Pith. sign in

REVIEW 4 major objections 4 minor 56 references

EStacker: Explaining Battery-Less IoT System Performance with Energy Stacks

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that battery-free IoT devices can be evaluated fairly and diagnosed precisely only when the testbed both replays identical energy and event environments and attributes every joule of harvested energy to a component or…

desk verdict A genuinely useful testbed with a new energy-attribution capability and a promising time-scaling method, but the design-space sweep in Section 7.2 needs real-time spot checks before the optimum is trusted. read the letter →

arxiv 2505.22366 v1 pith:XM4FSUQN submitted 2025-05-28 cs.AR

Lukas Liedtke , Per Gunnar Kjeldsberg , Frank Alexander Kraemer , Magnus Jahre This is my paper
classification cs.AR
keywords energyharvestingbattery-lessIoTintermittentcomputingperformanceevaluationstackstimescalingenergy-neutralsensingtestbed
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Battery-free IoT devices run entirely on harvested energy, so their performance depends on the exact timing and amount of that energy—yet existing testbeds cannot both reproduce identical energy and event environments and show where the energy went. This paper claims that both are necessary for fair, diagnosable evaluation, and introduces EStacker to supply them: it replays recorded irradiance and event traces so every configuration faces the same conditions, and it produces energy stacks that attribute every unit of harvested energy to hardware components and application activities. The paper further claims that its ST-SP optimization, which scales time, input power, and the application's sampling frequency by the same factor, cuts evaluation time by 6.3x on average while keeping throughput error around 7.7%. If these claims hold, developers can find performance bugs they cannot see in end-to-end metrics and can sweep solar-panel and capacitor design spaces in days rather than months.

What carries the argument

The energy stack is the central diagnostic object: a time-aggregated account that divides all harvested energy across supply-chain components and application activities, built from out-of-band voltage and current measurements sampled at up to 800 kHz plus activity signals reported by the SoC. The time-saving mechanism is the ST-SP scaling identity, $E = P \times t = (P \times S_{TP}) \times (t / S_{TP})$, which preserves total energy when time is compressed by a factor $S_{TP}$ and power is raised by the same factor. Because the application's idle power does not scale linearly with sampling frequency, the paper derives a closed-form correction factor $S_f$ (Equation 4) that tells the developer how much to raise the sampling frequency to hit the target average power. These two pieces together make a week-long experiment stand in for a month-long one while keeping the temporal activity profile intact.

What would settle it

Take the IMU benchmark at its 7x speed-up with a 2.6 F capacitor bank and measure the total energy actually delivered to the SoC/sensor subsystem under ST-SP versus real-time execution; Section 6.2.2 already reports a 13.7% excess, so a series of such measurements at several speed-up factors and capacitances that show the delivered-energy ratio deviating from 1 by more than the claimed 7.7% throughput error would falsify the linear-scaling premise.

Watch

Extended reading notes

Core claim

EStacker is the first evaluation platform for battery-less IoT that (i) controls both the energy environment and the event environment through trace replay and (ii) generates energy stacks, breaking total energy consumption into categories such as MPPT losses, energy-storage losses and residual, DC/DC conversion losses, and, within the SoC/sensor subsystem, sampling+processing, communication, idle, boot, and backup. The ST-SP optimization scales time, input power, and the sampling frequency together so that the real-time balance of supplied and consumed energy is preserved; across six benchmarks it achieves 6.3x average speed-up with 7.7% average absolute throughput error and 1.4% activity-profile error, compared with 21.2% and 28.6% for the scaled-time-unscaled-power baseline. Two case studies show the utility: an energy stack profile exposed an ESR-induced shutdown bug whose fix improved a time-of-flight benchmark's throughput 3.3x, and ST-SP turned a 25-configuration design-space sweep from 41.7 days into 7.7 days.

Load-bearing premise

The load-bearing premise is that proportionally scaling input power, time, and sampling frequency leaves the energy supply subsystem's efficiency unchanged—specifically that MPPT behavior, capacitor ESR losses, and charge/discharge dynamics scale linearly—and that throughput is proportional to the amount of active runtime.

