Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata

To analyze complex and heterogeneous real-time embedded systems, recent works have proposed interface techniques between real-time calculus (RTC) and timed automata (TA), in order to take advantage of the strengths of each technique for analyzing various components. But the time to analyze a state-based component modeled by TA may be prohibitively high, due to the state space explosion problem. In this paper, we propose a framework of granularity-based interfacing to speed up the analysis of a TA modeled component. First, we abstract fine models to work with event streams at coarse granularity. We perform analysis of the component at multiple coarse granularities and then based on RTC theory, we derive lower and upper bounds on arrival patterns of the fine output streams using the causality closure algorithm. Our framework can help to achieve tradeoffs between precision and analysis time.Comment: QAPL 201

A Comprehensive Experimental Comparison of Event Driven and Multi-Threaded Sensor Node Operating Systems

The capabilities of a sensor network are strongly influenced by the operating system used on the sensor nodes. In general, two different sensor network operating system types are currently considered: event driven and multi-threaded. It is commonly assumed that event driven operating systems are more suited to sensor networks as they use less memory and processing resources. However, if factors other than resource usage are considered important, a multi-threaded system might be preferred. This paper compares the resource needs of multi-threaded and event driven sensor network operating systems. The resources considered are memory usage and power consumption. Additionally, the event handling capabilities of event driven and multi-threaded operating systems are analyzed and compared. The results presented in this paper show that for a number of application areas a thread-based sensor network operating system is feasible and preferable

Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

Stochastic Learning Feedback Hybrid Automata for Power Management in Embedded Systems

In this paper we show that stochastic learning automata based feedback control switching strategy can be used for dynamic power management (DPM) employed at the system level. DPM strategies are usually incorporated at the operating systems of embedded devices to exploit multiple power states available in today\u27s ACPI compliant devices. The idea is to switch between power states depending on the device usage, and since device usage times are not deterministic, probabilistic techniques are often used to create stochastic strategies, or strategies that make decisions based on probabilities of device usage spans. Previous work (Irani et al., 2001) has shown how to approximate the probability distribution of device idle times and dynamically update them, and then use such knowledge in controlling power states. Here, we use stochastic learning automata (SLA) which interacts with the environment to update such probabilities, and then apply techniques similar to (Irani et al., 2001) to optimize power usage with minimal effect on response time for the devices

Run-time Energy Management for Mobiles

Due to limited energy resources, mobile computing requires an energy-efficient a rchitecture. The dynamic nature of a mobile environment demands an architecture that allows adapting to (quickly) changing conditions. The mobile has to adapt d ynamically to new circumstances in the best suitable manner. The hardware and so ftware architecture should be able to support such adaptability and minimize the energy consumption by making resource allocation decisions at run-time. To make these decisions effective, a tradeoff has to be made between computation , communication and initialization costs (both time and energy). This paper describes our approach to construct a model that supports taking such decisions

No-Sense: Sense with Dormant Sensors

Wireless sensor networks (WSNs) have enabled continuous monitoring of an area of interest (body, room, region, etc.) while eliminating expensive wired infrastructure. Typically in such applications, wireless sensor nodes report the sensed values to a sink node, where the information is required for the end-user. WSNs also provide the flexibility to the end-user for choosing several parameters for the monitoring application. For example, placement of sensors, frequency of sensing and transmission of those sensed data. Over the years, the advancement in embedded technology has led to increased processing power and memory capacity of these battery powered devices. However, batteries can only supply limited energy, thus limiting the lifetime of the network. In order to prolong the lifetime of the deployment, various efforts have been made to improve the battery technologies and also reduce the energy consumption of the sensor node at various layers in the networking stack. Of all the operations in the network stack, wireless data transmission and reception have found to consume most of the energy. Hence many proposals found in the literature target reducing them through intelligent schemes like power control, reducing retransmissions, etc. In this article we propose a new framework called Virtual Sensing Framework (VSF), which aims to sufficiently satisfy application requirements while conserving energy at the sensor nodes.Comment: Accepted for publication in IEEE Twentieth National Conference on Communications (NCC-2014

Applying real-time interface and calculus for dynamic power management in hard real-time systems

Power dissipation has been an important design issue for a wide range of computer systems in the past decades. Dynamic power consumption due to signal switching activities and static power consumption due to leakage current are the two major sources of power consumption in a CMOS circuit. As CMOS technology advances towards deep sub-micron domain, static power dissipation is comparable to or even more than dynamic power dissipation. This article explores how to apply dynamic power management to reduce static power for hard real-time systems. We propose online algorithms that adaptively control the power mode of a system, procrastinating the processing of arrived events as late as possible. To cope with multiple event streams with different characteristics, we provide solutions for preemptive earliest-deadline-first and fixed-priority scheduling policies. By adopting a worst-case interval-based abstraction, our approach can not only tackle arbitrary event arrivals, e.g., with burstiness, but also guarantee hard real-time requirements with respect to both timing and backlog constraints. We also present extensive simulation results to demonstrate the effectiveness of our approache