AERO: Adaptive and Efficient Runtime-Aware OTA Updates for Energy-Harvesting IoT

2026-01-23

AI Summary

This work addresses the challenge of reliably performing over-the-air (OTA) updates on energy-harvesting IoT devices, where intermittent power supply undermines update consistency under conventional approaches. To overcome this limitation, the authors propose a runtime-aware adaptive OTA mechanism that integrates update tasks into the application’s directed acyclic graph (DAG). By dynamically identifying affected execution regions and reconfiguring task dependencies, the method enables coordinated scheduling of regular application tasks and update operations under both energy and timing constraints—ensuring consistency without requiring system restarts. Combining energy-aware scheduling with restart-free update techniques, the approach significantly improves update reliability and energy efficiency under typical workloads, outperforming existing solutions.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Energy-harvesting (EH) Internet of Things (IoT) devices operate under intermittent energy availability, which disrupts task execution and makes energy-intensive over-the-air (OTA) updates particularly challenging. Conventional OTA update mechanisms rely on reboots and incur significant overhead, rendering them unsuitable for intermittently powered systems. Recent live OTA update techniques reduce reboot overhead but still lack mechanisms to ensure consistency when updates interact with runtime execution. This paper presents AERO, an Adaptive and Efficient Runtime-Aware OTA update mechanism that integrates update tasks into the device's Directed Acyclic Graph (DAG) and schedules them alongside routine tasks under energy and timing constraints. By identifying update-affected execution regions and dynamically adjusting dependencies, AERO ensures consistent up date integration while adapting to intermittent energy availability. Experiments on representative workloads demonstrate improved update reliability and efficiency compared to existing live update approaches.

Problem

• Energy-Harvesting IoT
• OTA Updates
• Intermittent Energy
• Runtime Consistency
• Update Reliability

Innovation

• Runtime-aware OTA
• Energy-harvesting IoT
• Intermittent computing
• Consistency-preserving update
• Task DAG scheduling