Editorial extensions

If this is right

  • Developers can compare any two application or hardware configurations knowing that observed performance differences come from the designs themselves, not from weather or event timing, because EStacker replays identical traces.
  • Energy stacks make 'why is my device slow?' answerable: across the paper's benchmarks, roughly half the harvested energy is lost before it reaches the SoC—20.6% in storage, 22.6% in the MPPT, 12.8% in the DC/DC converter—while sampling and processing dominate application-level consumption.
  • ST-SP makes design-space exploration practical: a 25-point sweep of solar-panel and capacitor sizes took 7.7 days instead of 41.7 days, with throughput predictions within about 8% on average.
  • The skip-nights extension (ST-SP-SN) raises the average speed-up to 10.9x at 7.2% throughput error, which is useful for solar-powered devices that are dark and idle for long stretches.
  • Preserving the activity profile under scaling means evaluations retain charge/discharge cycles and boot/shutdown behavior, which is what reactive workloads actually depend on.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, energy stacks could serve as ground truth for validating faster analytical models and simulators of battery-less systems, since those tools necessarily abstract away component-level losses the stacks make visible.
  • The same scaled-time-scaled-power idea could be tested on other harvester types (thermoelectric, RF, motion) to see whether the 7.7% error band persists when MPPT and ESR behavior differ from solar-plus-supercap.
  • The TOF case study suggests energy stack profiles are a debugging instrument, not just a benchmarking output: the 3.3x improvement came from a bug (sensor left powered on after an ESR-induced reboot) that end-to-end throughput alone would not localize.
  • Because Section 6.2.2 shows errors rising at larger capacitances (10.6% throughput error at 3.0 F), practitioners should re-validate ST-SP at their specific design point or impose a capacitance-dependent speed-up cap.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper presents EStacker, an evaluation platform for battery-less IoT systems that controls both the energy environment (via replayable solar irradiance traces and an LED) and the event environment (via event traces), while measuring per-component and per-activity energy consumption to generate energy stacks. The authors propose ST-SP, a scaled-time, scaled-power strategy that accelerates evaluation by proportionally scaling time, input trace power, and the application's sampling frequency. Across six benchmarks, they report an average 6.3x speed-up with 7.7% average throughput error and 1.4% average activity profile error, compared with 21.2% and 28.6% for the prior ST-UP approach. Two case studies illustrate the platform's utility: energy stack profiles are used to diagnose a time-of-flight sensor power-gating problem, yielding a 3.3x throughput improvement, and ST-SP is used to sweep a 25-point solar-panel/capacitor design space for a smart parking application in 7.7 days rather than 41.7 days.

Significance. If the results hold, EStacker fills a genuine gap: existing testbeds such as EHTestbed, Shepherd, and Ekho provide repeatable energy environments but cannot attribute energy consumption to hardware components and application activities. The energy-stack contribution is well demonstrated by the TOF case study, which shows a concrete diagnostic payoff. ST-SP is a simple, clearly explained idea, and the direct quantitative comparison against ST-UP is appropriate. The paper is also commendably transparent about ST-SP's limitations, including its inapplicability to purely reactive applications and the nonlinear-efficiency effects documented in Section 6.2.2. However, the central accuracy claim is only as strong as the error measurements, and the design-space case study uses ST-SP exactly in the configuration range where those measurements show the largest, configuration-dependent bias.

