Time-sensitive networking (TSN), as a solution to the nondeterministic communication of traditional Ethernet, meets the real-time and deterministic communication requirements of intelligent automobiles. There are three traffic types in intelligent automobiles, including time-triggered (TT) flows, audio-video-bridging (AVB) flows, and best-effort (BE) flows, and TSN proposes the gate control list (GCL) to control the transmission of the above traffic. The current GCL synthesis usually serves the hard real-time TT flows but ignores the delay of non-TT flows; and it introduces guard bands to ensure the noninterference transmission of TT flows, inevitably wasting bandwidth. Therefore, this article proposes a design synthesis and optimization strategy to improve the transmission of AVB flows and bandwidth utilization while ensuring the real-time performance of TT flows. This strategy first adopts the initial time window design to allocate the antecedent time windows for AVB flows, then transmits AVB flows according to the deadline to enhance the scheduling of AVB flows, and finally flexibly adjusts the time windows of TT flows and AVB flows according to the size of AVB flows to improve bandwidth utilization. Experimental results show the effectiveness of the proposed strategy in improving the transmission of AVB flows and bandwidth utilization compared to the state-of-the-art methods.

Controller area network (CAN) protocol is an efficient standard enabling communication among electronic control units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle intrusion detection system (IDS) is developed. The challenge is that existing techniques of IDSs rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges multidimensional long short-term memory (MD-LSTM) and self-attention mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including stacked long short-term memory (LSTM), MD-LSTM, etc. Experiments show that our approach has the best-detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies, such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better-energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high-detection accuracy of 98.81% and a low latency of 1.88 ms.

Controller area network (CAN) is widely adopted in automobiles and susceptible to cyber attacks with the development of intelligent connected vehicles. While neural networks have demonstrated high accuracy in detection of such attacks, they consume a large amount of resources, hence unsuitable to be directly used for the automotive domain. In this work, we propose a lightweight intrusion detection system (LIDS) for CAN. It first filters out denial-of-service (DoS) and Fuzzy attacks through list screening, following which, a multilayer perceptron (MLP) model is deployed to predict Impersonation attacks. Leveraging this combination, the detection accuracy is kept and the resources required are significantly reduced. LIDS is able to run on small hardware with 520-kB memory and CPU of 240 MHz. Its power consumption is one order of magnitude lower than the existing works, thus an excellent candidate for protection of CAN in automobiles.

The co-verification of safety and security is a necessary process for safety- and security-critical distributed applications, but conflicts exist between safety and security. The state-of-the-art Block-based Vulnerability Pre-assignment (BVP) and Reversed Block-based Time Pre-assignment (RBTP) co-verification techniques of safety and security have three main limitations: 1) only co-verifying the boundary values and ignoring the verification of non-boundary values; 2) algorithm redundancy (i.e., BVP and RBTP must be used simultaneously) makes the verification process complex and cumbersome; and 3) only one safety attribute and one security attribute participate in the co-verification. In this study, we explore the causal mechanisms for the mutual influence between safety and security: 1) they compete with each other in relation to Worst Case Execution Time (WCET); 2) there is a lack of cooperation between the two; and 3) both are pursuing maximum performance for each individual. Above causal mechanisms precisely conforms to the problem of maximizing benefits in non-cooperative games (i.e., Nash equilibrium). Therefore, we propose the Fast Game Verification (FGV) based on non-cooperative game to co-verify reliability in safety and confidentiality in security. FGV achieves non-boundary value co-verification and avoids redundancy. We develop Fast Game Verification plus plus (FGV++) algorithm to co-verify multiple safety attributes and multiple security attributes. We conduct actual cases of distributed applications. In terms of the co-verification of two attributes, FGV demonstrates average acceptance rate of 59.96% within 0.638 s, surpassing both BVP&RBTP by 6.94% (within 0.812 s); FGV++ achieves average acceptance rate of 58.73% within 0.663 s, exhibiting a 4.53% advantage over BVP and RBTP. In the co-verification of four attributes, the average acceptance rate of FGV++ has increased to 61.07% within 1.34102 s.

The precise classification of human activities through sensor data collection and analysis addresses the broad demands in healthcare, security surveillance, and smart home applications amidst the rapid development of IoT technology. However, achieving high efficiency and accuracy remains a significant challenge for HAR algorithms. This paper proposes a HAR algorithm based on Transformer and Random Forest (Transformer-RF). The algorithm extracts and integrates multimodal features in the time domain, frequency domain, and statistical metrics, constructing one-dimensional and two-dimensional feature sets through feature transformation. The Transformer component, leveraging self-attention mechanisms, captures long-range dependencies and extracts global contextual information. Concurrently, the Random Forest component randomly selects features and samples, enhancing model diversity and improving complex human activity recognization capabilities. Experimental results demonstrate that compared with state-of-the-art algorithms, the Transformer-RF model achieves superior performance on both one-dimensional and two-dimensional feature sets, with an accuracy of up to 94.17%. The primary contribution of this paper lies in the introduction of an innovative Transformer-RF human activity recognization method, which not only ensures high accuracy but also exhibits excellent generalization capability and practical application potential. This study provides new insights and technical solutions for the field of human activity recognization, offering significant theoretical and practical value.

As promising industrial embedded networks, Controller Area Networks with Flexible Data-rate (CAN-FD) are widely used in time-sensitive domains, such as automotive networks. However, the absence of built-in security mechanisms in CAN-FD necessitates the development of security protection mechanisms. The existing Lightweight Authentication for Secure Automotive Networks (LASAN) framework focuses on enhancing the security of CAN/CAN-FD communication but neglects the conflict between security and delay. In this study, we conduct a thorough analysis of the causal mechanism related to the security and delay of LASAN and propose a static message scheduling method called Cooperative Swapping Approach (CSA) to achieve secure and low-delay CAN-FD communication. CSA is to minimize the end-to-end delay of precedence-constrained CAN-FD applications by swapping message positions in a valid message sequence. Nevertheless, exchanging message positions may impact the precedence dependencies between messages; therefore, we propose a novel Cooperative Transform Approach (CTA) within the CSA to efficiently preserve these precedence constraints. Valid message sequences with minimal end-to-end delays of a motivation example and an Adaptive Cruise Control (ACC) application are obtained in LASAN by CSA. These sequences are implemented on the embedded microcontroller platform of STM32H743IITs for evaluation. Experimental results show that our proposed CSA can effectively reduce the end-to-end delay of LASAN and outperform the state-of-the-art static message scheduling method in terms of low delay.

With the development of the automotive industry toward intelligence and automation, there is a trend of controller area network (CAN) migrating to CAN with flexible data-rate (CAN-FD), where frame packing (i.e., packing signals of various periods, deadlines, and payloads into frames following the standard CAN-FD format) is critical to address the high bandwidth demand with limited resources. Existing works have applied integer linear programming (ILP), which easily gets intractable as the number of signals to be packed increases, or proposed heuristics, which are not able to obtain the optimal solution. In addition, the security model employed does not meet the AUTOSAR SecOC specification. This article reports a novel frame-packing approach for CAN-FD with an AUTOSAR-compliant security model. We establish the theory that extending the existing frame to pack signals with the same period leads to shorter worst-case transmission time (WCTT) and thus lower bus utilization compared to creating a new frame. Following this principle, the design space is tremendously pruned, where the optimal solution is guaranteed to remain. With pruning, we are able to increase the optimally solvable size of the problem from 150 signals to 300 signals, which is sufficient for practical usage. When there are 300 signals, only 10−142 of the original design space needs to be explored. To further improve efficiency, we apply pruning to heuristics. When the signal size is 500, for simulated annealing (SA), the computation time can be reduced by 54.1% and the bus utilization can be saved by 10.5% with pruning being deployed. In addition, we propose the max–min ant system as an alternative, which achieves better bus utilization than SA in shorter computation. Our reported method is generally applicable to other CAN-based distributed networks demanding higher bandwidth as well, such as in industry automation.

Controller Area Network with Flexible Data-rate (CAN-FD) is a promising industrial embedded network because of its high bandwidth and long data field length. However, CAN-FD does not deploy any security protection mechanisms, leaving it vulnerable to network attacks. In recent years, authentication and authorization frameworks have often been deployed in industrial embedded networks (e.g., automotive networks) to provide secure CAN/CAN-FD communication. However, these frameworks cannot simultaneously enhance confidentiality, integrity, and availability; moreover, these frameworks are mainly based on a distributed security management mechanism, resulting in large computation, communication, and memory overhead. This paper proposes a trinity-enabled and lightweight security framework called TrinitySec based on cryptographic algorithms for CAN-FD communication. TrinitySec ensures the availability of ECU and CAN-FD messages through authentication and authorization, as well as the confidentiality and integrity of CAN-FD messages through a symmetric-key algorithm and Hash-based Message Authentication Code (HMAC) function. TrinitySec proposes a low-overhead centralized security management mechanism instead of the existing distributed management mechanism. We formally verify the security of TrinitySec using the ProVerif tool. We implement TrinitySec on STM32H743IIT Micro Controller Unit (MCU) with ARM Cortex M7 core and evaluate that TrinitySec outperforms other state-of-the-art security frameworks in terms of computation, communication, memory, and storage overhead.

Deep learning has displayed superior performance in aerial scene (AS) categorization. However, existing methods for AS classification tend to lack adaptability and efficiency, particularly in optimizing its performance on different embedded devices. They also often fail to dynamically adjust to varying scales of feature representations, which can limit their effectiveness across different datasets and devices. To solve these issues, we provide two algorithms. The first algorithm explores local key features by mining the interactivity between channels. The range of cross-channel interactions is dynamically determined through an adaptive strategy. This ensures that convolution operations are optimized. The resulting features are improved by the second proposed algorithm. The second algorithm introduces convolutions, attention, and functions to calculate the weight of features. It enhances feature discriminative power by assigning adaptive weights. Then, we introduce an inference acceleration method for AS categorization. We create efficient codes for the proposed algorithms through automated optimization to match different devices. The inference time of the proposed method is reduced on various devices. Experiments on three frequently used datasets show we attain higher accuracy. The accuracy of some categories reaches 100%. In contrast to similar methods, our accelerated algorithm has at least 43.16%, 53.37%, 10.07%, 31.16%, and 8.08% less inference time on RTX 2,080Ti, Titan V, Jetson TX2, Jetson NX, and Jetson Nano GPUs, respectively.

