Invited - Energy harvesting and transient computing

Merrett, Geoff V.

doi:10.1145/2897937.2905011

Cited by 12 publications

(2 citation statements)

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“…To overcome these issues, batteryless IoT devices have been proposed [4,5]. The energy used by these devices is directly harvested from environmental and renewable sources such as solar, wind, and radio-frequency (RF).…”

Section: Motivationmentioning

confidence: 99%

Interweaving Real-Time Jobs with Energy Harvesting to Maximize Throughput

Schieber

Samineni

Vahidi

2023

Preprint

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Motivated by baterryless IoT devices, we consider the following scheduling problem. The input includes $n$ unit time jobs $\calJ = \lrc{J_1, \ldots, J_n}$, where each job $J_i$ has a release time $r_i$, due date $d_i$, energy requirement $e_i$, and weight $w_i$. We consider time to be slotted; hence, all time related job values refer to slots. Let $T=\max_i\lrc{d_i}$. The input also includes an $h_t$ value for every time slot $t$ $\lrp{1 \leq t \leq T}$, which is the energy harvestable on that slot. Energy is harvested at time slots when no job is executed. The objective is to find a feasible schedule that maximizes the weight of the scheduled jobs. A schedule is feasible if for every job $J_j$ in the schedule and its corresponding slot $t_j$, $t_{j} \neq t_{j'}$ if ${j} \neq {j'}$, $r_j \leq t_j \leq d_j$, and the available energy before $t_j$ is at least $e_j$. To the best of our knowledge, we are the first to consider the theoretical aspects of this problem. In this work we show the following. \textsf{(1)} A polynomial time algorithm when all jobs have identical $r_i, d_i$ and $w_i$. \textsf{(2)} A $\frac{1}{2}$-approximation algorithm when all jobs have identical $w_i$ but arbitrary $r_i$ and $d_i$. \textsf{(3)} An FPTAS when all jobs have identical $r_i$ and $d_i$ but arbitrary $w_i$. \textsf{(4)} Reductions showing that all the variants of the problem in which at least one of the attributes $r_i$, $d_i$, or $w_i$ are not identical for all jobs are $\NPH$.

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Section: Motivationmentioning

confidence: 99%

Interweaving Real-Time Jobs with Energy Harvesting to Maximize Throughput

Schieber

Samineni

Vahidi

2023

Preprint

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“…Consideration needs to be given to the volatility of harvested energy and whether storage is actually required for the vast majority of environmental monitoring applications. Transient computing offers an attractive alternative to the long-term storage of harvested energy [8] and could negate the need to store harvested energy in large quantities locally in a sensor node. This technique utilises only instantaneous energy available from an energy-harvesting system, performing processing tasks when energy is sufficient and turning off when it can no longer sustain operation.…”

Section: Introductionmentioning

confidence: 99%

Powering the Environmental Internet of Things

Curry

Harris

2019

Sensors

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The Internet of Things (IoT) is a constantly-evolving area of research and touches almost every aspect of life in the modern world. As technology moves forward, it is becoming increasingly important for these IoT devices for environmental sensing to become self-powered to enable long-term operation. This paper provides an outlook on the current state-of-the-art in terms of energy harvesting for these low-power devices. An analytical approach is taken, first defining types of environments in which energy-harvesters operate, before exploring both well-known and novel energy harvesting techniques and their uses in modern-day sensing.

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