major comments (4)
  1. [§6.2.2, §7.2] Section 6.2.2 shows that ST-SP's accuracy degrades in the capacitance range used by the design-space sweep: at 2.6 F the ESS provides 13.7% more energy to the SSS under ST-SP than in the baseline, and at 3.0 F the throughput error is 10.6%. Section 7.2 then performs the entire parking design-space sweep (C = 1.2-3.2 F, S_I = 0.02-0.10) using only ST-SP and never cross-validates against the real-time baseline. If this energy bias shifts configurations non-uniformly, the reported optimum (C = 1.7 F or 2.2 F with S_I = 0.1) and the event-detection percentages in Fig. 16a may not reflect real-time behavior. Please validate at least the reported optimum and the neighboring configurations from the sweep against the real-time baseline.
  2. [§6.2.2, Fig. 14] The statement that ST-SP 'retains its accuracy across the energy storage range' is based on averages (6.4% throughput error, 4.5% APE), but the 2.6 F and 3.0 F configurations have substantially larger individual errors, and the 2.6 F configuration changes the qualitative activity pattern (IMU active on day 2 under ST-SP, off in the baseline). Because these are exactly the configurations used in the §7.2 design-space exploration, the paper should report per-configuration errors and quantify the sensitivity of the Fig. 16a rankings to the observed bias.
  3. [§5.3, Figs. 10, 13, 14] All accuracy results appear to be single runs with no error bars or replication. Given that EStacker is designed to reproduce energy and event traces exactly, repeated runs are feasible and would make the 7.7% versus 21.2% throughput-error comparison and the 13.7% energy bias at 2.6 F much more convincing. Please add repetitions or at least state the measurement precision of the platform.
  4. [§5.3, Fig. 13] The Activity Profile Error is reported only after DTW with a one-hour window, and raw pre-DTW APE values are not given. Since 'retaining temporal behavior' is a central claim, the reader cannot judge how much of the 1.4% average APE is due to the filtering. Please report both raw and DTW-filtered APE, or provide an explicit justification for the one-hour window.
minor comments (4)
  1. [Abstract, §7.2, Fig. 16 caption] The abstract and Section 7.2 state the non-ST-SP design-space evaluation time as 41.7 days, but Figure 16's caption says 44.7 days; this inconsistency should be corrected.
  2. [§3.1] The term 'Data Aquisition and Control Unit (DACU)' is misspelled; it should be 'Data Acquisition and Control Unit'.
  3. [§6.2.1] For ST-SP-SN, the paper reports an average speed-up of 10.9x but does not explain how the skip-nights fast-forward interacts with the chosen S_TP values from Table 1; a sentence clarifying this would help the reader reconcile the two speed-up numbers.
  4. [Table 1, §5.1] The columns S_I, S_TP, and S_f are easy to confuse; the table caption or Section 5.1 should explicitly restate that S_I is the irradiance scaler for panel size, S_TP is the time/power scaling factor, and S_f is the sampling-frequency scaling factor.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: ST-SP and energy stacks are validated against independent real-time baselines and direct measurements.

full rationale

The paper's central quantitative claims are not circular. ST-SP's energy-preservation relation (Eq. 1) is an algebraic identity describing how time and power are scaled; it is a design choice, not a derived prediction. The throughput prediction (Eq. 5) is an explicit calibration model: its parameters come from a separate profiling step, and its output is compared against an independently measured real-time baseline (Section 6.2). The reported 7.7% average throughput error is therefore an empirical result, not a reconstruction of the model's own inputs. Energy stacks are aggregations of directly measured voltages and currents per component and activity, not quantities that are derived from the claims they support. The self-citations to the authors' prior work (e.g., PES [27], ESS [35]) are used for terminology and related-work context, not as load-bearing justification for ST-SP or the energy-stack method. The Section 7.2 design-space sweep is performed entirely with ST-SP and is not cross-validated against the real-time baseline; this is a validation-coverage limitation, and Section 6.2.2 explicitly documents configuration-dependent bias (13.7% energy difference at 2.6 F). Such model error is a correctness concern, not circularity, because the error is discovered by comparison with an independent baseline rather than defined into the prediction. No step was found in which a prediction reduces by construction to its inputs, a fitted parameter is renamed as a prediction, or a load-bearing uniqueness claim is imported from the authors' own prior work.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The ST-SP derivation (Eq. 4) uses profiling measurements P_A, P_I, T_App, and t_A as inputs; these are measured, not fitted to the result, so they are not counted as free parameters. The chosen experimental factors S_TP, S_I, and the DTW window are listed below because they affect the reported speed-up and error values. No new physical entities are introduced.

