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

TL;DR

This paper analyzes and models the energy consumption of a reconfigurable IoT node, demonstrating over 60% energy savings through software and hardware optimizations, and providing accurate predictive models for various operational states.

Contribution

It offers a detailed energy profile, optimization strategies, and validated models for energy prediction in a Silicon-based RIoT node within the SUPERIOT framework.

Findings

Over 60% energy reduction achieved through software modifications.

Developed three highly accurate energy consumption models.

Validated models with over 97% accuracy for diverse configurations.

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.