Deep neural networks (DNNs) have demonstrated remarkable performance in many fields, and deploying them on resource-limited devices has drawn more and more attention in industry and academia. Typically, there are great challenges for intelligent networked vehicles and drones to deploy object detection tasks due to the limited memory and computing power of embedded devices. To meet these challenges, hardware-friendly model compression approaches are required to reduce model parameters and computation. Three-stage global channel pruning, which involves sparsity training, channel pruning, and fine-tuning, is very popular in the field of model compression for its hardware-friendly structural pruning and ease of implementation. However, existing methods suffer from problems such as uneven sparsity, damage to the network structure, and reduced pruning ratio due to channel protection. To solve these issues, the present article makes the following significant contributions. First, we present an element-level heatmap-guided sparsity training method to achieve even sparsity, resulting in higher pruning ratio and improved performance. Second, we propose a global channel pruning method that fuses both global and local channel importance metrics to identify unimportant channels for pruning. Third, we present a channel replacement policy (CRP) to protect layers, ensuring that the pruning ratio can be guaranteed even under high pruning rate conditions. Evaluations show that our proposed method significantly outperforms the state-of-the-art (SOTA) methods in terms of pruning efficiency, making it more suitable for deployment on resource-limited devices.

Time-Sensitive Networking (TSN) realizes high bandwidth and time determinism for data transmission and thus becomes the crucial communication technology in time-critical systems. The Gate Control List (GCL) is used to control the transmission of different classes of traffic in TSN, including Time-Triggered (TT) flows, Audio-Video-Bridging (AVB) flows, and Best-Effort (BE) flows. Most studies focus on optimizing GCL synthesis by reserving the preceding time slots to serve TT flows with the strict delay requirement, but ignore the deadlines of non-TT flows and cause the large delay. Therefore, this paper proposes a comprehensive scheduling method to enhance the real-time scheduling of AVB flows while guaranteeing the time determinism of TT flows. This method first optimizes GCL synthesis to reserve the preceding time slots for AVB flows, and then introduces the Earliest Deadline First (EDF) method to further improve the transmission of AVB flows by considering their deadlines. Moreover, the worst-case delay (WCD) analysis method is proposed to verify the effectiveness of the proposed method. Experimental results show that the proposed method improves the transmission of AVB flows compared to the state-of-the-art methods.

The network architecture that Time-Sensitive Networking (TSN) is used as the backbone network and the Controller Area Network (CAN) serves as the intra-domain network is considered as the CAN-TSN interconnection network architecture, which has gained considerable attention within industrial embedded networks, such as spacecraft, intelligent automobiles, and factory automation. The architecture employs the CAN-TSN gateway as a central hub for transmitting and managing a significant volume of communications between the CAN domains and TSN. However, the CAN-TSN gateway faces a high congestion challenge due to the rapid growth in data volume, making it difficult to effectively support different time planning mechanisms provided by TSN. In this article, we propose a two-stage mixed-criticality traffic scheduler. The scheduler in the first stage adopts a Message Optimization Algorithm (MOA) to aggregate multiple CAN messages into a single TSN message (including the aggregation of critical and non-critical CAN messages), which reduces the number of CAN messages requiring transmission. In the second stage, the scheduler proposes a Message Scheduling Optimization Algorithm (MSOA) to schedule critical TSN messages. This algorithm reassembles all the critical CAN messages (within the un-schedulable TSN messages) to generate new TSN messages for rescheduling. Experimental results show that our proposed scheduler effectively improves the acceptance ratio of critical and non-critical CAN messages and outperforms the state-of-the-art message scheduling method in terms of acceptance ratio while improving the bandwidth utilization and the number of schedule table entries. We further construct a hardware platform to evaluate the performance of MSOA. The consistency between practical results and theoretical results shows the effectiveness of MSOA.

Many embedded applications demand both resource efficiency and timing guarantee. However, resource sharing naturally complicates the analysis that extracts the worst-case scenario out of contention. Global Fixed-Priority (GFP) preemptive multiprocessor scheduling is one of the mainstream strategies to resolve contention on computational resources. It allows jobs of the same task to be executed on different processors, hence potentially enabling better parallelism and more efficient resource utilization. Unfortunately, its worst-case response time (WCRT) analysis is challenging. Existing approaches divide a high-priority task into three workloads, namely, carry-in workload, body workload, and carry-out workload, trying to optimize them individually. In this work, we propose a holistic WCRT analysis for GFP preemptive multiprocessor scheduling, where a task is no longer divided. Specifically, (i) we establish the tight interference scenario for the task being analyzed to find the most interfering high-priority jobs in any time interval; (ii) we obtain the starting released instant of each high-priority task’s first job to determine the maximum interference from high-priority tasks’ first jobs to the task being analyzed; (iii) we build the worst-case tight interference scenario for the task being analyzed by combining the tight interference scenario and the starting released instants; (iv) we prove that the WCRT of the task being analyzed can be decided by the worst-case tight interference scenario. Evaluation on schedulability shows that our proposed analysis achieves 4.2%-8.6% higher acceptance ratio in randomly generated data sets than the state-of-the-art workload division approaches.

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. The uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. The state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm.

With the advancement of intelligence and networked automotive, the domain-centralized architecture, which employs time sensitive networking (TSN) as the inter-domain backbone network and control area network with flexible data rate (CAN-FD) as the intra-domain network, has garnered significant attention. However, cross-domain end-to-end communication involves multiple components, and significant disparities between TSN and CAN-FD render response time analysis within domain-centralized architecture for mixed-critical traffic exceptionally complex. In this paper, we develop a cross-domain with TSN and CAN-FD end-to-end response time evaluation tool, which analyzes the response time of mixed-critical traffic under different design options segment by segment. We specifically analyze the waiting times of different messages in the domain control unit when faced with the design options of one-to-one and multi-to-one conversion of CAN-FD and TSN frames. The proposed evaluation tool can be easily extended to different design options to support more application scenarios. Theoretical computational analysis and real hardware measurements show the effectiveness of our tool.

Proteins commonly perform biological functions through protein-protein interactions (PPIs). The knowledge of PPI sites is imperative for the understanding of protein functions, disease mechanisms, and drug design. Traditional biological experimental methods for studying PPI sites still incur considerable drawbacks, including long experimental time and high labor costs. Therefore, many computational methods have been proposed for predicting PPI sites. However, achieving high prediction performance and overcoming severe data imbalance remain challenging issues. In this paper, we propose a new sequence-based deep learning model called CLPPIS (standing for CNN-LSTM ensemble based PPI Sites prediction). CLPPIS consists of CNN and LSTM components, which can capture spatial features and sequential features simultaneously. Further, it utilizes a novel feature group as input, which has 7 physicochemical, biophysical, and statistical properties. Besides, it adopts a batch-weighted loss function to reduce the interference of imbalance data. Our work suggests that the integration of protein spatial features and sequential features provides important information for PPI sites prediction. Evaluation on three public benchmark datasets shows that our CLPPIS model significantly outperforms existing state-of-the-art methods.

Intelligent connected vehicle is rapidly growing with the 5-G technology; the diversity of functional interfaces has significantly expanded the avenues of attack, making automotive controller area network (CAN) more vulnerable to cyberthreats. Automotive CAN network attacks are a direct threat to traffic safety, and in this study, we explore the use of intrusion detection techniques for mitigating cyberattacks. However, most automotive CAN network intrusion detection technologies are not capable of defending against sophisticated attacks, making it extremely challenging for detecting intrusions in practice. In this article, we propose a novel time interval conditional entropy method for detecting intrusions in automotive CAN networks. The time interval conditional entropy intrusion detection method is not susceptible to interference and is capable of detecting a variety of attacks. In our experiments, the conditional entropy values of regular communication messages are collected and utilized to distinguish and detect the attacks. The time interval conditional entropy detection method is implemented and evaluated in our controller area net-work bus (CAN-BUS) network platform. The experiments show that our method has higher detection accuracy and is easier to deploy compared to existing automotive CAN network intrusion detection methods.

With the deep integration of the Internet of Things (IoT) technology and the increase of computational power and memory, vehicles can also serve as the infrastructures for Intelligent Transportation System (ITS), e.g., as fog nodes. However, when connecting vehicles to the internet, alongside with the benefits it brings, it also opens many new challenges such as security attacks. Controller Area Network (CAN) is one of the main in-vehicle communication protocols in modern cars. Its lack of sender verification mechanism makes CAN particularly vulnerable to cyber-attacks including masquerade attack. Fingerprinting Electronic Control Units (ECUs) based on hardware characteristics has been proved feasible and effective on defending CAN buses. However, most state-of-the-art works exploited the supervised learning algorithm to identify the transmitter based on the signal characteristics. This makes the decision process hard to understand, and it also limits the deployment on proprietary CAN bus without prior knowledge. To solve this, we design a novel clock-skew-based approach capable of pinpointing the sender and detecting intrusion on proprietary CAN bus. We take a single CAN frame as the object for measurement, and adjust the measuring process such that our approach can be independent of the transmission time of frames. Based on the statistical analysis of data from real vehicles, we propose a novel box-plot algorithm based on score mechanism to filter the raw data. Finally, the clock skews are estimated and accumulated to build a linear model for representing the transmitter ECU. The evaluation results on one CAN prototype and two production vehicles show that our approach is able to well identify and differentiate ECUs on the bus without prior knowledge. The data processed by the proposed box-plot algorithm can describe the hardware characteristics of ECUs precisely. We also show the ability of our approach to protecting the CAN bus against the masquerade attack.

