Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation
arXiv:2606.24340v1 Announce Type: new 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 treatin
The proliferation of IoT devices and increasing demand for edge computing drive the need for more efficient and autonomous power management solutions, especially in batteryless systems.
Reliable operation of batteryless IoT devices is crucial for scaling the IoT paradigm and expanding its applications in remote or maintenance-free environments.
This research introduces adaptive scheduling methods that transcend hardware-specific limitations, making batteryless IoT deployments more robust and capable of handling complex, variable workloads.
- · IoT device manufacturers
- · Smart infrastructure developers
- · AI at the edge sector
- · Energy harvesting technology providers
- · Manufacturers of traditional battery-dependent IoT devices
- · Legacy energy harvesting system integrators using static schedulers
Increased reliability and lifespan for batteryless IoT devices in diverse environments.
Expansion of IoT applications into new domains where power access is intermittent or maintenance is impractical.
Acceleration of edge AI capabilities by enabling more complex computations on constrained, self-sustaining devices.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG