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Hauptverfasser: Mudraje, Ishwar, Vogelgesang, Kai, Devreker, Jasper, Gerhorst, Luis, Raffeck, Phillip, Wägemann, Peter, Herfet, Thorsten
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2501.17684
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author Mudraje, Ishwar
Vogelgesang, Kai
Devreker, Jasper
Gerhorst, Luis
Raffeck, Phillip
Wägemann, Peter
Herfet, Thorsten
author_facet Mudraje, Ishwar
Vogelgesang, Kai
Devreker, Jasper
Gerhorst, Luis
Raffeck, Phillip
Wägemann, Peter
Herfet, Thorsten
contents The Internet of Batteryless Things revolutionizes sustainable communication as it operates on harvested energy. This harvested energy is dependent on unpredictable environmental conditions; therefore, device operations, including those of its networking stack, must be resilient to power failures. Reactive intermittent computing provides an approach for solving this by notifications of impending power failures, which is implemented by monitoring the harvested energy buffered in a capacitor. However, to use this power-failure notification and guarantee forward progress, systems must break down tasks into atomic transactions that can be predictably finished before the energy runs out. Thus, static program-code analysis must determine the worst-case energy consumption (WCEC) of all transactions. In Wi-Fi-capable devices, drivers are often closed-source, which avoids the determination of WCEC bounds for transactions since static analysis requires all code along with its semantics. In this work, we integrate an energy-aware networking stack with reverse-engineered Wi-Fi drivers to enable full-stack WCEC analysis for physical transmission and reception of packets. Further, we extended a static worst-case analysis tool with a resource-consumption model of our Wi-Fi driver. Our evaluation with the RISC-V-based ESP32-C3 platform gives worst-case bounds with our static analysis approach for the transactions of the full communication stack, therefore showing that Wi-Fi-based reactive intermittent computing is feasible.
format Preprint
id arxiv_https___arxiv_org_abs_2501_17684
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Reverse Engineering the ESP32-C3 Wi-Fi Drivers for Static Worst-Case Analysis of Intermittently-Powered Systems
Mudraje, Ishwar
Vogelgesang, Kai
Devreker, Jasper
Gerhorst, Luis
Raffeck, Phillip
Wägemann, Peter
Herfet, Thorsten
Networking and Internet Architecture
The Internet of Batteryless Things revolutionizes sustainable communication as it operates on harvested energy. This harvested energy is dependent on unpredictable environmental conditions; therefore, device operations, including those of its networking stack, must be resilient to power failures. Reactive intermittent computing provides an approach for solving this by notifications of impending power failures, which is implemented by monitoring the harvested energy buffered in a capacitor. However, to use this power-failure notification and guarantee forward progress, systems must break down tasks into atomic transactions that can be predictably finished before the energy runs out. Thus, static program-code analysis must determine the worst-case energy consumption (WCEC) of all transactions. In Wi-Fi-capable devices, drivers are often closed-source, which avoids the determination of WCEC bounds for transactions since static analysis requires all code along with its semantics. In this work, we integrate an energy-aware networking stack with reverse-engineered Wi-Fi drivers to enable full-stack WCEC analysis for physical transmission and reception of packets. Further, we extended a static worst-case analysis tool with a resource-consumption model of our Wi-Fi driver. Our evaluation with the RISC-V-based ESP32-C3 platform gives worst-case bounds with our static analysis approach for the transactions of the full communication stack, therefore showing that Wi-Fi-based reactive intermittent computing is feasible.
title Reverse Engineering the ESP32-C3 Wi-Fi Drivers for Static Worst-Case Analysis of Intermittently-Powered Systems
topic Networking and Internet Architecture
url https://arxiv.org/abs/2501.17684