free parameters (3)
  • S_TP (max time and power scaling factor) = 2 to 10 per benchmark (Table 1)
    Chosen by hand by increasing S_TP until the sampling-frequency constraint or LED power limit is reached; directly determines the claimed 6.3x average speed-up.
  • S_I (irradiance scaler) = 1.5 to 3.0 per benchmark (Table 1)
    Chosen by hand to supply enough energy for each benchmark; equivalent to changing solar panel area and influences activity levels.
  • DTW window size = 1 hour
    Chosen for the APE metric in Section 5.3 to filter minor timing differences; the reported APE values depend on this choice and raw APE is not reported.
assumptions (4)
  • domain assumption ST-SP applies only to applications with periodicity
    Section 4.4 states purely event-driven applications cannot be sped up because the event trace would need to run faster than the energy trace, breaking temporal alignment.
  • domain assumption Throughput is proportional to active runtime
    Used in Eq. 5 to predict real-time throughput from profiling and scaled-experiment runtime; the paper acknowledges in Section 6.2.2 that nonlinear ESS effects can change active-period lengths, so this is approximate.
  • domain assumption Input power traces scale linearly when multiplied by S_TP
    Section 4.2 states traces can be directly multiplied by the scaling factor; depends on LED driver linearity and solar cell response, and the ST-SP-SN skip-nights variant assumes zero-irradiance periods can be skipped.
  • domain assumption Proportional scaling preserves energy storage charge and discharge dynamics
    This is the core ST-SP premise in Section 4.1; Section 6.2.2 shows it can fail at 2.6 F and 3.0 F because higher input power changes ESS efficiency.

how reviews work

0 comments
Cite this review

Pith. "Pith review of EStacker: Explaining Battery-Less IoT System Performance with Energy Stacks." pith.science (2026). https://pith.science/paper/XM4FSUQN

@misc{pith2026250522366,
  author       = {Pith},
  title        = {Pith review of: EStacker: Explaining Battery-Less IoT System Performance with Energy Stacks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XM4FSUQN}},
  note         = {Machine review of arXiv:2505.22366}
}
read the original abstract

The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The key alternative is energy harvesting, but battery-less IoT systems require extensive evaluation to demonstrate that they are sufficiently performant across the full range of expected operating conditions. IoT developers thus need an evaluation platform that (i) ensures that each evaluated application and configuration is exposed to exactly the same energy environment and events, and (ii) provides a detailed account of what the application spends the harvested energy on. We therefore developed the EStacker evaluation platform which (i) provides fair and repeatable evaluation, and (ii) generates energy stacks. Energy stacks break down the total energy consumption of an application across hardware components and application activities, thereby explaining what the application specifically uses energy on. We augment EStacker with the ST-SP optimization which, in our experiments, reduces evaluation time by 6.3x on average while retaining the temporal behavior of the battery-less IoT system (average throughput error of 7.7%) by proportionally scaling time and power. We demonstrate the utility of EStacker through two case studies. In the first case study, we use energy stack profiles to identify a performance problem that, once addressed, improves performance by 3.3x. The second case study focuses on ST-SP, and we use it to explore the design space required to dimension the harvester and energy storage sizes of a smart parking application in roughly one week (7.7 days). Without ST-SP, sweeping this design space would have taken well over one month (41.7 days).

Figures

Figures reproduced from arXiv: 2505.22366 by the authors.

Figure 2
Figure 2. The hardware architecture of a state-of-the-art battery-less IoT system [10], [23]–[26]. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The EStacker evaluation platform. EStacker enables (i) repeatable evaluation of EH-based IoT systems and (ii) accurately attributing energy consumption to the IoT system components and application tasks through energy stacks. 3.2 The Introspection and Control Unit (ICU) EStacker supports two different state-of-the-art approaches for emulating realistic energy inputs. The first method is inspired by EHTestbed [19] an… view at source ↗
Figure 4
Figure 4. Real-time evaluation compared to 2× accelerated evaluation with ST-UP and ST-SP. Capturing both perfor￾mance and temporal behavior of real-time execution requires proportionally scaling time, input, and output power. achieve sufficiently high sampling frequencies, and we hence added the DCPU to push the sampling frequency up to 800 kHz. The DCPU interfaces with the ADCs to receive the raw sample data, converts them … view at source ↗
Figures from the paper (12 more)
Figure 5
Figure 5. Figure 5: Example of task execution in a periodic IoT application. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The 4-step process for evaluating a given IoT application with ST-SP. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: The selected outdoor solar trace, recorded from 20.10.2022 - 24.10.2022 in southern Germany (station 1) [44]. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Activity Profile Error (APE) example with the [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Energy consumption per IoT system component (Figure 9a) and per SoC task (Figure 9b), recorded during [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Evaluation time speed-up and throughput prediction error for ST-UP, ST-SP, and ST-SP-SN, compared [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Irradiance trace versus energy storage voltage for [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 13
Figure 13. Figure 13: Activity profile prediction error (APE) rel [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]
Figure 14
Figure 14. Figure 14: ST-SP error for IMU at 7× speed-up across energy storage configurations. ST-SP retains its accuracy across a range of different energy storage sizes. ST-SP relative to the baseline when running IMU with its maximum 7× speed-up. Overall, ST-SP retains its accuracy acro…
Figure 15
Figure 15. Figure 15: Figure 15a zooms in on the time window from 880 seconds to 1,240 seconds of the execution of the TOF benchmark. The first key observation is the periodic and large (green) Sensor spikes that occur when the TOF benchmark enables 17 [PITH_FULL_IMAGE:figures/full_fig_p0…
Figure 15
Figure 15. Figure 15: Energy stack profile excerpt for TOF with 0.2 second time steps demonstrating a performance problem and its solution. Energy stack profiles provide insight into performance issues that are challenging to detect with end-to-end analysis. the time-of-flight sensor. Thes…
Figure 16
Figure 16. Figure 16: Design space exploration with EStacker on a smart parking application. [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

