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Batteryless image sensors present an opportunity for long-life, long-range sensor deployments that require zero maintenance, and have low cost. Such deployments are critical for enabling remote sensing applications, e.g., instrumenting national highways, where individual devices are deployed far (kms away) from supporting infrastructure. In this work, we develop and characterize Camaroptera, the first batteryless image-sensing platform to combine energy-harvesting with active, long-range (LoRa) communication. We also equip Camaroptera with a Machine Learning-based processing pipeline to mitigate costly, long-distance communication of image data. This processing pipeline filters out uninteresting images and only transmits the images interesting to the application. We show that compared to running a traditional Sense-and-Send workload, Camaroptera’s Local Inference pipeline captures and sends upto \( 12\times \) more images of interest to an application. By performing Local Inference , Camaroptera also sends upto \( 6.5\times \) fewer uninteresting images, instead using that energy to capture upto \( 14.7\times \) more new images, increasing its sensing effectiveness and availability. We fully prototype the Camaroptera hardware platform in a compact, 2 cm \( \times \) 3 cm \( \times \) 5 cm volume. Our evaluation demonstrates the viability of a batteryless, remote, visual-sensing platform in a small package that collects and usefully processes acquired data and transmits it over long distances (kms), while being deployed for multiple decades with zero maintenance. 

Intermittently-powered, energy-harvesting devices operate on energy collected from their environment and must operate intermittently as energy is available. Runtime systems for such devices often rely on checkpoints or redo-logs to save execution state between power cycles, causing arbitrary code regions to re-execute on reboot. Anynon-idempotentprogram behavior—behavior that can change on each execution—can lead to incorrect results. This work investigates non-idempotent behavior caused by repeating I/O operations, not addressed by prior work. If such operations affect a control statement or address of a memory update, they can cause programs to take different paths or write to different memory locations on re-executions, resulting in inconsistent memory states. We provide the first characterization of input-dependent idempotence bugs and develop IBIS-S, a program analysis tool for detecting such bugs at compile time, and IBIS-D, a dynamic information flow tracker to detect bugs at runtime. These tools use taint propagation to determine the reach of input. IBIS-S searches for code patterns leading to inconsistent memory updates, while IBIS-D detects concrete memory inconsistencies. We evaluate IBIS on embedded system drivers and applications. IBIS can detect I/O-dependent idempotence bugs, giving few (IBIS-S) or no (IBIS-D) false positives and providing actionable bug reports. These bugs are common in sensor-driven applications and are not fixed by existing intermittent systems.