efficiently handle slew buffering with continuous buffer locations. Fourth, buffer blockage avoidance is handled, which makes the algorithms ready for practical use. Experiments on industrial netlists demonstrate that our algorithms are very effective and highly efficient: we achieve about 90x speedup and save up to 20% buffer area over the commonly used van Ginneken style buffering. The new algorithms also significantly outperform previous works that indirectly address the slew buffering problem.

Conventionally, the topology of signal net routing is almost always restricted to Steiner trees, either unbuffered or buffered. However, introducing redundant paths into the topology (which leads to non-tree) may significantly improve timing performance as well as tolerance to open faults and variations. These advantages are particularly appealing for timing critical net routings in nanoscale VLSI designs where interconnect delay is a performance bottleneck and variation effects are increasingly remarkable. We propose Steiner network construction heuristics which can generate either tree or non-tree with different slack-wirelength tradeoff, and handle both long path and short path constraints. We also propose heuristics for simultaneous Steiner network construction and buffering, which may provide further improvement in slack and resistance to variations. Furthermore, incremental non-tree delay update techniques are developed to facilitate fast Steiner network evaluations. Extensive experiments in different scenarios show that our heuristics usually improve timing slack by hundreds of pico seconds compared to traditional approaches. When process variations are considered, our heuristics can significantly improve timing yield because of nominal slack improvement and delay variability reduction.

As a prevalent constraint, sharp slew rate is often required in circuit design which causes a huge demand for buffering resources. This problem requires ultra-fast buffering techniques to handle large volume of nets, while also minimizing buffering cost. This problem is intensively studied in this paper. First, a highly efficient algorithm based on dynamic programming is proposed to optimally solve slew buffering with discrete buffer locations. Second, a new algorithm is developed to handle the difficult cases in which no assumption is made on buffer input slew. Third, an adaptive buffer selection approach is proposed to efficiently handle slew buffering with continuous buffer locations. Experiments on industrial netlists demonstrate that our algorithms are very effective and highly efficient: we achieve > 100X speed up and save up to 40% buffer area over the commonly-used van Ginneken style buffering.

Conventionally, signal net routing is almost always implemented asSteiner trees. However, non-tree topology is often superior on timing performance as well as tolerance to open faults and variations. These advantages are particularly appealing for timing critical net routings in nano-scale VLSI designs where interconnect delay is a performance bottleneck and variation effects are increasingly remarkable. We propose Steiner network construction heuristics which can generate either tree or non-tree with different slack-wirelength tradeoff, and handle both long path and short path constraints. Incremental non-tree delay update techniques are developed to facilitate fast Steiner network evaluations. Extensive experiments in different scenarios show that our heuristics usually improve timing slack by hundreds of pico seconds compared to traditional tree approaches.

Most existing buffering algorithms neglect the impact of inductance on circuit performance, which causes large error in circuit analysis and optimization. Even for the approaches considering inductance effects, their delay models are too simplistic to catch the actual performance. As delay-length dependence is approaching linear with inductance effect [1], fewer buffers are needed to reduce RLC delay. This motivates this work to propose a new algorithm for RLC buffer insertion. In this paper, a new buffer insertion algorithm considering inductance for intermediate and global interconnect is proposed, based on downstream impedance instead of traditional downstream capacitance. A new pruning technique that provides tremendous speedup and a new frequency estimation method that is very accurate in delay computation are also proposed. Experiments on industrial netlists demonstrate that our new algorithm reduces the number of buffers up to 34.4% over the traditional van Ginneken’s algorithm that ignores inductance. Our impedance delay estimation is very accurate compared to SPICE simulations, with only 10 % error while the delay model used in the previous RLC algorithm has 20 % error [2]. The accurate delay model not only reduces the number of buffers, but also brings high fidelity to the buffer solutions. Incorporating slew constraints, the algorithm is accelerated by about 4 × with only slight degradation in solution quality. 1.