Managing multi-mode tasks with time cost and quality levels using optimal discrete control synthesis

International audienceReal-time control systems are complex to design, and automation support is important. We are interested in systems with multiple tasks, each with multiple modes, implementing a functionality with different levels of quality (e.g., computation approximation), and cost (e.g., computation time, energy). It is complex to control the switching of modes in order to insure properties like bounding cost while maximizing quality. We outline a technique for the automatic generation of such controllers involving an automaton-based formal model, and using optimal discrete control synthesi

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Model-based design of correct controllers for dynamically reconfigurable architectures

International audienceDynamically reconfigurable hardware has been identified as a promising solution for the design of energy efficient embedded systems. However, its adoption is limited by the costly design effort including verification and validation, which is even more complex than for non dynamically reconfigurable systems. In this paper, we propose a tool-supported formal method to automatically design a correct-by-construction control of the reconfiguration. By representing system behaviors with automata, we exploit automated algorithms to synthesize controllers that safely enforce reconfiguration strategies formulated as properties to be satisfied by control. We design generic modeling patterns for a class of reconfigurable architectures, taking into account both hardware architecture and applications, as well as relevant control objectives. We validate our approach on two case studies implemented on FPGAs

A domain-specific language for task handlers generation, applying discrete controller synthesis

We propose a simple programming language, called Nemo, specific to the domain of multi-task real-time embedded systems, such as in robotic, automotive or avionics systems. It can be used to specify a set of resources with usage constraints, a set of tasks that consume them according to various modes, and applications sequencing the tasks. We obtain automatically an application-specific task handler that correctly manages the constraints (if there exists one), through a compilation-like process including a phase of discrete controller synthesis. This way, this formal technique contributes to the safety of the designed systems, while being encapsulated in a tool that makes it usable by end-users and application experts. Our approach is based on the synchronous modelling techniques, languages and tools

A Domain-Specific Language for Multitask Systems, Applying Discrete Controller Synthesis

International audienceWe propose a simple programming language, called Nemo, specific to the domain of multitask real-time control systems, such as in robotic, automotive, or avionics systems. It can be used to specify a set of resources with usage constraints, a set of tasks that consume them according to various modes, and applications sequencing the tasks. We automatically obtain an application-specific task handler that correctly manages the constraints (if there exists one), through a compilation-like process including a phase of discrete controller synthesis. This way, this formal technique contributes to the safety of the designed systems, while being encapsulated in a tool that makes it usable by application experts. Our approach is based on the synchronous modelling techniques, languages, and tools

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

Automatic generation of safe handlers for multi-task systems

International audienceWe are interested in the programming of real-time embedded control systems, such as in robotic, automotive or avionic systems. They are designed with multiple tasks, each with multiple modes. It is complex to design task handlers that control the switching of activities in order to insure safety properties of the global system. We propose a model of tasks in terms of transition systems, designed especially with the purpose of applying existing discrete controller synthesis techniques. This provides us with a systematic methodology, for the automatic generation of safe task handlers, with the support of synchronous languages and associated tools