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

2025-05-28

AI Summary

The proliferation of IoT devices renders battery-powered operation increasingly unsustainable, necessitating reproducible and attributable energy-efficiency evaluation methodologies for batteryless systems. This paper introduces EStacker, an energy-aware evaluation platform that addresses this challenge through three key contributions: (1) a novel “energy stacks” model enabling fine-grained energy attribution, precisely decomposing application-level energy consumption across hardware components and task-level activities; (2) a time-power joint scaling (ST-SP) optimization technique that accelerates evaluation by 6.3× while preserving temporal behavior fidelity; and (3) integrated energy-aware runtime monitoring and reproducible energy-environment simulation to ensure fair, controlled, and repeatable assessment. Experimental evaluation demonstrates that EStacker reduces the design-space exploration cycle for an intelligent parking system from 41.7 days to 7.7 days and improves performance bottleneck identification efficiency by 3.3×.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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).

Problem

• Evaluating battery-less IoT systems' performance under varied energy conditions
• Providing detailed energy consumption breakdown across hardware and activities
• Optimizing evaluation time while maintaining system behavior accuracy

Innovation

• Provides fair and repeatable energy evaluation
• Generates detailed energy consumption breakdowns
• Optimizes evaluation time with ST-SP scaling