TY - UNPB
T1 - An Energy-Aware RIoT System
T2 - Analysis, Modeling and Prediction in the SUPERIOT Framework
AU - Bocus, Mohammud J.
AU - Hakkinen, Juha
AU - Fontes, Helder
AU - Drzewiecki, Marcin
AU - Qiu, Senhui
AU - Eder, Kerstin
AU - Piechocki, Robert
N1 - 14 pages, 13 figures, 11 tables
PY - 2025/1/17
Y1 - 2025/1/17
N2 - 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.
AB - 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.
KW - cs.ET
KW - cs.AR
KW - cs.NI
KW - cs.PF
KW - cs.SY
KW - eess.SY
U2 - 10.48550/arXiv.2501.10093
DO - 10.48550/arXiv.2501.10093
M3 - Preprint
BT - An Energy-Aware RIoT System
ER -
