Building and programming tiny battery-free energy harvesting embedded computer systems is hard for the average maker because of the lack of tools, hard to comprehend programming models, and frequent power failures. With the high ecologic cost of equipping the next trillion embedded devices with batteries, it is critical to equip the makers, hobbyists, and novice embedded systems programmers with easy-To-use tools supporting battery-free energy harvesting application development. This way, makers can create untethered embedded systems that are not plugged into the wall, the desktop, or even a battery, providing numerous new applications and allowing for a more sustainable vision of ubiquitous computing. In this paper, we present BFree, a system that makes it possible for makers, hobbyists, and novice embedded programmers to develop battery-free applications using Python programming language and widely available hobbyist maker platforms. BFree provides energy harvesting hardware and a power failure resilient version of Python, with durable libraries that enable common coding practice and off the shelf sensors. We develop demonstration applications, benchmark BFree against battery-powered approaches, and evaluate our system in a user study. This work enables makers to engage with a future of ubiquitous computing that is useful, long-Term, and environmentally responsible.

Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala's specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces runtime by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail.