Modeling and Analysis of Scheduling for Distributed Real-time Embedded Systems

Aimed at the deficiencies of resources based time Petri nets （RBTPN） in doing scheduling analysis for distributed real-time embedded systems, the assemblage condition of complex scheduling sequences is presented to easily compute scheduling length and simplify scheduling analysis. Based on this, a new hierarchical RBTPN model is proposed. The model introduces the definition of transition border set, and represents it as an abstract transition. The abstract transition possesses all resources of the set, and has the highest priority of each resource; the cxecution time of abstract transition is the longest time of all possible scheduling sequences. According to the characteristics and assemblage condition of RBTPN, the refinement conditions of transition border set are given, and the conditions ensure the correction of scheduling analysis. As a result, it is easy for us to understand the scheduling model and perform scheduling analysis.

Low-Cost Embedded Controller for Complex Control Systems

Because of limited resource of embedded platforms, the computational complexity of advanced control algorithms raises significant challenges for the use of embedded systems in complex control field. A Scilab/Scicos based embedded controller is developed on which various control software can be easily modeled, simulated, implemented, and evaluated to meet the ever-expanding requirements of industrial control applications. Built on the Cirrus Logic EP9315 ARM systems-on-chip board, this embedded controller is possible to develop complex embedded control systems that employ advanced control strategies in a rapid and cost-efficient fashion. Due to the free and open source nature of the software packages used, the cost of the embedded controller is minimized.

Aspect-Oriented Design Method for Embedded Systems Based on Timed Statecharts

The formal modelling and verification method has become an effective way of improving the reliability and correctness of complex,safety-critical embedded systems.Statecharts are widely used to formally model embedded applications,but they do not realise the reasonable separation of system concerns,which would result in code scattering and tangling.Aspect-Oriented Software Development(AOSD)technology could separate crosscutting concerns from core concerns and identify potential problems in the early phase of the software development life cycle.Therefore,the paper proposes aspect-oriented timed statecharts(extended timed statecharts with AOSD)to separately model base functional requirements and other requirements(e.g.,scheduling,error handling),thereby improving the modularity and development efficiency of embedded systems.Furthermore,the dynamic behaviours of embedded systems are simulated and analysed to determine whether the model satisfies certain properties(e.g.,liveness,safety)described by computation tree logic formulae.Finally,a given case demonstrates some desired properties processed with respect to the aspect-oriented timed statecharts model.

Energy-Efficient Deterministic Fault-Tolerant Scheduling for Embedded Real-Time Systems

By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability of the system was analyzed through checkpointing and the energy saving was considered via dynamic voltage and frequency scaling. Simulation results showed that the proposed algorithm had better performance compared with the existing voltage allocation techniques. The proposed technique saves 51.5% energy over FT-Only and 19.9% over FT ＋ EC on average. Therefore, the proposed method was more appropriate for aperiodic tasks in embedded real-time systems.

Cloud Control Systems

The concept of cloud control systems is discussed in this paper, which is an extension of networked control systems (NCSs). With the development of internet of things (IOT), the technology of NCSs has played a key role in IOT. At the same time, cloud computing is developed rapidly, which provides a perfect platform for big data processing, controller design and performance assessment. The research on cloud control systems will give new contribution to the control theory and applications in the near future. © 2014 Chinese Association of Automation.

Review on Cyber-physical Systems

Cyber-physical systems CPS are complex systems with organic integration and in-depth collaboration of computation, communications and control 3C technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS. © 2014 Chinese Association of Automation.

Water Supply Networks as Cyber-physical Systems and Controllability Analysis

Cyber-physical systems (CPS) is a system of systems which consists of many subsystems that can stand alone in an individual manner and can be taken as a typical complex network. CPS can be applied in the critical infrastructures such as water supply networks, energy supply systems, and so on. In this paper, we analyze the structure of modern city water supply networks from the view of CPS theory. we use complex network theory to build an undirected and unweighted complex network model for the water supply networks to investigate the structural properties, and present the structure of the water supply networks and detect communities by a spectral analysis of the Laplacian matrix. Then, we analyze the structure and controllability of water supply networks by the structural controllability method. The results show the feasibility and effectiveness of the proposed complex network model. © 2014 Chinese Association of Automation.

Decentralized Event-Triggered Average Consensus for Multi-Agent Systems in CPSs with Communication Constraints

The paper investigates decentralized event-triggered average consensus problem for multi-agent systems in cyberphysical systems (CPSs) with communication constraints. To reduce communication burden and improve the communication efficiency of multi-agent systems in CPSs, event-trigger is distributed at subsystem/agent level. A multi-agent system is then modeled as a reduced dimension hybrid system by taking into account decentralized event-triggered mechanism, communication delays and data dropouts within one framework. Some sufficient conditions for average consensus of each agent and an upper bound of communication delay and maximal allowable number of successive data dropouts (MANSD) are obtained, which can conveniently provide the relationship between the triggering parameters, communication constraints and the system stability. Specially, the quantitative relationship between the triggering parameters, MANSD and the system stability is derived. Finally, simulation results are given to illustrate the effectiveness of the proposed method. © 2014 Chinese Association of Automation.

Security-aware Signal Packing Algorithm for CAN-based Automotive Cyber-physical Systems

Network and software integration pose severe challenges in cyber-security for controller area network (CAN)-based automotive cyber-physical system (ACPS), therefore we employ message authentication code (MAC) to defend CAN against masquerade attack, but the consequent bandwidth overhead makes it a necessity to find the tradeoff among security, real-time and bandwidth utilization for signal packing problem (SPP) of CAN. A mixed-security signal model is firstly proposed to formally describe the properties and requirements on security and real-time for signals, and then a mixed-integer linear programming (MILP) formulation of SPP security-aware signal packing (SASP) is implemented to solve the tradeoff problem, where the bandwidth utilization is improved and the requirements in both security and real-time are met. Experiments based on both society of automotive engineers (SAE) standard signal set and simulated signal set showed the effectiveness of SASP by comparing with the state-of-the-art algorithm. © 2014 Chinese Association of Automation.

A Priority-aware Frequency Domain Polling MAC Protocol for OFDMA-based Networks in Cyber-physical Systems

Wireless networking in cyber-physical systems (CPSs) is characteristically different from traditional wireless systems due to the harsh radio frequency environment and applications that impose high real-time and reliability constraints. One of the fundamental considerations for enabling CPS networks is the medium access control protocol. To this end, this paper proposes a novel priority-aware frequency domain polling medium access control (MAC) protocol, which takes advantage of an orthogonal frequency-division multiple access (OFDMA) physical layer to achieve instantaneous priority-aware polling. Based on the polling result, the proposed work then optimizes the resource allocation of the OFDMA network to further improve the data reliability. Due to the non-polynomial-complete nature of the OFDMA resource allocation, we propose two heuristic rules, based on which an efficient solution algorithm to the OFDMA resource allocation problem is designed. Simulation results show that the reliability performance of CPS networks is significantly improved because of this work. © 2014 Chinese Association of Automation.

Partitioned k-Exclusion Real-Time Locking Protocol Motivated by Multicore Multi-GPU Systems

Graphic processing units （GPUs） have been widely recognized as cost-efficient co-processors with acceptable size, weight, and power consumption. However, adopting GPUs in real-time systems is still challenging, due to the lack in framework for real-time analysis. In order to guarantee real-time requirements while maintaining system utilization ~in modern heterogeneous systems, such as multicore multi-GPU systems, a novel suspension-based k-exclusion real-time locking protocol and the associated suspension-aware schedulability analysis are proposed. The proposed protocol provides a synchronization framework that enables multiple GPUs to be efficiently integrated in multicore real-time systems. Comparative evaluations show that the proposed methods improve upon the existing work in terms of schedulability.

Embedded Software Control of a Hybrid Vehicle＇s ECU/FCM System

This paper describes a research project that uses embedded systems design principles to construct and simulate an Engine Control Unit （ECU） for a hybrid car. The ECU is designed to select a fuel type based on the stress level of the simulated engine. The primary goal of the project was to use a robotics kit, connected to sensors, to simulate a hybrid car under certain stress conditions such as hill climbing or full throttle. The project uses the LEGO~ Mindstorms~ NXT robotics kit combined with a Java-based firmware, a pressure sensor to simulate a gas pedal, and a tilt sensor to determine when the car is traveling uphill or downhill. The objective was to develop, through simulation, a framework for adjusting the ratios/proportions of fuel types and mixture under the stress conditions. The expected result was to establish a basis for determining the ideal/optimal fuel-mix-stress ratios on the hybrid car＇s performance. Using the NXT robotics kit abstracted the low level details of the embedded system design, which allowed a focus on the high level design details of the research. Also, using the NXJ Java-based firmware allowed the incorporation of object oriented design principles into the project. The paper outlines the evolution and the compromises made in the choice of hardware and software components, and describes the computations and methodologies used in the project.

A Product Line Approach to Design an Embedded Web System for Healthcare

With the recent advances in mobile technology and wireless network technology, embedded systems are being widely used in modem society today. Particularly, a home healthcare system is a networked embedded system where the main functions are to control the disease processes and to help patients maintain their independence and maximum level of function within their own homes and communities. It seems to be self-evident to design a system that would support both patients and their healthcare providers in the process of treatment. Nevertheless, little work in integrating embedded devices with intemet for the support of patients have been done to date. In this paper, we show how to design a healthcare system for supporting the management of the conditions of patients with chronic diseases. This system is built around wireless networked embedded devices, and integrates the intemet technology for telemonitoring the patient＇s health and notifying of doctors if emergency action is required. Also, patients themselves may specify personal alerts for condition-related issues.