Recent research has shown that collaborative computing of CPUs and GPUs in the same system can effectively accelerate large-scale SGD-based matrix factorization (MF), but it faces the problem of limited scalability due to parameter synchronization in the server. Theoretically, asynchronous methods can overcome this shortcoming. However, through a series of tests, observations, and analyses, we realize that developing an effective asynchronous multi-CPU/GPU MF framework faces several major design challenges: the underutilized CPUs, high communication overhead, and the asynchronous data safety issue. This article presents a unified multi-CPU/GPU asynchronous computing framework for SGD-based matrix factorization, namedUMA-MF.UMA-MFtreats CPUs and GPUs in the system as distributed workers that train matrix datasets in parallel and update feature parameters asynchronously. It provides a cache-friendly CPU external working mode, which can improve the CPU's cache hit rate, thereby promoting the efficient use of CPUs. It offers an algorithm to find the shortest communication ring topology of heterogeneous CPU/GPU workers and builds computing-communication pipelines to minimize the communication overhead. It implements a wait-free structure and load-balanced data distribution to achieve asynchronous data safety.UMA-MFcan effectively accelerate SGD-based MF on multi-CPU/GPU systems in an asynchronous way. On a physical platform with configurations ranging from single processor system to 2CPUs--4CPUs system, for five common datasets Netfix, R1, R2, Goodreads, and de-dense,UMA-MFachieves up to 3.56x speedup compared with HCC-MF, which is the state-of-the-art multi-CPU/GPU synchronous computing framework for SGD-based MF.UMA-MFalso shows good scalability. When the system is scaled to 2CPUs-4GPUs, the training time speedup ofUMA-MFcan reach 70%--97% of the ideal speedup.

This article investigates the reliability of a special cyber-physical system (CPS) with an unreliable service and a complex boundary behavior. A flat semi-dormant multicontroller (FSDMC) model is constructed on a special CPS named arbitrated networked control system (ANCS) with dual channels. In this study, the dual-channel ANCS is considered as a Markov repairable system, which integrates the binary state of physical device failure and the multistate of information flow. The FSDMC is modeled as anN/(d,c)-M/M/c/K/SMWVqueuing system with an unreliable service. A dual rate matrix method is proposed to solve the stationary distribution of the queuing system and obtain the closed-form matrix solution of the distribution. Based on the queuing model, an optimization model is established to minimize the proposed cost performance rate function. A particle swarm optimization algorithm is used to solve the optimization model and obtain the optimal values of the system parameters under stable conditions. The closed-form expression of the instantaneous availability of the FSDMC on the physical failure rate and repair rate of the controller is yielded iteratively. The linear relationship between system instantaneous failure rate and task instantaneous failure rate is expressed. The sensitivity of task failure rate to system parameters is analyzed. Several reliability metrics are used to evaluate system reliability and task reliability. Experiments are conducted in real application scenarios to compare the task reliability using redundancy technology and real parallel applications. Experiments show that the proposed reliability model can more effectively guarantee system reliability goals compared with its counterparts.

Given monocular images as inputs, monocular depth estimation (MDE) infers pixel-level depth. MDE is always a critical stage in scene sensing on edge devices. Existing MDE studies frequently employ deep neural networks (DNNs) for MDE, but they still face some problems, such as sacrificing computational complexity and efficiency in return for great precision, or losing more precision in exchange for increased efficiency. To alleviate these issues; 1) we propose an encoder–decoder network (EdgeNet) for precise and fast MDE on different edge devices. When recovering depth in the decoder, we design upsampling modules to aggregate global depth information with low computational complexity, improving the accuracy of the decoder by extracting its different ranges of depth information; 2) we develop a two-stage channel pruning method to, respectively, prune the encoder and decoder based on their characteristics. Our pruning method further reduces latency and model/computational complexity of EdgeNet, while losing little accuracy; and 3) we optimize the pruned EdgeNet to decrease graphics processing unit (GPU) scheduling overhead. The optimization accelerates MDE inference by an order of magnitude on the TX2 GPU device, when the input resolution is 224$\times $224. Extensive experiments show that our strategies are effective on different edge GPU devices, when input resolutions differ in outdoor or indoor scenes. For example, compared with the state of the art, the optimized EdgeNet, respectively, reduces the GPU latency by 76.3% and 89.2% on Nano and TX2 GPU devices with 2.6% lower root mean square error when the input resolution is 128$\times $416.

Controller area network with flexible data-rate (CAN-FD) has received great attention in automotive cyber–physical systems (ACPSs) due to its high bandwidth and long payload. However, CAN-FD adopts a broadcast message transmission mechanism and lacks security protection, making it extremely vulnerable to cyberattacks. CAN-FD message packing (packing signals into messages) with low bandwidth occupancy (utilization) under security constraints is the prerequisite for running intelligent applications in ACPS. In this work, we implement a two-stage CAN-FD message packing solution to reduce bandwidth utilization and improve signal acceptance rate under security constraints. The first stage solves the message packing problem of minimizing bus bandwidth utilization under security constraints. The second stage aims at improving the signal acceptance rate by repacking signals. Experimental results show that the first stage reduces average bus bandwidth utilization by 135% compared with the unpacking solution, and the second stage improves the average signal acceptance rate by 5% than existing advanced methods.

With the development of multiprocessor technology, multiple processors are increasingly used in embedded real-time systems. The parallelism of multiple processors makes the worst-case response time (WCRT) analysis of multiprocessor real-time systems complicated. In the widely used global fixed-priority (GFP) scheduling, some effective approximate WCRT analysis methods have been proposed. These methods mainly focus on optimizing overall procedure or carry-in workload and assume that the whole carry-out workload of a high-priority task cause interference on the analyzed job. However, through our careful observation of specific examples, the partial carry-out workload of a high-priority task and the analyzed job may be executed in parallel. In other words, the part of parallel carry-out workload of a high-priority task could not cause interference on the analyzed job. In this article, we propose an improved WCRT analysis method by optimizing carry-out interference estimation for GFP scheduling, resulting in a more accurate upper bound of WCRT than the recent advanced methods. Experimental results show that our proposed method is superior than the recent advanced methods in terms of the acceptance rate of the system.

Reliability requirement assurance is an important quality of service (QoS) for workflow execution in cloud-based services. For a workflow with a reliability requirement, the enough replication for redundancy minimization (ERRM) and quantitative fault-tolerance with minimum execution cost + (QFEC+) algorithms are state-of-the-art algorithms to reduce the redundancy and cost, respectively. In this work, we define the reliability increment ratio (RIR) and propose the redundancy minimization using RIR (R_RIR) algorithm. In addition, we introduce the geometric mean and propose the cost reduction using geometric mean (C_GM) algorithm based on redundancy minimization. Experimental results show the proposed R_RIR and C_GM algorithms are superior to state-of-the-art algorithms: (1) although both R_RIR and ERRM show the same redundancy results, R_RIR is proven to generate minimal redundancy, whereas ERRM cannot; (2) R_RIR only consumes a few seconds to achieve minimal redundancy for large-scale workflows, and it has much higher time efficiency than ERRM; and (3) C_GM generates less cost than QFEC+ in a large part of cases.

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.

Cyber-physical systems (CPSs) realize the automatic control of entities through computing systems and networks. Stability is an important factor in CPS for system upgrading and troubleshooting. Traditional analysis methods focus on simulation and formal analysis, which have two major limitations: first, the current state information of CPS is difficult to obtain; second, most CPS face the state space explosion problem. These problems can be avoided and a good analysis can be provided based on empirical data. The main work of this article is summarized as follows: first, a phase space reconstruction method is designed to divide the dataset into several subsequences with the same shape; second, we propose a stability analysis method based on correlation dimensions. Results indicate that the proposed approach can obtain a stable correlation dimension. CPS perform better if the correlation dimension is maintained within a certain range; otherwise, a destabilizing factor exists. The proposed stability analysis has less complexity and running time.

Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energyparallelscheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely: 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.

CAN-FD (CAN with flexible data rate) has been developed to support automated driving as a high-bandwidth version of the conventional CAN (controller area network) bus protocol. Due to the complexity of the emerging automotive functionalities, there exist dependencies between the tasks and thus also between the CAN-FD messages. The current industrial practice is that the same application has exactly the same message transmission flow (i.e., the same ordered sequence of messages to be transmitted) across all vehicles. This renders large-scale attacks possible and potentially leads to millions of vehicles to be recalled, as one vehicle being compromised exposes all the others. To address this issue, an application could have different (obfuscated) message flows on individual vehicles. The challenge is to find a large number of available flows (i.e., flows that respect dependencies and meet application deadlines) within short time. For this purpose, we propose a novel priority assignment approach, which assigns the ordered positions in a flow (named priorities) to the messages. It dynamically generates new valid flows (i.e., flows with only dependencies respected and deadlines not considered) by message swapping, instead of exploring all valid flows as in the existing approaches. We apply pruning through affix-matching to further enhance the efficiency. That is, the prefix, infix, and suffix are all matched when determining whether a certain flow should be discarded without evaluating its availability, aiming for lower false positive rate (FSR) and false negative rate (FNR) than adfix-matching (only prefix and suffix are matched) in the state-of-the-art approach. Experimental results show that the proposed approach dominates the state-of-the-art approach, in the number of available flows found (up to 79x) and time consumption (up to 200x), most notably when the proportion of available flows is small. This work is an important step for obfuscated priority assignment to be deployed on practical CAN-FD messages.

With the trend towards automated driving, Controller Area Network (CAN) is migrating to CAN with Flexible Data-Rate (CAN-FD), where frame packing (i.e., packing signals of various periods, deadlines, and payloads into frames following the standard CAN- FD format) is critical to address the high bandwidth demand with limited resources. Existing works have applied Integer Linear Programming (ILP), which easily gets intractable as the number of signals to be packed increases, or proposed heuristics, which are not able to obtain the optimal solution. In addition, the security model employed does not meet the AUTOSAR SecOC specification. This paper reports a novel frame packing approach for CAN-FD with an AUTOSAR-compliant security model. We establish the theory that extending the existing frame to pack signals with the same period leads to shorter WCTT (worst-case transmission time) and thus lower bus utilization compared to creating a new frame. Following this principle, the design space is tremendously pruned. As shown in the comprehensive experiments, only 10−9 of the original size or even a smaller portion needs to be explored, while the optimality is kept. The computational time is correspondingly reduced, generating solutions within 15 minutes to large-scale problems that are otherwise intractable with ILP.

There is a demand of high bandwidth in the emerging real-time applications, such as autonomous vehicles, robotics, and industrial automation, where time-sensitive networking (TSN) is a promising solution. According to IEEE 802.1, a port in a TSN switch has eight prioritized FIFO (first-in first-out) queues, whose gates are opened or closed following a gate control list (GCL). Most of the existing works use one TT (time-triggered) queue for the hard real-time traffic, i.e., traffic flows with hard deadlines, which easily achieves timing determinism through GCL. Unfortunately, as a rigid mechanism, GCL is not able to handle timing jitter. In this work, we propose a hybrid strategy towards robust TSN. GCL is applied to only one queue named TT T1 for a small number of hard real-time flows with negligible jitter. The remaining flows with hard deadlines are allocated to a prioritized queue named TT T2 without GCL. Similarly, GCL is removed from all other queues handling AVB (audio-video-bridging) flows with soft deadlines and BE (best-effort) flows with no deadlines. Two analyses are proposed to obtain delay bounds for the TT T2 flows and periodic AVB flows, respectively, with interference from TT T1. Although safety is not compromised if the periodic AVB flows miss their deadlines, it is often desirable in practice to satisfy them for quality of service. In order to strike a balance, contention between the AVB queues is resolved with credit values on top of priorities. Experiments support that the delay bounds for the TT T2 and AVB flows are safe. In addition, changing the credit function can lead to different delay bounds of AVB flows, which is valuable for real-world configurations of TSN.

This paper presents a heterogeneous collaborative computing framework for SGD-based Matrix Factorization, named HCC-MF. HCC-MF can train the feature matrix efficiently using multiple CPUs and GPUs. It performs collaborative computing with data parallelism, where a server CPU is in charge of management and synchronization and other heterogeneous worker CPUs and worker GPUs performs calculation with their data assignments. HCC-MF adopts two data partition strategies, “data partition with heterogeneous load balance” and “data partition with hidden synchronization.” We build a time cost model to guide the data distribution among multiple workers and we design several communication optimization techniques with consideration of datasets’ and processors’ characteristics. Experimental results indicate that HCC-MF can utilize more than 88% of the platform’s computing power, yielding a speedup of 2.9 compared with advanced SGD-based MF, CuMF_SGD, on large-scale data sets.

The CAN (Controller Area Network) signals transmitted in vehicles have great analytical value with the quick development of complex automotive software. The boom in big data creates an opportunity to transmit CAN signals from the in-vehicle network to the big data cloud platform, through which the signal analysis can be conducted. The signals are transmitted from the in-vehicle network to TelematicsBOX via CAN bus and then sent to the big data cloud platform. When using the CAN bus for signal transmission of the in-vehicle network, signals larger than 1 byte need to be unpacked into several 1-byte signals. The general solution of automotive software manufacturers usually uses the model-based development method to unpack the CAN signals, but such method is inefficient. To solve this problem, we develop a CAN signal unpacking tool called AutoToolCSU, which is based on a configured template through a GUI (Graphical User Interface). Compared to the model-based development method, AutoToolCSU not only greatly improves the development efficiency of CAN signal unpacking but also interfaces with the standard development processes of automotive software manufacturers.

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches—that are independent of, or weakly dependent on DVFS—for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

Emerging cyber-physical systems (CPSs), such as in the domains of automotive, robotics, and industrial automation, often run complex functions with different criticality levels on a heterogeneous and distributed architecture. The ever stronger interactions between the cyber components and the physical environment lead to dynamic and irregular release of these functions. This article investigates dynamic scheduling of such mixed-criticality functions, where each function is modeled by a directed acyclic graph with no assumption on its period or minimum interarrival time. Unlike the existing methods that passively address the mixed criticality with a remedy when deadline misses are observed-this results in a high deadline miss ratio (DMR), and it is particularly undesirable for the high-criticality functions-we propose a novel dynamic scheduling approach using active strategies (ASDYS in short), where the mixed criticality is actively treated throughout the scheduling process. Automotive CPSs are used as an example for illustration. Experimental results show that our approach is significantly better than the existing methods in both the DMR of high-criticality functions and the overall system DMR.

As an essential part of Internet of Things, monocular depth estimation (MDE) predicts dense depth maps from a single red-green-blue (RGB) image captured by monocular cameras. Past MDE methods almost focus on improving accuracy at the cost of increased latency, power consumption, and computational complexity, failing to balance accuracy and efficiency. Additionally, when speeding up depth estimation algorithms, researchers commonly ignore their adaptation to different hardware architectures on edge devices. This article aims to solve these challenges. First, we design an efficient MDE model for precise depth sensing on edge devices. Second, We employ a reinforcement learning algorithm and automatically prune redundant channels of MDE by finding a relatively optimal pruning policy. The pruning approach lowers model runtime and power consumption with little loss of accuracy through achieving a target pruning ratio. Finally, we accelerate the pruned MDE while adapting it to different hardware architectures with a compilation optimization method. The compilation optimization further reduces model runtime by an order of magnitude on hardware architectures. Extensive experiments confirm that our methods are effective for images of different sizes on two public datasets. The pruned and optimized MDE achieves promising depth sensing with a better tradeoff among model runtime, accuracy, computational complexity, and power consumption than the state of the arts on different hardware architectures.

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%.

Controller Area Network with Flexible Data-rate (CAN FD) is beneficial for the in-vehicle communication of Internet of Connected Vehicles (IoCVs) because of its high bandwidth and data field length. However, CAN FD lacks a security authentication mechanism, making it extremely vulnerable to masquerade attacks. This study proposes the security enhancement for a real-time parallel in-vehicle application adopting a two-stage method. The first stage obtains the lower bound of an in-vehicle application by quickly abandoning most of sequences, while the second stage enhances security by adding Message Authentication Codes (MACs) to messages taking advantage of the laxity interval from the lower bound to the deadline. Experiments with an example and the adaptive cruise control in-vehicle application show the advantage of the proposed two-stage method in increasing the total byte size of MACs.

With the rapid development of Internet of vehicles, connected vehicles, autonomous vehicles, and autonomous driving technologies, automotive Controller Area Networks (CAN) have suffered from numerous security threats. Deep learning models are the current mainstream intrusion detection techniques for threat analysis, and the state-of-the-art intrusion detection technique introduces the Generative Adversarial Networks (GAN) model to generate usable attacked samples to supplement the training samples, but it exists the limitations of rough CAN message block construction and fails to detect the data tampering threat. Based on the CAN communication matrix defined by the automotive Original Equipment Manufacturer (OEM) for a vehicle model, we propose an enhanced deep learning GAN model with elaborate CAN message blocks and the enhanced GAN discriminator. The elaborate CAN message blocks in the training samples can precisely reflect the real generated CAN message blocks in the detection phase. The GAN discriminator can detect whether each message has suffered from the data tampering threat. Experimental results illustrate that the enhanced deep learning GAN model has higher detection accuracy, recall, and F1 scores than the state-of-the-art deep learning GAN model under various attacks and threats.

With Internet of things technologies, billions of embedded devices, including smart gateways, smart phones, and mobile robots, are connected and deeply integrated. Almost all these embedded devices are battery-constrained and energy-limited systems. In recent years, several works used energy pre-assignment techniques to study the dynamic energy-constrained scheduling of a parallel application in heterogeneous multicore embedded systems. However, the existing energy pre-assignment techniques cannot satisfy the actual energy constraint, because it is the joint constraint on dynamic energy and static energy. Further, the modeling and verification of these works are based on the simulations, which have not been verified in real embedded devices. This study aims to propose a dynamic and static energy-constrained scheduling framework in heterogeneous multicore embedded devices. Solving this problem can utilize existing energy pre-assignment techniques, but it requires a deeply integrated design flow and methodology. The design flow consists of four processes: (1) power and energy modeling; (2) power parameter measurement; (3) basic framework design including energy pre-assignment; and (4) framework optimization. Each design flow has corresponding design methodology. Both our theoretical analysis and practical verification using the low-power ODROID-XU4 device confirm the effectiveness of the proposed framework.

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.

Optimisation of timing performance and power consumption on heterogeneous multi-core architectures is gaining increasing attention. Systems and devices may have varying demands on timing and power, which motivates more flexible optimisation. Along this line, we consider a heterogeneous computing architecture with multiple cores, where each core runs a mixed stream of general and dedicated tasks with a certain scheduling strategy. Employing the queuing model, we first propose a load balancing algorithm, which minimises the average response time of the general tasks whilst guaranteeing the timing requirements of the dedicated tasks. Built upon the above, we propose a bi-directional optimisation algorithm that is able to improve the timing performance under the constraint of power consumption, and reduces the power consumption for the given timing requirement. Extensive numerical experiments illustrate the significance of the proposed algorithms. Implementation on a real platform validates the consistency between the theoretical analysis and the practical results.

Multiprocessor systems are increasingly deployed in real-time applications, where reliability, energy consumption, and makespan are often the main scheduling objectives. In this work, we investigate the dynamic scheduling of tasks modeled by directed acyclic graphs (DAGs), which is an NP-hard problem with all existing methods being heuristics. Our contributions have two steps: 1) assuming that the allocation of DAG nodes to processors is given, we propose optimal energy allocation (OEA) and search-based OEA (SOEA)-the first optimal methods that minimize the energy consumption while satisfying the reliability requirement-for homogeneous and heterogeneous systems, respectively and 2) we present a novel scheduling algorithm out-degree scheduling (ODS) that allocates the DAG nodes according to their out-degrees, and considering energy consumption, reliability, as well as dynamic finish time. ODS dominates the widely applied heterogeneous earliest finish time (HEFT) in makespan. Combining SOEA with ODS makes a complete solution to the problem of dynamic DAG scheduling on multiprocessor systems, and achieves generally better results compared to the existing approaches. Specifically, in most cases, we are better on all the three objectives, i.e., reliability, energy, as well as makespan, and in other cases, we are better on some of the objectives.

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.

Reliability requirement is one of the most important quality of services (QoS) and should be satisfied for a reliable workflow in cloud computing. Primary-backup replication is an important software fault-tolerant technique used to satisfy reliability requirement. Recent works studied quantitative fault-tolerant scheduling to reduce execution cost by minimizing the number of replicas while satisfying the reliability requirement of a workflow on heterogeneous infrastructure as a service (IaaS) clouds. However, a minimum number of replicas does not necessarily lead to the minimum execution cost and shortest schedule length in a heterogeneous IaaS cloud. In this study, we propose the quantitative fault-tolerant scheduling algorithms QFEC and QFEC+ with minimum execution costs and QFSL and QFSL+ with shortest schedule lengths while satisfing the reliability requirements of workflows. Extensive experimental results show that (1) compared with the state-of-the-art algorithms, the proposed algorithms achieve less execution cost and shorter schedule length, although the number of replicas are not minimum; (2) QFEC and QFEC+ are designed to reduce execution cost, and QFEC+ is better than QFEC for all low-parallelism and high-parallelism workflows; and (3) QFSL and QFSL+ are designed to decrease schedule length, and QFSL+ is better than QFSL for all low-parallelism and high-parallelism workflows.

With the rapid growth of connectivity and autonomy for today’s automobiles, their security vulnerabilities are becoming one of the most urgent concerns in the automotive industry. The lack of message authentication in Controller Area Network (CAN), which is the most popular in-vehicle communication protocol, makes it susceptible to cyber attack. It has been demonstrated that the remote attackers can take over the maneuver of vehicles after getting access to CAN, which poses serious safety threats to the public. To mitigate this issue, we propose a novel intrusion detection system (IDS), called BTMonitor (Bit-time-based CAN Bus Monitor). It utilizes the small but measurable discrepancy of bit time in CAN frames to fingerprint their sender Electronic Control Units (ECUs). To reduce the requirement for high sampling rate, we calculate the bit time of recessive bits and dominant bits, respectively, and extract their statistical features as fingerprint. The generated fingerprint is then used to detect intrusion and pinpoint the attacker. BTMonitor can detect new types of masquerade attack that the state-of-the-art clock-skew-based IDS is unable to identify. We implement a prototype system for BTMonitor using Xilinx Spartan 6 FPGA for data collection. We evaluate our method on both a CAN bus prototype and a real vehicle. The results show that BTMonitor can correctly identify the sender with an average probability of 99.76% on the real vehicle.

The data generated by millions of sensors in the industrial Internet of Things (IIoT) are extremely dynamic, heterogeneous, and large scale and pose great challenges on the real-time analysis and decision making for anomaly detection in the IIoT. In this article, we propose a long short-term memory (LSTM)-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in the IIoT. In a nutshell, the LSTM-NN builds a model on normal time series. It detects outliers by utilizing the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of the Gaussian Naive Bayes model through the predictive error. We evaluate our approaches on three real-life datasets that involve both long-term and short-term time dependence. Empirical studies demonstrate that our proposed techniques outperform the best-known competitors, which is a preferable choice for detecting anomalies.

Guaranteeing safety is always a prerequisite in the process of realizing various automotive applications. However, the automotive functional safety design has been challenged by multiple factors, such as the complexity of the new generation automotive electrical and electronic (E/E) architecture, the continuous release and update of automotive functional safety standard International Standardization Organization (ISO) 26262, the release of new AUTOSAR adaptive platform standard, and the increase in different types of costs. In this article, we summarize the recent advances of automotive functional safety design methodologies through analysis, design, optimization, and runtime phases, respectively: 1) functional safety analysis; 2) functional safety guarantee; 3) safety-aware cost optimization; and 4) safety-critical multifunctional scheduling. Then, we provide the future trends in functional safety design methodologies that will be directly oriented to autonomous vehicles and adapt to the next generation functional safety standard ISO 21448.

The development of the complexity and connectivity of modern automobiles has caused a massive rise in the security risks of in-vehicle networks (IVNs). Nevertheless, existing IVN designs (e.g., controller area network) lack cybersecurity consideration. Intrusion detection, an effective method for defending against cyberattacks on IVNs while providing functional safety and real-time communication guarantees, aims to address this issue. Therefore, the necessity of its research has risen. In this paper, an IVN environment is introduced, and the constraints and characteristics of an intrusion detection system (IDS) design for IVNs are presented. A survey of the proposed IDS designs for the IVNs is conducted, and the corresponding drawbacks are highlighted. Various optimization objectives are considered and comprehensively compared. Lastly, the trend, open issues, and emerging research directions are described.

The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g., WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g., 67.21 percent throughput improvement, 72.99 percent reduced delay, and 69.59 percent jitter improvements, compared with alternative SDN systems.

In distributed automotive embedded systems, safety issues run through the entire life cycle, and safety mechanisms for error handling are desirable for risk control. This article focuses on safety enhancement (i.e., safety mechanisms for error handling) for a safety-critical automotive application within its deadline. A stable stopping approach used for safety enhancement for an automotive application is proposed based on the static recovery mechanism provided in ISO 26262. The Stable Stopping-based Safety Enhancement (SSSE) approach is proposed by combining known backward recovery, proposed forward recovery, and proposed forward-and-backward recovery through primary-backup repetition. The stable stopping (i.e., SSSE) approach is a convergence algorithm, which means that when the reliability value reaches a steady state and the algorithm can stop. Experimental results reveal that the exposure level defined in ISO 26262 drops from E3 to E1 after using SSSE, and such improvement enables a safety guarantee of higher level.

Reliability is widely identified as an increasingly relevant issue in heterogeneous service-oriented systems because processor failure affects the quality of service to users. Replication-based fault-tolerance is a common approach to satisfy application's reliability requirement. This study solves the problem of minimizing redundancy to satisfy reliability requirement for a directed acyclic graph (DAG)-based parallel application on heterogeneous service-oriented systems. We first propose the enough replication for redundancy minimization (ERRM) algorithm to satisfy application's reliability requirement, and then propose heuristic replication for redundancy minimization (HRRM) to satisfy application's reliability requirement with low time complexity. Experimental results on real and randomly generated parallel applications at different scales, parallelism, and heterogeneity verify that ERRM can generate least redundancy followed by HRRM, and the state-of-the-art MaxRe and RR algorithm. In addition, HRRM implements approximate minimum redundancy with a short computation time.

This paper investigates resource provisioning in cyber-physical systems (CPSs) by developing a new definition of anelasticity. A flat semi-dormant multicontroller (FSDMC) model is established on a special type of CPS platform named arbitrated networked control system with dual communication channels. A novel, quantitative, and formal definition of anelasticity for the FSDMC is proposed. A new finite capacity M/M/c queuing system with N-policy and asynchronous multiple working vacations of partial servers is established, and the FSDMC is modeled as a quasi-birth-and-death process to obtain the stationary probability distribution of the system. Based on the queueing model, we quantify various performance indices of the system to build a nonlinear cost-performance ratio (CPR) function. An optimization model is presented to minimize the CPR. A particle swarm optimization (PSO) algorithm is used to find the optimum solution of the optimization model and obtain the optimal configuration values of the system parameters under stability condition. By changing the system parameters, the sensitivity of the system performance indices and the CPR are analyzed, respectively. The unexpected workload varies randomly over time. Thus, an M/M/1/K queue is constructed in a Markovian environment by employing a three-state, irreducible Markov process. In this queue, the conditional average queue length and the probabilities of the three-state process are calculated. Then, the anelasticity value of the system is precisely determined. When the average arrival rate exceeds the average service rate in the queueing system, an optimal CPR unchanged adaptive algorithm based on PSO is designed to dynamically adjust the controller service rate. Extensive numerical results show the usefulness and effectiveness of the proposed techniques and exhibit that the system can maintain elastic invariance in adaptive adjustment parameters.

Graph clustering aims to identify clusters that feature tighter connections between internal nodes than external nodes. We noted that conventional clustering approaches based on a single vertex or edge cannot meet the requirements of clustering in a higher-order mixed structure formed by multiple nodes in a complex network. Considering the above limitation, we are aware of the fact that a clustering coefficient can measure the degree to which nodes in a graph tend to cluster, even if only a small area of the graph is given. In this paper, we introduce a new cluster quality score, i.e., the local motif rate, which can effectively respond to the density of clusters in a higher-order graph. We also propose a motif-based local expansion and optimization algorithm (MLEO) to improve local higher-order graph clustering. This algorithm is a purely local algorithm and can be applied directly to higher-order graphs without conversion to a weighted graph, thus avoiding distortion of the transform. In addition, we propose a new seed-processing strategy in a higher-order graph. The experimental results show that our proposed strategy can achieve better performance than the existing approaches when using a quadrangle as the motif in the LFR network and the value of the mixing parameter $\mu $ exceeds 0.6.

We study the worst case response time (WCRT) analysis and evaluation for sporadic message-processing tasks in a multicore automotive gateway of a controller area network (CAN) cluster. We first build a multicore automotive gateway on CAN clusters. Two WCRT analysis methods for message-processing tasks in the multicore gateway are subsequently presented based on global and partitioned scheduling paradigms. We evaluate the WCRT results of two analysis methods with real message sets provided by the automaker, and present the design optimization guide.

The functional safety research for automotive cyber-physical systems (ACPS) has been studied in recent years; however, these studies merely consider the change in the exposure of the functional safety classification and assume that the driver’s controllability in the functional safety classification is always fixed and uncontrollable. In fact, the driver’s controllability is variable during the runtime phase, such that the execution process of safety-critical automotive functions is a human-interaction-aware process between the driver and ACPS. To adapt to the changes in the driver’s controllability, this article studies the human-interaction-aware adaptive functional safety processing for multi-functional ACPS in two main phases. In the design phase, where the driver’s controllability is fixed at the highest level (i.e., C3), we obtain the approximate optimal priority sequence of safety-critical functions without exhausting all sequences by proposing the refined exploration method. In the runtime phase, where the driver’s controllability level is variable (i.e., C0, C1, C2, or C3), we propose the human-interaction-aware task remapping method to autonomously respond to the change of the driver’s controllability. Examples and experiments confirm that the proposed adaptive functional safety processing can reduce overall task redundancy of safety-critical automotive functions while meeting their functional safety requirements, shorten the overall response time of safety-critical automotive functions, and increase the slack time for non-safety-critical automotive functions.

