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An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

Mohammud J. Bocus, Juha Hakkinen, Helder Fontes, Marcin Drzewiecki, Senhui Qiu, Kerstin Eder, Robert Piechocki

TL;DR

The paper addresses energy efficiency in a Silicon-based Reconfigurable IoT (RIoT) node within the SUPERIOT framework by performing measurement-based energy profiling across BLE, NBVLC, sensing, and E-ink operations. It develops three end-to-end energy models for normal, low-power, and very low-power configurations and validates them with new data, achieving accuracy above $97\%$. The authors demonstrate substantial energy reductions through software optimizations (over $60\%$) and hardware-level tweaks (deep sleep, cut points, and E-ink LUT optimization), enabling effective energy budgeting for future iterations. This work provides a solid foundation for energy-aware design of hybrid and fully printed IoT nodes, including energy harvesting scenarios, and offers a benchmarking framework to track improvements across design cycles.

Abstract

This paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display. Extensive measurements were conducted to establish a detailed energy profile, which serves as a benchmark for evaluating the effectiveness of subsequent optimizations and future node iterations. To minimize the energy consumption, multiple optimizations were implemented at both the software and hardware levels, achieving a reduction of over 60% in total energy usage through software modifications alone. Further improvements were realized by optimizing the E-ink display driving waveform and implementing a very low-power mode for non-communication activities. Based on the measured data, three measurement-based energy consumption models were developed to characterize the energy behavior of the node under: (i) normal, unoptimized operation, (ii) low-power, software-optimized operation, and (iii) very low-power, hardware-optimized operation. These models, validated with new measurement data, achieved an accuracy exceeding 97%, confirming their reliability for predicting energy consumption in diverse configurations.

An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

TL;DR

The paper addresses energy efficiency in a Silicon-based Reconfigurable IoT (RIoT) node within the SUPERIOT framework by performing measurement-based energy profiling across BLE, NBVLC, sensing, and E-ink operations. It develops three end-to-end energy models for normal, low-power, and very low-power configurations and validates them with new data, achieving accuracy above . The authors demonstrate substantial energy reductions through software optimizations (over ) and hardware-level tweaks (deep sleep, cut points, and E-ink LUT optimization), enabling effective energy budgeting for future iterations. This work provides a solid foundation for energy-aware design of hybrid and fully printed IoT nodes, including energy harvesting scenarios, and offers a benchmarking framework to track improvements across design cycles.

Abstract

This paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display. Extensive measurements were conducted to establish a detailed energy profile, which serves as a benchmark for evaluating the effectiveness of subsequent optimizations and future node iterations. To minimize the energy consumption, multiple optimizations were implemented at both the software and hardware levels, achieving a reduction of over 60% in total energy usage through software modifications alone. Further improvements were realized by optimizing the E-ink display driving waveform and implementing a very low-power mode for non-communication activities. Based on the measured data, three measurement-based energy consumption models were developed to characterize the energy behavior of the node under: (i) normal, unoptimized operation, (ii) low-power, software-optimized operation, and (iii) very low-power, hardware-optimized operation. These models, validated with new measurement data, achieved an accuracy exceeding 97%, confirming their reliability for predicting energy consumption in diverse configurations.
Paper Structure (18 sections, 23 equations, 11 figures, 7 tables)

This paper contains 18 sections, 23 equations, 11 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Overview of Si-based RIoT Node
  5. Energy Measurement Setup
  6. Energy Measurement and Analysis
  7. Sensing and E-ink displaying test (while BLE connection active)
  8. NBVLC TX and NBVLC RX test (while BLE connection active)
  9. Si-based Node optimization
  10. Software optimization
  11. E-ink display optimization
  12. Hardware optimization
  13. Energy Modeling
  14. Normal (unoptimized) node operation
  15. Low power (software-optimized) node operation
  16. ...and 3 more sections

Figures (11)

  • Figure 1: Illustration of (a) Si-based RIoT node, (b) Cut points on node (see markings U1, U2, U6, U7 and U9).
  • Figure 2: Current measurement setup. Points U1, U2, U6, U7 and U9 were cut, and jumper wires soldered across them to allow easy connection with power profilers.
  • Figure 3: Current profile as measured across measuring point U2 when unoptimized E-ink display was active.
  • Figure 4: Current profile as measured across node during low power sensing, E-ink displaying (unoptimized) and NBVLC TX/RX while node is connected to a central device via BLE. nbvlc_off() function is not activated after NBVLC data reception.
  • Figure 5: Current profile as measured across measuring point U2 when optimized E-ink display was active.
  • ...and 6 more figures