Specification and Verification of Dynamically Reconfigurable Systems Using Dynamic Linear Hybrid Automata

A dynamically reconfigurable system can change its configuration during operation, and studies of such systems are being carried out in many fields. In particular, medical technology and aerospace engineering must ensure system safety because any defect will have serious consequences. Model checking is a method for verifying system safety. In this paper, we propose the Dynamic Linear Hybrid Automaton (DLHA) specification language and show a method to analyze reachability for a system consisting of several DLHAs.

Thermoelectric energy harvesting for internet of things devices using machine learning:A review

Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.

Cyber-physical-social System in Intelligent Transportation

A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it's a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail. © 2014 Chinese Association of Automation.

Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means

Dataset classification is an essential fundament of computational intelligence in cyber-physical systems (CPS). Due to the complexity of CPS dataset classification and the uncertainty of clustering number, this paper focuses on clarifying the dynamic behavior of acceleration dataset which is achieved from micro electro mechanical systems (MEMS) and complex image segmentation. To reduce the impact of parameters uncertainties with dataset classification, a novel robust dataset classification approach is proposed based on neighbor searching and kernel fuzzy c-means (NSKFCM) methods. Some optimized strategies, including neighbor searching, controlling clustering shape and adaptive distance kernel function, are employed to solve the issues of number of clusters, the stability and consistency of classification, respectively. Numerical experiments finally demonstrate the feasibility and robustness of the proposed method. © 2014 Chinese Association of Automation.

Cost Minimization of Wireless Sensor Networks with Unlimited-lifetime Energy for Monitoring Oil Pipelines

Cyber-physical-system (CPS) has been widely used in both civil and military applications. Wireless sensor network (WSN) as the part and parcel of CPS faces energy problem because sensors are battery powered, which results in limited lifetime of the network. To address this energy problem, we take advantage of energy harvesting device (EHD) and study how to indefinitely prolong oil pipeline monitoring network lifetime by reasonable selecting EHD. Firstly, we propose a general strategy worst case-energy balance strategy (WC-EBS), which defines worst case energy consumption (WCEC) as the maximum energy sensor node could expend for oil pipeline monitoring WSN. When the energy collected by EHD is equal or greater than WCEC, network can have an unlimited lifetime. However, energy harvesting rate is proportional to the price of EHD, WC-EBS will cause high network cost. To reduce network cost, we present two optimization strategies, optimization workloadenergy balance strategy (OW-EBS ) and optimization first nodeenergy balance strategy (OF-EBS). The main idea of OW-EBS is to cut down WCEC by reducing critical node transmission workload; OF-EBS confirms critical node by optimizing each sensor node transmission range, then we get the optimal energy harvesting rate in OF-EBS. The experimental results demonstrate that OF-EBS can indefinitely extend network lifetime with lower cost than WC-EBS and OW-EBS, and energy harvesting rate P in each strategy satisfies POF-EBS ≤POW-EBS ≤PWC-EBS. © 2014 Chinese Association of Automation.

A Concept of Dynamically Reconfigurable Real-time Vision System for Autonomous Mobile Robotics

This paper describes specific constraints of vision systems that are dedicated to be embedded in mobile robots. If PC-based hardware architecture is convenient in this field because of its versatility, flexibility, performance, and cost, current real-time operating systems are not completely adapted to long processing with varying duration, and it is often necessary to oversize the system to guarantee fail-safe functioning. Also, interactions with other robotic tasks having more priority are difficult to handle. To answer this problem, we have developed a dynamically reconfigurable vision processing system, based on the innovative features of Cleopatre real-time applicative layer concerning scheduling and fault tolerance. This framework allows to define emergency and optional tasks to ensure a minimal quality of service for the other subsystems of the robot, while allowing to adapt dynamically vision processing chain to an exceptional overlasting vision process or processor overload. Thus, it allows a better cohabitation of several subsystems in a single hardware, and to develop less expensive but safe systems, as they will be designed for the regular case and not rare exceptional ones. Finally, it brings a new way to think and develop vision systems, with pairs of complementary operators.

Energy Efficient Scheduler of Aperiodic Jobs for Real-time Embedded Systems

Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no longer the only metric by which optimality is judged.In fact,energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's lifetime.In this work,we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption.Specifically,we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling(DVFS)technique.We address a variant of earliest deadline first(EDF)scheduling algorithm called energy saving-dynamic voltage and frequency scaling(ES-DVFS)algorithm that is suited to unpredictable future energy production and irregular job arrivals.We prove that ES-DVFS cannot attain a total value greater than C/ˆSα,whereˆS is the minimum speed of any job and C is the available energy capacity.We also investigate the implications of having in advance,information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS.We show that such advance knowledge makes possible the design of semi-on-line algorithm,ES-DVFS∗∗,that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor.The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.