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

TL;DR

EStacker is a platform that provides fair, repeatable evaluation of battery-less IoT systems by generating energy stacks, and it includes an optimization method that significantly reduces evaluation time while maintaining accuracy.

Contribution

The paper introduces EStacker, an evaluation platform for energy harvesting IoT systems, and ST-SP, an optimization technique that accelerates evaluation without losing fidelity.

Findings

EStacker enables fair, repeatable energy consumption analysis.

ST-SP reduces evaluation time by 6.3x with minimal error.

Energy stacks help identify performance bottlenecks.

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).