Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation

2026-06-23

AI Summary

This study addresses the challenge of dynamic scheduling in batteryless IoT systems, where traditional approaches relying on static thresholds or hardware-specific models struggle under highly variable energy availability and workload dynamics. The work proposes two hardware-agnostic, dynamic scheduling strategies that operate without prior knowledge of energy consumption: a model-free reinforcement learning (RL) agent and an online approximate prediction (AP) method, both treating applications as black boxes. To the best of our knowledge, this is the first demonstration of black-box dynamic scheduling in batteryless IoT that is independent of hardware characteristics, systematically uncovering the trade-offs among task throughput, node survivability, and execution pacing. Experimental results show that AP closely approaches oracle-level performance, RL flexibly balances energy usage and survival rate, and adaptive task-rate control (AsTAR) excels under prolonged energy outages, while devices with large capacitors can still benefit from static strategies.

Abstract

In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy. As edge applications grow in complexity, traditional energy-aware schedulers struggle with unpredictable workloads due to their reliance on static execution thresholds or pre-measured, hardware-specific task profiles. To overcome this, we propose two novel, hardware-agnostic dynamic scheduling strategies treating applications as a "black box," requiring no prior energy information: a model-free Reinforcement Learning (RL) agent and an on-the-fly Approximated Prediction (AP) method. We evaluate these methods against an adaptive task rate approach (AsTAR) and optimized static thresholds using a custom-built, physically accurate simulation framework driven by real-world solar data and dynamic LoRa transmission profiles. Rather than claiming universal superiority, our analysis exposes the distinct operational trade-offs of each method: the AP approach delivers lightweight, near-oracle task throughput; the RL agent provides tunable survival-execution balancing; and AsTAR excels at execution pacing across long energy gaps. Finally, we demonstrate that while these advanced strategies provide critical resilience for severely constrained systems with small capacitors, devices with larger energy buffers can efficiently rely on simpler, less computationally expensive static policies.

Problem

• batteryless IoT
• unknown workloads
• energy-harvesting
• task scheduling
• dynamic execution

Innovation

• hardware-agnostic scheduling
• batteryless IoT
• reinforcement learning
• approximated prediction
• energy harvesting