Time- and Energy-Aware Task Scheduling in Environmentally-Powered Sensor Networks

Hanschke, Lars; Renner, Christian

doi:10.1007/978-3-030-14094-6_9

Cited by 1 publication

(2 citation statements)

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“…How budget B is spent is determined by the scheduler. Literature knows various techniques, for example, by using program flows [28] or a Lazy Scheduling Algorithm (LSA) [30]. In contrast to schedulers, which decide when to spend energy, energy managers decide how much energy to spend.…”

Section: (A) (B)mentioning

confidence: 99%

“…As a consequence, the power demand of a sensor node

P_{normaln} (t)

simplifies to

P_{normaln} (t) = V_{normaln} \cdot I_{normaln} (t),

with

I_{n} (t)

being the current drawn by the node. This current varies with used peripherals, for example, radio or MCU, and has to be obtained by in‐lab measurements [27, 28] or on‐line [29]. Over a time interval

normalΔ t

I_{n} (t)

usually fluctuates but the literature for energy management [7] has shown that the time average over

normalΔ t

can be used without sacrificing accuracy.…”

Section: Design Of Emrepmentioning

confidence: 99%

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EmRep: Energy management relying on state‐of‐charge extrema prediction

Hanschke

Renner

2021

IET Computers & Digital Tech

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The persistent rise of Energy Harvesting Wireless Sensor Networks entails increasing demands on the efficiency and configurability of energy management. New applications often profit from or even require user-defined time-varying utilities, for example, the health assessment of bridges is only possible at rushhour. However, monitoring times do not necessarily overlap with energy harvest periods. This misalignment is often corrected by over-provisioning the energy storage. Favourable small-footprint and cheap energy storage, however, fill up quickly and waste surplus energy. Hence, EmRep is presented, which decouples the energy management of high-intake from low-intake harvest periods. Based on the State-of-Charge extrema prediction, the authors enhance energy management and reduce saturation of energy storage by design. Considering multiple user-defined utility profiles, the benefits of EmRep in combination with a variety of prediction algorithms, time resolutions, and energy storage sizes are showcased. EmRep is tailored to platforms with small energy storage, in which it is found that it doubles effective utility, and also increases performance by 10% with large-sized storage. K E Y W O R D S embedded system, energy consumption, hardware-software codesignThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: (A) (B)mentioning

confidence: 99%

“…As a consequence, the power demand of a sensor node