On the Effectiveness of Energy Metering on Every Node

Li, Qiang; Martins, Marcelo L.; Gnawali, Omprakash; Fonseca, Rodrigo

doi:10.1109/dcoss.2013.67

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Downsides relate to the inaccurate reproduction of reality due to environmental changes, varying hardware tolerances, and many other dynamics. Even if conditions and hardware are exactly the same, different device instances can exhibit significant variations in their consumption [20]. Also, simulations mainly focus on improving a priori adjustments, leaving runtime optimizations open to other solutions.…”

Section: Simulation and Estimation Of Power Consumptionmentioning

confidence: 99%

Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Rottleuthner,

Schmidt,

Wählisch

2019

Preprint

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Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling. However, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service. In this paper, we present Eco, a hardware-software co-design enabling generic energy management on IoT nodes. Eco is tailored to devices with limited resources and thus targets most of the upcoming IoT scenarios. The proposed measurement module combines commodity components with a common system interfaces to achieve easy, flexible integration with various hardware platforms and the RIOT IoT operating system. We thoroughly evaluate and compare accuracy and overhead. Our findings indicate that our commodity design competes well with highly optimized solutions, while being significantly more versatile. We employ Eco for energy management on RIOT and validate its readiness for deployment in a five-week field trial integrated with energy harvesting. CCS CONCEPTS• Computer systems organization → Embedded systems; • Hardware → Energy metering.

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Section: Simulation and Estimation Of Power Consumptionmentioning

confidence: 99%

Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Rottleuthner,

Schmidt,

Wählisch

2019

Preprint

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“…The documentation could highly differ from a manufacturer data sheet as the first is based on runtime energy measurements, and takes into account the surrounding environment (deployment environment noise, interaction with the target platform, etc.). Besides, a component data sheet is not always in accordance with the real energy figures [14]. Finally, a motivation and a long-term objective of our proposal is to be able to use the provided energy models as inputs for energy simulators in order to make them more reliable, and reduce the gap existing between the simulation and the real world [15].…”

Section: A Motivationsmentioning

confidence: 99%

Automatic Inference of Energy Models for Peripheral Components in Embedded Systems

Cherifi

Vantroys

Boé³

et al. 2017

2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)

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Surrounding autonomous embedded devices are in a constant expansion. The advent and the rise of Internet of Things (IoT) enable these objects to take a giant step forward, especially regarding their large scale deployment in real-world applications of the everyday life. A significant part of these objects are battery-powered and energy-dependent. Thus, energy is a critical resource which greatly complicates the development of the embedded software. By decomposing the energy consumption of a battery-powered IoT device, we can see that peripheral components are the major contributors among the overall consumption. Indeed, these components are exploited and repeatedly used by the object to interact and communicate with its surrounding environment during all the application lifetime. Acquire the expertise to handle accurately, during the development stage, the behaviour of every on-board peripheral component is a big challenge to improve the development of IoT embedded applications.To guide the developer in this task, we propose an automated inference procedure of energy models for peripheral components. An accurate automata-based model of the energy consumption can be generated, with only little efforts from the developer, based on real runtime measurements, providing precise energy figures. The proposed process is focused on a lightweight code generation step and simple analyses of the energy output traces, allowing a quick regeneration of the models in the case of a peripheral component modification. We show the potentials of the proposed procedure by real experiments on real peripherals. The obtained results are satisfactory, and we believe that our proposition is able to enhance the embedded development in an energy-constrained environment.

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“…However, as new technologies such as 802.11 and LTE are being adopted by IoT, it is important to profile the energy efficiency of devices using these complex technologies. Specifically, it is difficult to simulate and evaluate the properties of real-world environments (e.g., interference), physical layer, and MAC layer parameters, and study their effects on energy consumption [9]- [11]. For example, while the energy consumption characteristics of 802.15.4 devices are mostly affected by MAC parameters, 802.11's physical layer parameters are diverse and significantly affect energy consumption.…”

Section: Introductionmentioning

confidence: 99%

EMPIOT: An energy measurement platform for wireless IoT devices

Dezfouli

Amirtharaj

2018

Journal of Network and Computer Applications

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Profiling and minimizing the energy consumption of resource-constrained devices is an essential step towards employing IoT in various application domains. Due to the large size and high cost of commercial energy measurement platforms, alternative solutions have been proposed by the research community. However, the three main shortcomings of existing tools are complexity, limited measurement range, and low accuracy. Specifically, these tools are not suitable for the energy measurement of new IoT devices such as those supporting the 802.11 technology. In this paper we propose EMPIOT, an accurate, low-cost, easy to build, and flexible power measurement platform. We present the hardware and software components of this platform and study the effect of various design parameters on accuracy and overhead. In particular, we analyze the effects of driver, bus speed, input voltage, and buffering mechanism on sampling rate, measurement accuracy and processing demand. These extensive experimental studies enable us to configure the system in order to achieve its highest performance. We also propose a novel calibration technique and report the calibration parameters under various settings. Using five different IoT devices performing four types of workloads, we evaluate the performance of EMPIOT against the ground truth obtained from a high-accuracy industrial-grade power measurement tool. Our results show that, for very low-power devices that utilize 802.15.4 wireless standard, the measurement error is less than 3.5%. In addition, for 802.11based devices that generate short and high power spikes, the error is less than 2.5%.

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On the Effectiveness of Energy Metering on Every Node

Cited by 8 publications

References 27 publications

Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Automatic Inference of Energy Models for Peripheral Components in Embedded Systems

EMPIOT: An energy measurement platform for wireless IoT devices

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