Power Characterization of Raspberry Pi Agricultural Sensor Nodes Using Arduino Based Voltmeter

Cabaccan, Carlo N.; Reidj, Febus; Cruz, Febus Reidj G.

doi:10.1109/ccoms.2018.8463240

Cited by 10 publications

(4 citation statements)

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“…Power and energy consumption metrics are used in multiple papers to study specific characteristics of IoT and edge systems [6], [10]- [14]. These papers use values from literature [10], local unique [9] or average [12], [13] measurements. Simulations can be an alternative for experiments [10], [15].…”

Section: Related Workmentioning

confidence: 99%

“…In [9], authors characterize Raspberry Pi 3B nodes, that are part of an agricultural monitoring wireless sensor network, from a power perspective during different workloads: idle, sensing, transmitting, and file logging. The goal is to estimate minimum and maximum lifespan of the battery powering sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…An API allows interactions with the testbed. In our experiments, we use all available Raspberry Pi 3B, 5 nodes at Grenoble, widely used in edge and IoT research [8], [9].…”

Section: A Testbedmentioning

confidence: 99%

“…We conduct our experiments using Raspberry Pies, all 3 B available in the testbed, as these nodes are highly studied in literature for prototyping [8] and deployments of IoT and edge systems [9]. However, fine-grain studies of power and energy variability are not conducted on these types of nodes.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Variability of Power and Energy Consumption for IoT Edge Nodes

Tofaily,

Raïs,

Anshus

2023

2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

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For IoT and edge systems, measuring, predicting and optimizing energy consumption is an open field. It is important to accurately and precisely characterize power and energy consumption of edge nodes, as energy can be a scarce and key resource. However, there are no fine-grain studies that aim at understanding the potential variability of power and energy consumption of edge nodes. Existing research works give minor or no significance to this potential variability.This paper addresses this problem by quantifying the variability of power and energy consumption on a single edge node, and among multiple homogeneous edge nodes, for three scenarios: Idle, CPU intensive "matrix product" and RAM intensive "flip". These scenarios are found in edge applications. Identical controlled experiments are repeated thoroughly, for each scenario.Results show that power and energy variability exist for all studied scenarios. On a single node, power and energy variability measurements are relatively low. On multiple homogeneous nodes, the variability can be significant. For example, for CPU intensive "matrix product", the variability in energy is equivalent to an idle up-time, in a month, of 7 hours and 52 hours, when considering single and multiple homogeneous nodes, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…An API allows interactions with the testbed. In our experiments, we use all available Raspberry Pi 3B, 5 nodes at Grenoble, widely used in edge and IoT research [8], [9].…”

Section: A Testbedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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