56 extracted references · 56 canonical work pages

  1. [1]

    [Online]

    Matt Arnott, Global IoT Forecast Report, 2023-2033, 2024. [Online]. Available:https://transformainsights. com/research/reports/global-iot-forecast-report-2023-2033

    work page 2023

  2. [2]

    High-temperature self-powered sensing system for a smart bearing in an aircraft jet engine,

    B. Zaghari, A. S. Weddell, K. Esmaeili, et al. , “High-temperature self-powered sensing system for a smart bearing in an aircraft jet engine, ”IEEE Transactions on Instrumentation and Measurement , vol. 69, 9 2020

    work page 2020

  3. [3]

    Design and Performance Eval- uation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring,

    M. Giordano, N. Baumann, M. Crabolu, R. Fischer, G. Bellusci, and M. Magno, “Design and Performance Eval- uation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring, ”IEEE Transactions on Instrumentation and Measurement , vol. 71, 2022

    work page 2022

  4. [4]

    Battery-Free and Wireless Technologies for Cardiovascular Implantable Medical Devices,

    J. Zhang, R. Das, J. Zhao, N. Mirzai, J. Mercer, and H. Heidari, “Battery-Free and Wireless Technologies for Cardiovascular Implantable Medical Devices, ”Advanced Materials Technologies, vol. 7, 6 2022

    work page 2022

  5. [5]

    Orbital edge computing: Nanosatellite constellations as a new class of computer system,

    B. Denby and B. Lucia, “Orbital edge computing: Nanosatellite constellations as a new class of computer system, ” inProceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) , 2020

    work page 2020

  6. [6]

    Tartan Artibeus: A Batteryless, Computational Satellite Research Platform,

    B. Denby, E. Ruppel, S. Kumar, Z. Manchester, and B. Lucia, “Tartan Artibeus: A Batteryless, Computational Satellite Research Platform, ” 2022. [Online]. Available: https : / / digitalcommons . usu . edu / smallsat / 2022/all2022/54/

    work page 2022

  7. [7]

    Research infrastructure to power the Internet of Things,

    M. Hayes, G. Fagas, J. Donnelly, et al., “Research infrastructure to power the Internet of Things, ” EnABLES project, Tech. Rep., 2021

    work page 2021

  8. [8]

    Is TinyML sustainable?

    S. Prakash, M. Stewart, C. Banbury, et al., “Is TinyML sustainable?” Communications of the ACM , vol. 66, 11 2023

    work page 2023

Show all 56 references
  1. [9]

    Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things,

    M. Gorlatova, J. Sarik, G. Grebla, M. Cong, I. Kymissis, and G. Zussman, “Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things, ”IEEE Journal on Selected Areas in Communications , vol. 33, 8 2015

    2015

  2. [10]

    Battery-less zero-maintenance embedded sensing at the mithræum of circus maximus,

    M. Afanasov, N. A. Bhatti, D. Campagna, et al. , “Battery-less zero-maintenance embedded sensing at the mithræum of circus maximus, ” inProceedings of the Conference on Embedded Networked Sensor Systems (Sen- Sys), 2020