Automotive functional safety standard ISO 26262 aims to avoid unreasonable risks due to systematic failures and random hardware failures caused by malfunctioning behavior. Automotive functions involve distributed end-to-end computation in automotive cyber-physical systems (ACPSs). The automotive industry is highly cost-sensitive to the mass market. This study presents a resource-cost-aware fault-tolerant design methodology for end-to-end functional safety computation on ACPSs. The proposed design methodology involves early functional safety requirement verification and late resource cost design optimization. We first propose the functional safety requirement verification (FSRV) method to verify the functional safety requirement consisting of reliability and response time requirements of the distributed automotive function during the early design phase. We then propose the resource-cost-aware fault-tolerant optimization (RCFO) method to reduce the resource cost while satisfying the functional safety requirement of the function during the late design phase. Finally, we perform experiments with real-life automotive and synthetic automotive functions. Findings reveal that the proposed RCFO and VFSR methods demonstrate satisfactory resource cost reduction compared with other methods while satisfying the functional safety requirement.

A typical automotive integrated architecture is a controller area network (CAN) cluster integrated by a central gateway. This study proposes a novel and exact worst-case response time (WCRT) analysis method for message-processing tasks in the gateway. We first propose a round search method to obtain lower bound on response time (LBRT) and upper bound on response time (UBRT), respectively. We then obtain the exact WCRT belonging to the scope of the LBRT and UBRT with an effective non-exhaustive exploration. Experimental results on a real CAN message set reveal that the proposed exact analysis method can reduce 99.99999% combinations on large-scale CAN clusters.

The automotive electronic system is a typical heterogeneous distributed embedded system. For such a resource-constrained, cost-sensitive system, how to optimize the hardware cost and energy is a hot topic in current research. Meanwhile, industrial safety requirement must be satisfied according to safety standards. To address this complex problem, This study proposes an optimization algorithm, namely hardware cost and energy consumption optimization algorithm (HCECO), which is based on a genetic algorithm combined with simulated annealing and a state-of-the-art scheduling strategy. It aims to reduce the hardware cost and energy consumption of the embedded product while satisfying the hard real-time and reliability requirements of safety-critical applications during the early design phase. The experiment is completed under three real applications. The results demonstrate that the HCECO algorithm can effectively reduce the hardware cost and energy consumption under the hard real-time and reliability constraints.

With the development of Internet of Things, the Healthcare Industrial IoT has become an effective way to curb the high mortality rate of heart disease. The accuracy of such system is mainly rely on the quality of ECG signals, in which noise reduction has been widely used. However, in the IoT environment, many kinds of noise which cannot be predicted in advance exist in signals, and make the signal morphology seriously damaged, which brings great challenge to the existing de-noise methods. By considering the self-adaptation and self-learning of deep neural network, we have proposed a multilayer LSTM model to the noise reduction of dynamic ECGs. Unlike other methods, our model makes both noise and ECG signals as part of time-series data, while other methods always consider them separately. Benefit from the recurrent structure of LSTM model, the most representative features will be extracted in LSTM memory units. By stacking multiple layers per time step, the useful information will be continuously refined and the noise signal will be discarded. Even if the ECG signals comprise many kinds of noise simultaneously, the model can still restore ECG signals with high quality without relying on threshold or signal quality. The experimental results show that the proposed model is insensitive to noise and the improvement of signal-to-noise ratio up to 55dB. This result is much better than the existing methods, which indicates LSTM is a new competitive method for ECG noise reduction.

An on-chip optical transceiver for transmission system over 100GBd is proposed based on optical time division multiplexing (OTDM) technology, and the performances, such as the insertion loss, the inter-symbol interference (ISI) crosstalk, and the potential symbol rate, are analyzed in detail. Co-designed with the double rail driver, on-chip Mach-Zehnder interferometer switch repeatedly generates extremely narrow sampling pulses of only 12ps full width at half maximum. Based on such narrow optical sampling pulse train, a four-stage cascaded optical switch divides the 25GHz clock cycle into four recurrent 9.5ps time slots and one blank time slot of 2ps. Thus, a 100GBd optical transmission channel is realized based on 4-bit 25Gbps bit-streams at the electrical interface. The ISI extinction ratio at the worst channel is 1.9dB with 10dB depth modulator, and the insertion loss caused by the OTDM mechanism is about 16dB. Further, taking advantages of dark modulation, an OTDM system with 5-bit 25Gbps bit-streams at the electrical interface is proposed to generate a 125GBd transmission utilizing the same optical sampling pulse. The ISI performance is much better and the extinction ratio at the worst channel is enhanced to 3.99dB.

Summary Modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture and framework design. Most current popular frameworks are not designed for multidatabases, and many practical problems in development arise. This study designs and implements a software development framework called Java data access service (JDAS) for multidatabases using the object‐oriented programming language Java. The JDAS framework solves related problems that arise when other frameworks are employed in practical software development with multidatabases by presenting and introducing design methods. JDAS consists of the modules of the database abstract, object relational mapping, connection pools management, configuration management, data access service, and inversion of control. Results and case study reveal that the JDAS framework effectively reduces development complexity and improves development efficiency of the software systems with multidatabases. Copyright © 2017 John Wiley & Sons, Ltd.

Industrial embedded systems are cost sensitive, and hardware cost of industrial production should be reduced for high profit. The functional safety requirement must be satisfied according to industrial functional safety standards. This study proposes three hardware cost optimization algorithms for functional safety-critical parallel applications on heterogeneous distributed embedded systems during the design phase. The explorative hardware cost optimization (EHCO), enhanced EHCO (EEHCO), and simplified EEHCO (SEEHCO) algorithms are proposed step by step. Experimental results reveal that EEHCO can obtain minimum hardware cost, whereas SEEHCO is efficient for large-scale parallel applications compared with the existing algorithms.

Automotive cyber-physical systems are energy-aware and safety-critical systems where energy consumption should be controlled from a perspective of design constraints and reliability should be enhanced toward functional safety goal assurance. In this paper, we solve the problem of reliability enhancement of an automotive function (i.e., functionality or application) under energy and response-time constraints based on the dynamic voltage and frequency scaling technique. The problem is solved by a two-stage solution, namely, response-time reduction under energy constraint and reliability enhancement under energy and response-time constraints. The first stage is solved by proposing average energy preallocation, and the second stage is solved by proposing a reliability-enhancement technique based on the first stage. Examples and experiments show that the proposed solution can not only assure energy and response-time constraints, but also enhances reliability as much as 16.66% compared with its counterpart.

Automotive functional safety requirement includes response time and reliability requirements learning from the functional safety standard ISO 26262. These two requirements must be simultaneously satisfied to assure automotive functional safety requirement. However, increasing reliability increases the response time intuitively. This study proposes a method to find the solution with the minimum response time while assuring reliability requirement. Pre-assigning reliability values to unassigned tasks by transferring the reliability requirement of the function to each task is a useful reliability requirement assurance approach proposed in recent years. However, the pre-assigned reliability values in state-of-the-art studies have unbalanced distribution of the reliability of all tasks, thereby resulting in a limited reduction in response time. This study presents the geometric mean-based non-fault-tolerant reliability pre-assignment (GMNRP) and geometric mean-based fault-tolerant reliability pre-assignment (GMFRP) approaches, in which geometric mean-based reliability values are pre-assigned to unassigned tasks. Geometric mean can make the pre-assigned reliability values of unassigned tasks to the central tendency, such that it can distribute the reliability requirements in a more balanced way. Experimental results show that GMNRP and GMFRP can effectively reduce the response time compared with their individual state-of-the-art counterparts.

ISO 26262 is a functional safety standard specifically made for automotive systems, in which the automotive safety integrity level (ASIL) is the representation of the criticality level. Recently, most studies have used ASIL decomposition to reduce the development cost of automotive functions. However, these studies have not paid special attention to the problem that the reliability goal may not be satisfied when ASIL decomposition is performed. In this study, we solve the problem of minimizing the development cost of a distributed automotive function while satisfying its reliability goal during the design phase by presenting two heuristic algorithms, reliabilitycalculation of scheme (RCS) and minimizing development cost with reliability goal (MDCRG). We first use RCS to calculate the reliability value of each ASIL decomposition scheme; then, the MDCRG is used to select the scheme with the minimum development cost while satisfying the reliability goal. Real-life benchmark and simulated functions based on real parameter values are used in experiments, and results show the effectiveness of the proposed algorithms.

Dynamic voltage and frequency scaling (DVFS) is a well-known energy consumption optimization technique in embedded systems and dynamically scaling down the voltage of a chip has been developed to achieve energy-efficient optimization. However, this operation may lead to a sharp rise in transient failures of processors and consequently weaken the reliability of systems. Reliability goal is an important functional safety requirement and must be satisfied for safety-critical applications. In this study, we aim to implement energy-efficient fault-tolerant scheduling for a reliable parallel application on heterogeneous distributed embedded systems, where the parallel application is described by a directed acyclic graph (DAG). An energy-efficient scheduling with a reliability goal (ESRG) algorithm is presented to reduce the energy consumption while satisfying the reliability goal for the parallel application. Considering that the application's reliability goal is unreachable if its reliability goal exceeds a certain threshold via ESRG, we further propose an energy-efficient fault-tolerant scheduling with a reliability goal (EFSRG) algorithm to reduce the energy consumption while satisfying the reliability goal based on an active replication scheme. Experimental results confirm that the energy consumption reduced by the proposed EFSRG algorithm is higher than those reduced by other approaches under different scale conditions.

