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.

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.

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.

An IPC-based Prolog design pattern for integrating backward chaining inference into applications or embedded systems

Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems. However, Prolog is not suitable to develop many kinds of components, such as data acquisition and task scheduling, which are also crucial. To make the best use of the advantages and bypass the disadvantages, it is attractive to integrate Prolog with programs developed by other languages. In this paper, an IPC-based method is used to integrate backward chaining inference implemented by Prolog into applications or embedded systems. A Prolog design pattern is derived from the method for reuse, whose principle and definition are provided in detail. Additionally, the design pattern is applied to a target system, which is free software, to verify its feasibility. The detailed implementation of the application is given to clarify the design pattern. The design pattern can be further applied to wide range applications and embedded systems and the method described in this paper can also be adopted for other logic programming languages.

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.

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.

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.

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.

The VILMA intelligent vehicle:an architectural design for cooperative control between driver and automated system

Intelligent autonomous vehicles have received a great degree of attention in recent years. Although the technology required for these vehicles is relatively advanced, the challenge is firstly to ensure that drivers can understand the capabilities and limitations of such systems and secondly to design a system that can handle the interaction between the driver and the automated intelligent system. In this study, we describe an approach using different strategies for an autonomous system and a driver to drive a vehicle cooperatively. The proposed strategies are referred to as cooperative planning and control and determine when and how the path projected by the autonomous system can be changed safely by the driver to a path that he wishes to follow. The first phase of the project is described, covering the design and implementation of an autonomous test vehicle. Experiments are carried out with a driver to test the cooperative planning and control concepts proposed here.

A Sensor Network Web Platform Based on WoT Technology

This study proposes a Web platform,the Web of Things(WoT),whose Internet of Things(IoT)architecture is used to develop the technology behind a new standard Web platform.When a remote sensor passes data to a microcontroller for processing,the protocol is often not known.This study proposes a WoT platform that enables the use of a browser in a mobile device to control a remote hardware device.An optimized code is written using an artificial intelligencebased algorithm in a microcontroller.Digital data convergence technology is adopted to process the packets of different protocols and place them on the Web platform for access by other mobile devices.The platform has high efficiency and cross-platform advantages,with no limitation on the operating system.Message queueing telemetry transport(MQTT)technology is used to simplify the original HTTP protocol.Assume that the mobile device is a subscriber,i.e.,the controller,and a microcontroller that connects the sensing device is the publisher.The publishers and subscribers of MQTT need not know each other if they share a message broker.The intermediate agent role is much like a router.Publishers and subscribers do not need to interact,and publishers do not have to wait for subscriber confirmation to cause interactive permission be locked.Nor must publishers and subscribers be online at the same time,and they are free to choose when to get messages.The proposed WoT method is compared with the traditional IoT method regarding data transfer.The results show that the proposed method can save time in processing large amounts of data,as the traditional IoT method wastes time,especially in data format transfer.

Hybridity in Embedded Computing Systems

An embedded system is a system that computer is used as a componelit ina larger device. In this paper, we study hybridity in embedded systems andpresent an interval based temporal logic to express and reason about hybridproperties of such ldnd of systems.

Development of a Wireless Environmental Data Acquisition Prototype Adopting Agile Practices: An Experience Report

The traditional software development model commonly named “waterfall” is unable to cope with the increasing functionality and complexity of modern embedded systems. In addition, it is unable to support the ability for businesses to quickly respond to new market opportunities due to changing requirements. As a response, the software development community developed the Agile Methodologies (e.g., extreme Programming, Scrum) which were also adopted by the Embedded System community. However, failures and bad experiences in applying Agile Methodologies to the development of embedded systems have not been reported in the literature. Therefore, this paper contributes a detailed account of our first-time experiences adopting an agile approach in the prototype development of a wireless environment data acquisition system in an academic environment. We successfully applied a subset of the extreme Programming (XP) methodology to our software development using the Python programming language, an experience that demonstrated its benefits in shaping the design of the software and also increasing productivity. We used an incremental development approach for the hardware components and adopted a “cumulative testing” approach. For the overall development process management, however, we concluded that the Promise/Commitment-Based Project Management (PB-PM/CBPM) was better suited. We discovered that software and hardware components of embedded systems are best developed in parallel or near-parallel. We learned that software components that pass automated tests may not survive in the tests against the hardware. Throughout this rapid prototyping effort, factors like team size and our availability as graduate students were major obstacles to fully apply the XP methodology.

Design and Development of Remote Laboratories with Internet of Things Setting

Given the recent developments of Internet of Things (IoT) researchers and academics are trying to utilize this concept for different innovative applications. One of those applications of IoT is the remote laboratories, where real hardware/software experimentation systems can be controlled and manipulated over the web. During the COVID-19 pandemic, this has become almost essential for academic and research activities. The paper starts with highlighting the necessity and current status of remote laboratories in terms of shortcomings and possible recourse. With these in mind, the second part illustrates an infrastructure of a remote laboratory facility as well as describes the development of a number of prototype remote laboratories that are utilized for academic activities.

Survivability Specification Framework for Dependability-Functionality Codesign of ERTS

In ordet to maintain the dependability of system and meet the functional needof users dtsire, this paper introduces a survivability mechanism into embedded real-time system,and proposes a general comprehensive, approach based on a rigorous definition of survivability. Thisapproach permits a trade-off between the function and the cost of system development. It emphasizesthe ultradependable implementation of crucial function without demanding that of entire system.

Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.

A Survey of Language-Based Approaches to Cyber-Physical and Embedded System Development

As computers continue to advance, they are becoming more capable of sensing, interacting, and communicating with the physical and cyber world. Medical devices, electronic braking systems in automotive applications, and industrial control systems are examples of the many Cyber-Physical Systems （CPS） that utilize these computing capabilities. Given the potential consequences of software related failures in such systems, a high degree of safety, security, and reliability is often required. Programming languages are important tools used by programmers to develop CPS. They provide a programmer with the ability to transform designs into machine code. Of equal importance is their ability to detect and avoid programming mistakes. The development of CPS has predominantly been accomplished using the C programming language. Although C is a powerful language, it lacks features present in other languages that facilitate the development of reliable systems. This has prompted research into language-based alternatives for improving program quality through the use of programming languages. This paper presents an overview of the characteristics of embedded and cyber-physical systems and the associated requirements imposed on programming languages. This is followed by a survey of relevant research into language- based methods for creating safe, reliable, and robust software for CPS.

Embedded fuzzing:a review of challenges,tools,and solutions

Fuzzing has become one of the best-established methods to uncover software bugs.Meanwhile,the market of embedded systems,which binds the software execution tightly to the very hardware architecture,has grown at a steady pace,and that pace is anticipated to become yet more sustained in the near future.Embedded systems also beneft from fuzzing,but the innumerable existing architectures and hardware peripherals complicate the development of general and usable approaches,hence a plethora of tools have recently appeared.Here comes a stringent need for a systematic review in the area of fuzzing approaches for embedded systems,which we term'embedded fuzzing"for brevity.The inclusion criteria chosen in this article are semi-objective in their coverage of the most relevant publication venues as well as of our personal judgement.The review rests on a formal definition we develop to represent the realm of embedded fuzzing.It continues by discussing the approaches that satisfy the inclusion criteria,then defines the relevant elements of comparison and groups the approaches according to how the execution environment is served to the system under test.The resulting review produces a table with 42 entries,which in turn supports discussion suggesting vast room for future research due to the limitations noted.

A regeneratable dynamic differential evolution algorithm for neural networks with integer weights

Neural networks with integer weights are more suited for embedded systems and hardware implementations than those with real weights. However, many learning algorithms, which have been proposed for training neural networks with float weights, are inefficient and difficult to train for neural networks with integer weights. In this paper, a novel regeneratable dynamic differential evolution algorithm (RDDE) is presented. This algorithm is efficient for training networks with integer weights. In comparison with the conventional differential evolution algorithm (DE), RDDE has introduced three new strategies: (1) A regeneratable strategy is introduced to ensure further evolution, when all the individuals are the same after several iterations such that they cannot evolve further. In other words, there is an escape from the local minima. (2) A dynamic strategy is designed to speed up convergence and simplify the algorithm by updating its population dynamically. (3) A local greedy strategy is introduced to improve local searching ability when the population approaches the global optimal solution. In comparison with other gradient based algorithms, RDDE does not need the gradient information, which has been the main obstacle for training networks with integer weights. The experiment results show that RDDE can train integer-weight networks more efficiently.

Robust state of charge and state of health estimation for batteries using a novel multi model approach

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.