    2020

  3. [11]

    Design of an RFID-Based Battery- Free Programmable Sensing Platform,

    A. P. Sample, D. J. Yeager, P. S. Powledge, A. V. Mamishev, and J. R. Smith, “Design of an RFID-Based Battery- Free Programmable Sensing Platform, ”IEEE Transactions on Instrumentation and Measurement, vol. 57, 11 2008

    2008

  4. [12]

    Realistic and Repeatable Emulation of Energy Harvesting Environments,

    J. Hester, L. Sitanayah, T. Scott, and J. Sorber, “Realistic and Repeatable Emulation of Energy Harvesting Environments, ”ACM Transactions on Sensor Networks , vol. 13, 2 2017

    2017

  5. [13]

    J. L. Hennessy and D. A. Patterson, Computer Architecture - A Quantitative Approach. Morgan Kaufmann Pub- lishers, 2018

    2018

  6. [14]

    A Top-Down method for performance analysis and counters architecture,

    A. Yasin, “A Top-Down method for performance analysis and counters architecture, ” in Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS) , 2014

    2014

  7. [15]

    Multi-stage CPI stacks,

    S. Eyerman, W. Heirman, K. Du Bois, and I. Hur, “Multi-stage CPI stacks, ” IEEE Computer Architecture Letters, vol. 17, 2018

    2018

  8. [16]

    A performance counter architecture for computing accurate CPI components,

    S. Eyerman, L. Eeckhout, T. Karkhanis, and J. E. Smith, “A performance counter architecture for computing accurate CPI components, ” inProceedings of the International Conference on Architectural Support for Program- ming Languages and Operating Systems (ASPLOS) , 2006. 21 A p...

    2006

  9. [17]

    Per-instruction cycle stacks through time-proportional event anal- ysis,

    B. Gottschall, L. Eeckhout, and M. Jahre, “Per-instruction cycle stacks through time-proportional event anal- ysis, ”IEEE Micro, vol. 44, 4 2024

    2024

  10. [18]

    Speedup stacks: Identifying scaling bottlenecks in multi-threaded applications,

    S. Eyerman, K. Du Bois, and L. Eeckhout, “Speedup stacks: Identifying scaling bottlenecks in multi-threaded applications, ” inProceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS), 2012

    2012

  11. [19]

    Environment and application testbed for low-power energy harvesting system design,

    L. Sigrist, A. Gomez, M. Leubin, J. Beutel, and L. Thiele, “Environment and application testbed for low-power energy harvesting system design, ”IEEE Transactions on Industrial Electronics , vol. 68, 11 2021

    2021

  12. [20]

    Shepherd: A portable testbed for the batteryless IoT,

    K. Geissdoerfer, M. Chwalisz, and M. Zimmerling, “Shepherd: A portable testbed for the batteryless IoT, ” in Proceedings of the Conference on Embedded Networked Sensor Systems (SenSys) , 2019

    2019

  13. [21]

    eProfiler: High-Precision Power Monitoring System for IoT Devices Featuring Extreme Dynamic Range of Operation,

    G. Kazdaridis, I. Zografopoulos, N. Sidiropoulos, P. Symeonidis, and T. Korakis, “eProfiler: High-Precision Power Monitoring System for IoT Devices Featuring Extreme Dynamic Range of Operation, ” inProceedings of the International Workshop on Energy Harvesting and Energy-Neutr...

    2020

  14. [22]

    Measurement and validation of energy harvesting IoT devices,

    L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele, “Measurement and validation of energy harvesting IoT devices, ” in Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017

    2017

  15. [23]

    A Reconfigurable Energy Storage Architecture for Energy-Harvesting De- vices,

    A. Colin, E. Ruppel, and B. Lucia, “A Reconfigurable Energy Storage Architecture for Energy-Harvesting De- vices, ” inProceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2018

    2018

  16. [24]

    Predictive Power Management for Internet of Battery-Less Things,

    Q. Ju and Y. Zhang, “Predictive Power Management for Internet of Battery-Less Things, ” IEEE Transactions on Power Electronics, vol. 33, 1 2018

    2018

  17. [25]