Summary Parallel file systems commonly distribute a file across multiple file servers with a fixed‐size stripe, thereby allowing data access through multiple file servers. This default data layout works well in traditional homogeneous storage systems, but when solid state disks (SSDs) are conducted into a storage system, the data layout of hybrid parallel file systems has a chance to obtain better I/O performance. In this study, we propose a variable‐sized stripe level data layout strategy for hybrid parallel file systems (SLDP). SLDP divides the file into several regions according to the data access pattern and then finds the optimal configurations for each region among the solid state disk file server nodes and mechanical hard disk drive file server nodes. It uses variable stripe sizes to reorganize the data layout of file systems. Furthermore, it considers SSD space limitation, the main idea is to distribute key regions of the file to hybrid parallel file systems based on the optimal stripe configuration, which can significantly improve the system I/O throughput performance. The remaining parts of a file are then distributed according to the SSD free space threshold, which can leverage the SSD servers as much as possible. To achieve this, SLDP divides a large file into many fine‐grained regions and adjusts the data layout method for each region according to the access patter. Experimental results show that the SLDP is feasible and can improve system performance. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Scheduling multiple parallel workflows, which arrive at different instants on heterogeneous distributed computing systems, is a great challenge because of the different requirements of resource providers and users. Overall scheduling length is the main concern of resource providers, whereas deadlines of workflows are the major requirements of users. Most algorithms use fairness‐based strategies to reduce the overall scheduling length. However, these algorithms cause obvious unfairness to longer‐makespan workflows or shorter‐makespan workflows. Furthermore, the systems cannot meet the deadlines of all workflows, particularly on large‐scale resource‐constrained computational grids. Gaining a reasonable balance between the overall scheduling length and the deadlines of workflows is a desirable goal. In this study, we first propose a fairness‐based scheduling algorithm called fairness‐based dynamic multiple heterogeneous selection value to achieve high performance of systems compared with existing works. Then, to meet the deadlines of partial higher‐priority workflows, we present a priority‐based scheduling algorithm called priority‐based dynamic multiple heterogeneous selection value. Finally, combining fairness‐based dynamic multiple heterogeneous selection value and priority‐based dynamic multiple heterogeneous selection value, we present the tradeoff‐based scheduling algorithm to meet the deadlines of more higher‐priority workflows while still allowing the lower‐priority workflows to be processed actively for better performance of systems. Both example and extensive experimental evaluations demonstrate significant improvement of our proposed algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Energy consumption is one of the primary design constraints in heterogeneous parallel and distributed systems ranging from small embedded devices to large‐scale data centers. The problem of minimizing the schedule length of an energy consumption‐constrained parallel application has been studied recently in homogeneous systems with a shared memory. To adopt the heterogeneity and distribution of high‐performance computing systems, this study solves the problem of minimizing the schedule length of an energy consumption‐constrained parallel application in heterogeneous distributed systems based on a dynamic voltage and frequency scaling energy‐efficient design technique. The aforementioned problem is divided into 2 subproblems in this study, namely, satisfying energy consumption constraint and minimizing schedule length. The first subproblem is solved by transferring the energy consumption constraint of the application to that of each task, whereas the second subproblem is solved by heuristically scheduling each task with low time complexity. Experiments using both fast Fourier transform and Gaussian elimination parallel applications show that the actual energy consumption values do not always exceed but are close to the given energy consumption constraints. In addition, the minimum schedule lengths are generated using the proposed algorithm.

In this paper, the joint optimization problem with energy efficiency and effective resource utilization is investigated for heterogeneous and distributed multi-core embedded systems. The system model is considered to be fully a heterogeneous model, that is, all nodes have different maximum speeds and power consumption levels from the perspective of hardware while they can employ different scheduling strategies from the perspective of applications. Since the concerned problem by nature is a multi-constrained and multi-variable optimization problem in which a closed-form solution cannot be obtained, our aim is to propose a power allocation and load balancing strategy based on Lagrange theory. Furthermore, when the problem cannot be fully solved by Lagrange approach, a data fitting method is employed to obtain core speed first, and then load balancing schedule is solved by Lagrange method. Several numerical examples are given to show the effectiveness of the proposed method and to demonstrate the impact of each factor to the present optimization system. Finally, simulation and practical evaluations show that the theoretical results are consistent with the practical results. To the best of our knowledge, this is the first work that combines load balancing, energy efficiency, hardware heterogeneity and application heterogeneity in heterogeneous and distributed embedded systems.

Recently, the modeling and design of distributed controllers in cyber-physical systems (CPSs), which suffer from messages lost, delay variation, and jitter, has gained lots of research attentions. A special CPS, arbitrated networked control system (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, we propose a novel ANCS with dual communication channels. The proposed ANCS uses a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy that is based on the event trigger protocol. A flat semi-dormant multicontrollers (FSDMC) model is developed for the proposed ANCS. We then model the FSDMC as an N/(d,c)-M/M/c/K/SMWV queue, and obtain various performance indices. Based on the model, a multiobjective optimization problem is then formulated to minimize the nonlinear energy consumption function and the nominal delay function presented in this study. To resolve the multiobjective optimization problem, a scheduling algorithm based on the multiobjective particle swarm optimization algorithm is proposed to generate the Pareto front and the corresponding nondominated vector sets. An optimal stopping algorithm is also designed to obtain the optimal value of the number of semi-dormant controllers. The optimal values of various parameters of the control system are obtained by using the above nondominated vector sets, and are applied to the proposed ANCS. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and the effects of the control system parameters on the optimal policy.

Heterogeneous processors are increasingly being used in embedded systems where parallel applications with precedence-constrained tasks widely exist. Reliability is an important functional safety requirement and reliability goal should be satisfied for safety-critical parallel applications; meanwhile, resource is limited in embedded systems and it should be minimized. This study solves the problem of resource consumption cost minimization of a reliable parallel application on heterogeneous embedded systems without using fault tolerance. The problem is decomposed into two subproblems, namely, satisfying reliability goal and minimizing resource consumption cost. The first subproblem is solved by transferring the reliability goal of the application to that of each task, and the second subproblem is solved by heuristically assigning each task to the processor with the minimum resource consumption cost while satisfying its reliability goal. Experiments with real parallel applications verify that the proposed algorithm obtains minimum resource consumption costs compared with the state-of-the-art algorithms.

The problem of minimizing the energy consumption of a real-time parallel application on a heterogeneous system has been studied recently, and slack time reclamation based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design technique has been proposed as a solution. However, the state-of-the-art algorithms merely minimize energy consumption through an “upward” approach (i.e., from exit to entry tasks) and do not apply the “downward” approach (i.e., from entry to exit tasks) to energy consumption minimization. This study solves the same problem by employing “downward” and “upward” approaches. The concepts of deadline-slack and task level are introduced to transfer the deadline of the parallel application to each task, that is, “downward” energy consumption minimization is implemented. “Upward” energy consumption minimization by reclaiming the slack time is then included to implement “downward” and “upward” energy consumption minimization with low time complexity. Results of the experiments using real parallel applications show that the proposed algorithm can generate the minimum energy consumption compared with the state-of-the-art algorithms under different real-time and scale conditions.

Energy consumption minimization is one of the primary design requirements for heterogeneous distributed systems. State-of-the-art algorithms are used to study the problem of minimizing the energy consumption of a real-time parallel application with precedence constrained tasks on a heterogeneous distributed system by introducing the concept of latest finish time (LFT) to reclaim the slack time based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design optimization technique. However, the use of DVFS technique alone is insufficient, and the energy consumption reduction is limited because scaling down the frequency is restricted in practice. Furthermore, these studies merely minimize energy consumption through a local energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on the fixed processor, rather than a global energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on different processors. This study solves the problem of minimizing the energy consumption of a real-time parallel application on heterogeneous distributed systems by using the combined non-DVFS and global DVFS-enabled energy-efficient scheduling algorithms. The non-DVFS energy-efficient scheduling (NDES) algorithm is solved by introducing the concept of deadline slacks to reduce the energy consumption while satisfying the deadline constraint. The global DVFS-enabled energy-efficient scheduling (GDES) algorithm is presented by moving the tasks to the processor slacks that generate minimum dynamic energy consumptions. Results of the experiments show that the combined NDES&GDES algorithm can save up to 36.25-55.65 percent of energy compared with state-of-the-art counterparts under different scales, parallelism, and heterogeneity degrees of parallel applications.

Energy efficiency has become a key issue for cloud computing platforms and data centers. Minimizing the total energy consumption of an application is one of the most important concerns of cloud providers, and satisfying the deadline constraint of an application is one of the most important quality of service requirements. Previous methods tried to turn off as many processors as possible by integrating tasks on fewer processors to minimize the energy consumption of a deadline constrained parallel application in a heterogeneous cloud computing system. However, our analysis revealed that turning off as many processors as possible does not necessarily lead to the minimization of total energy consumption. In this study, we propose an energy-aware processor merging (EPM) algorithm to select the most effective processor to turn off from the energy saving perspective, and a quick EPM (QEPM) algorithm to reduce the computation complexity of EPM. Experimental results on real and randomly generated parallel applications validate that the proposed EPM and QEPM algorithms can reduce more energy than existing methods at different scales, parallelism, and heterogeneity degrees.

Robust sensor system plays an important role in Internet of Things (IoTs). These intelligent sensors are required to be low cost and reliable, which provides confidentiality for private sensitive data. However, this protected system is still under the risk of Differential Fault Analysis (DFA) attacks. In this paper, we focus on DFA attacks to AES decryption as decryption receives the equalling importance as encryption. First, we induce a fault at the input of the third round in the procedure of AES decryption, in which w e successfully break it using one pair of fault-free and faulty plaintexts within 232 searching space. Then, we improve this attack by use of S-Box distribution table, which reduces the computational time from 853 ms to 70 ms on a dual Intel(R) Pentium(R) E6700 core (3.20 GHz). Compared to the existing work, our proposed attack reduces 79.5% computational time when both methods employ two pairs of fault-free and faulty ciphertexts/plaintexts.