    Camaroptera: A batteryless long-range remote visual sensing system,

    M. Nardello, H. Desai, D. Brunelli, and B. Lucia, “Camaroptera: A batteryless long-range remote visual sensing system, ” inProceedings of the International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems (ENSsys), 2019

    2019

  18. [26]

    An Architectural Charge Management Interface for Energy-Harvesting Systems,

    E. Ruppel, M. Surbatovich, H. Desai, K. Maeng, and B. Lucia, “An Architectural Charge Management Interface for Energy-Harvesting Systems, ” inProceedings of the International Symposium on Microarchitecture (MICRO) , 2022

    2022

  19. [27]

    PES: An energy and throughput model for energy harvesting IoT sys- tems,

    F. Ghasemi, L. Liedtke, and M. Jahre, “PES: An energy and throughput model for energy harvesting IoT sys- tems, ” inProceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS), 2023

    2023

  20. [28]

    A Tale of Two Intermittencies,

    M. L. Wymore and H. Duwe, “A Tale of Two Intermittencies, ” in Proceedings of the Conference on Embedded Networked Sensor Systems (SenSys) , 2023

    2023

  21. [29]

    Protean: An energy-efficient and heterogeneous platform for adaptive and hardware-accelerated battery-free computing,

    A. Bakar, R. Goel, J. de Winkel, et al., “Protean: An energy-efficient and heterogeneous platform for adaptive and hardware-accelerated battery-free computing, ” in Proceedings of the Conference on Embedded Networked Sensor Systems (SenSys), 2023

    2023

  22. [30]

    Flicker: Rapid Prototyping for the Batteryless Internet-of-Things,

    J. Hester and J. Sorber, “Flicker: Rapid Prototyping for the Batteryless Internet-of-Things, ” in Proceedings of the Conference on Embedded Networked Sensor Systems (SenSys) , 2017

    2017

  23. [31]

    [Online]

    European Union, Photovoltaic geographical information system , 2025. [Online]. Available: https://re.jrc. ec.europa.eu/pvg%5C_tools/en/

    2025

  24. [32]

    Ekho: Realistic and repeatable experimentation for tiny energy-harvesting sensors,

    J. Hester, T. Scott, and J. Sorber, “Ekho: Realistic and repeatable experimentation for tiny energy-harvesting sensors, ” inProceedings of the Conference on Embedded Networked Sensor Systems (SenSys) , 2014

    2014

  25. [33]

    Realistic simulation for tiny batteryless sensors,

    M. Furlong, J. Hester, K. Storer, and J. Sorber, “Realistic simulation for tiny batteryless sensors, ” in Proceedings of the International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems (ENSsys) , 2016

    2016

  26. [34]

    Smart parking in IoT-Enabled cities: A survey,

    F. Al-Turjman and A. Malekloo, “Smart parking in IoT-Enabled cities: A survey, ”Sustainable Cities and Society, vol. 49, 2019

    2019

  27. [35]

    ESS: Repeatable evaluation of energy harvesting subsystems for industry-grade IoT platforms,

    F. Ghasemi, L. Liedtke, and M. Jahre, “ESS: Repeatable evaluation of energy harvesting subsystems for industry-grade IoT platforms, ” inIEEE International Symposium on Workload Characterization (IISWC) , 2023

    2023

  28. [36]

    Texas Instruments Inc., Texas Instruments TMP117 Data Sheet , https : / / www . ti . com / lit / ds / symlink / tmp117.pdf, 2022

    2022

  29. [37]

    22 A preprint - August 27, 2025

    InvenSense Inc., TDK ICM-42670-P Data Sheet, https://invensense.tdk.com/wp-content/uploads/2021/ 07/ds-000451_icm-42670-p-datasheet.pdf , 2022. 22 A preprint - August 27, 2025

    2021

  30. [38]

    STMicroelectronics NV, ST STHS34PF80 Data Sheet , https : / / www . st . com / resource / en / datasheet / sths34pf80.pdf, 2023

    2023

  31. [39]

    pdf, 2024

    STMicroelectronics NV, ST VL53L1X Data Sheet , https://www.st.com/resource/en/datasheet/vl53l1x. pdf, 2024

    2024

  32. [40]

    Analog Devices Inc., Analog Devices MAX32664 Data Sheet , https : / / www . analog . com / media / en / technical-documentation/data-sheets/MAX32664.pdf, 2022

    2022

  33. [41]

    Maxim Integrated Products Inc., Maxim MAX30101 Data Sheet , https : / / www . analog . com / media / en / technical-documentation/data-sheets/MAX30101.pdf, 2020

    2020

  34. [42]

    Supporting peripherals in intermittent systems with just-in-time checkpoints,

    K. Maeng and B. Lucia, “Supporting peripherals in intermittent systems with just-in-time checkpoints, ” in Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) , 2019

    2019

  35. [43]

    Torex Semiconductor Ltd., Torex XC6135 Data Sheet , https://product.torexsemi.com/system/files/ series/xc6135.pdf, 2024

    2024

  36. [44]

    Dittmann, F

    A. Dittmann, F. Dinger, W. Herzberg, et al., PV-Live dataset - Measurements of global horizontal and tilted solar irradiance, 2024. [Online]. Available: https://zenodo.org/doi/10.5281/zenodo.11092107

    2024 doi

  37. [45]

    Sanyo Semiconductor Co., Ltd., AM-5412 Amorphous Solar Cell Data Sheet , https://docs.rs-online.com/ dd86/0900766b80d10cf7.pdf, 2022

    2022

  38. [46]

    Texas Instruments Inc., TI BQ25504 Data Sheet , https://www.ti.com/lit/ds/symlink/bq25504.pdf, 2023

    2023

  39. [47]

    Analog Devices Inc., AD MAX17223 Data Sheet , https : / / www . analog . com / media / en / technical - documentation/data-sheets/MAX17220-MAX17225.pdf, 2021

    2021

  40. [48]

    Nordic Semiconductor, nRF52832 Product Specification, https://docs.nordicsemi.com/bundle/nRF52832_ PS_v1.9/resource/nRF52832_PS_v1.9.pdf, 2023

    2023

  41. [49]

    infineon

    Infineon Technologies AG, Infineon ILD8150 Data Sheet , https : / / www . infineon . com / dgdl / Infineon - ILD8150_ILD8150E-DataSheet-v01_05-EN.pdf?fileId=5546d462696dbf120169ba96613f4c20 , 2022

    2022

  42. [50]

    kippzonen

    Kipp & Zonen B.V., Kipp & Zonen SP Lite2 Brochure , https : / / www . kippzonen . com / Download / 358 / Brochure-Pyranometers-SP-Lite2-English , 2024

    2024

  43. [51]

    Dynamic programming algorithm optimization for spoken word recognition,

    H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition, ” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 26, 1 1978

    1978

  44. [52]

    Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data,

    F. Piccialli, F. Giampaolo, E. Prezioso, D. Crisci, and S. Cuomo, “Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data, ”ACM Transactions on Internet Technology, vol. 21, 3 2021

    2021

  45. [53]

    Riotee: An Open-Source Hardware and Software Platform for the Battery-Free Internet of Things,

    K. Geissdoerfer and M. Zimmerling, “Riotee: An Open-Source Hardware and Software Platform for the Battery-Free Internet of Things, ” inProceedings of the Conference on Embedded Networked Sensor Systems (Sen- Sys), 2024

    2024

  46. [54]

    REHASH: A flexible, developer focused, heuristic adaptation platform for intermittently powered computing,

    A. Bakar, A. G. Ross, K. S. Yildirim, and J. Hester, “REHASH: A flexible, developer focused, heuristic adaptation platform for intermittently powered computing, ”Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 5, 3 2021

    2021

  47. [55]

    FlockLab 2: Multi-Modal Testing and Validation for Wireless IoT,

    R. Trüb, R. Da Forno, L. Sigrist, et al., “FlockLab 2: Multi-Modal Testing and Validation for Wireless IoT, ” 2020. [Online]. Available: http://hdl.handle.net/20.500.11850/442038

    2020

  48. [56]

    FlockLab: A testbed for distributed, syn- chronized tracing and profiling of wireless embedded systems,

    R. Lim, F. Ferrari, M. Zimmerling, C. Walser, P. Sommer, and J. Beutel, “FlockLab: A testbed for distributed, syn- chronized tracing and profiling of wireless embedded systems, ” inProceedings of the International Conference on Information Processing in Sensor Networks (ISPN) ...

    2013

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.