The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new Augmented Lagrange Multiplier (ALM) algorithm to recover the weather monitoring data. A large amount of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Computer systems tend to be heterogeneous parallel and distributed computing systems, which are characterized by having various types of computational units interconnected via networks for executing multiple parallel jobs precedence-constrained tasks. Scheduling multiple jobs, which arrive at different instants, on such systems for fastest execution is a well-known NP-hard optimization problem. In order to achieve high-performance of systems, two important factors can be improved. One factor is the heterogeneity. Most algorithms use the upward rank value for ordering tasks and the earliest finish time for assigning processors. These two criteria can be improved to permit creating accurate and efficient schedules in heterogeneous distributed computing systems. Another factor is the fairness, existing algorithms are for static scheduling, and failed to make full use of the fairness in dynamic environments, such that obvious unfairness to longer-makespan jobs or shorter-makespan jobs can be caused. A dynamic multiple parallel jobs scheduling algorithm called F DMHSV (Fairness of Dynamic Multiple Heterogeneous Selection Value) is proposed to address the above problems to achieve high-performance of systems in this paper. Both example and extensive experimental evaluation demonstrate significant improvement of the F_DMHSV algorithm.

Energy savings optimization becomes one of the major concerns in the wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the limited nonrechargeable battery power. In this paper, we focus on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Following the principle of opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.

A high-performance scheduling for a DAG (Directed Acyclic Graph) task graph on heterogeneous networked embedded systems or parallel and distributed systems is to maximize concurrency and minimize inter-processor communication. Most of the algorithms using upward rank value for task prioritizing and earliest finish time for processor assignment. But both approaches ignored the heterogeneity of system and could not create accurate and efficient schedules. Yet no one has doubled about and recognized that. A fully heterogeneous task scheduling algorithm is proposed to address the above problems in this paper. The fundamentals of DAG model and corresponding algorithms are investigated. New concepts called Heterogeneous Upward Rank Value (HURV) and Heterogeneous Priority Rank Value (HPRV) are defined. An algorithm called Heterogeneous Select Value (HSV) is proposed in paper. Both benchmark and extensive experimental evaluation demonstrate the significant improvements in proposed algorithm.

Deformable part models show a remarkable detection performance for a variety of object categories. During training these models rely on energy-based methods and heuristic initialization to search and localize parts, equivalent to learning local object features. Due to weak supervision, however, those learnt part detectors contain lots of noise and are not enough reliable to classify the object. This paper investigates part localization problem and extends the latent-SVM by incorporating local consistency of image features. The objective is to adaptively select part sub-windows that overlap semantically meaningful components as much as possible, which leads to a more reliable learning of the part detectors in a weakly-supervised setting. The main idea of our method is estimating part-specific color/texture models as well as edge distribution within each training example, followed by a foreground segmentation for part localization. The experimental results show that we achieve an overall improvement of about 3% mAP over the latent-SVM on non-rigid objects.

We present a two-stage method to accurately segment single or multiple moving objects and their shadows, especially when the moving objects have similar chromaticity and intensity to their shadows or when they are immersed in the shadows of other moving objects. Our algorithm first detects potential shadows via brightness ratios at each motion region, which is already separated from the background of an image sequence. Movement patterns are then applied to optimize the regions of moving objects and their shadows. We conducted experiments using our captured image sequences and public videos of Highway I and II to verify our method. The results demonstrate the method's efficiency quantitatively and qualitatively in comparison with ground truth and several advanced methods.

WiFi access points, mesh routers, wireless sensors and any other wireless routers along the road can serve as roadside unit (RSU), and these RSUs can provide infrastructural supports for wireless access and data dissemination in cyber-transportation systems. We present a hybrid routing scheme in vehicular networks for inter-vehicle, vehicle-to-roadside and inter-roadside data dissemination in urban hybrid networks. First, a location-based crowdsensing framework, including online sensing and offline crowdsourcing, is proposed to retrieve the number and location of available RSU resources. Then, we combine RSU resources and ad hoc solutions to design a routing switch mechanism, which can guarantee quality of data dissemination under various network connectivity and deployment configurations. The performance of our hybrid data dissemination scheme is evaluated using both simulation and real testbed experiments.

This paper focuses on the fuzzy-H∞-filter-design problem for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Two cases of the time-varying delays are considered: 1) The delays are differentiable and have both the lower and upper bounds of the delay derivatives, and 2) the delays are bounded but not necessary to be differentiable. Since we employ a new fuzzy Lyapunov-Krasovskii functional (LKF) and estimate a tighter upper bound of its derivative, the proposed delay-derivative-dependent bound-real-lemma (BRL) condition has advantages over some previous results in that it enlarges the application scope but has less conservatism, which is established theoretically. The BRL condition that depends on both the upper and lower bounds of the delay derivatives is derived. Then, based on the aforesaid BRL, a new fuzzy H∞filter scheme is proposed, and a sufficient condition for the existence of such a filter is established in terms of linear-matrix inequalities (LMIs). Finally, some numerical examples and an application to the truck-trailer system are utilized to demonstrate the effectiveness and reduced conservatism of our results.

Reconfigurable system-on-chip (RSoC) is a promising alternative to deliver both flexibility and performance at the same time, and also a technical solution looking to the future needs of embedded applications. But the complex design process is impeding the development of extensive applications. This paper proposes an RSoC design methodology based on function-level programming model on account of the characteristics of the reconfigurable architecture. In the programming model, system designers use high-level language to complete functional specification by calling the co-function-library. Then the dynamic hardware/software partitioning algorithm will decide whether an invoked function should be running on hardware or software automatically. According to the partitioning result, the dynamic linker will switch functions' execution mode in real time. And the above items can facilitate an automatic design flow through specification to the system implementation. Experiments and tests have verified the feasibility and efficiency of the automatic design flow.

Spectrum detection technology is one of the key technologies in cognitive radio network (CRN), and its primary task is to identify the existence of spectrum holes and the appearance of authorized users. In order to meet the hard real-time and high reliable requirements of the spectrum detection in CRN, this paper presents a novel wide-band spectrum sensing algorithm, called clustering-based joint compressive sensing(C-JCS), which combines hierarchical data-fusion idea with jointly compressive reconstruction technology. To validate the efficiency and effectiveness, we compare the C-JCS with independent compressive sensing (ICS) and joint compressive sensing (JCS) in the detection probability, false-alarm probability and algorithm execution time under the circumstance of different SNR and compression ratio. The simulation results show that the C-JCS can sense the wide-band spectrum with high accuracy in time, so as to meet the requirements of the spectrum sensing in CRN.

Localization has many important applications in wireless sensor networks. A variety of wireless technologies, such as acoustic, infrared, and ultra-wide band (UWB) media have been applied for localization purposes. This paper consists of two parts. The first part presents new UWB-based communication protocols for received signal strength (RSS) information collection, namely, a robust UWB coding method called U-BOTH (UWB based on Orthogonal Variable Spreading Factor and Time Hopping), an ALOHA-type channel access method and a message exchange protocol to collect location information. The second part presents the localization algorithm, which is applied in coal mine environments. The localization algorithm first derives the corresponding UWB path loss model, then applies the maximum likelihood estimation (MLE) method to compute the distances to the reference sensors using the RSS information, and to estimate the coordinate of the moving sensor using least squares (LS) method. The performance of the system is validated using theoretic analysis and simulations. Results show that U-BOTH transmission technique can effectively reduce the bit error rate under the path loss model, and the corresponding ranging and localization algorithms can accurately compute object locations in coal mine environments.

Localization has many important applications in wireless sensor networks (WSNs). A variety of technologies, such as acoustic, infrared. and UWB (ultra-wide band) media have been utilized for localization purposes. In this paper, we propose a helistic, buttom-up design of a UWB-based communication architecture and related protocols for localization in WSNs. A new UWB coding method, called U-BOTH (UWB ased on Orthogonal Variable Spreading Factor and Time Hopping), is utilized for minimum interference communication, and an ALOHA-type channel access method and a message exchange protocol are used to collect distance information in WSNs. We derive the corresponding UWB path loss model in order to apply the maximum likelihood estimation (MLE) method to compute the distances between neighbor nodes using the RSSI information. Then, we propose NMDS-MLE (Non-metric Multidimensional Scaling and Maximum Likelihood Estimation) localization algorithms based on the two types of distance information: estimated distance and Euclidean distance. The performance of the system is validated using theoretic analysis and simulations.

The traditional networking builds on layered protocol architecture to isolate the complexities in different layers. It has been realized that real-life wireless sensor networks (WSNs) must be considered holistically across different layers for optimum performance. We consider a special case of WSNs that is deployed in underground tunnels. Underground communications present unique signal propagation characteristics due to the geographic and geological features, which in turn impact the underground network deployment and multi-hop routing patterns. We propose an efficient routing algorithm, called BRIT (bounce routing in tunnels), for underground WSNs, and evaluate BRIT against the bottomline AODV in terms of network throughput, packet loss rate, stability and latencies using simulations. The contributions of the paper include a hybrid signal propagation model in three dimentional underground tunnels, an assortment of sensor deployment strategies in tunnels, an integrated routing metric (forwarding speed), and a route suppression mechanism.

Based on the Bayesian network trust model, an improved trust model is proposed in this paper. It includes two parts: one is the improved searching resource peers algorithm, which can reduce the redundancy packages received by peers and enhance the reliability of system; the other is the improved trust computing and updating method, which applies logarithm for evaluation and the number of services to compute and update the trust, so that it can prevent malicious peers from attacking normal peers efficiently, meanwhile restrain malicious peers from increasing their trust respectively by collusion in its clique. The sketch maps of trust value illustrate that the new model controls the behavior of malicious peer in P2P more efficiently

In the movement control area, PTD algorithm is an effective means to adjust the motor dynamic quality. The paper introduces the architecture of the motor speed control and the transfer function of the motor. In addition, a motor speed control using digital PID is modelled and simulated based on Ptolemy II in the paper. By optimal designing, the performance of the control using integrator-disparted PID algorithm is better than that using other algorithms. The merit of the algorithm is verified in the digital PID control based on FPGA.