Cloud processing reduces on-device power use but increases wireless transmission needs, which can drain batteries quickly. Edge processing saves data transmission power by analyzing data locally, but requires more capable (and slightly more power-hungry) processors or more awake time. The decision where to process data is often driven by other factors as well, such as the likely frequency of updating the associated algorithms. The best approach often involves a balance of the two approaches, based on the specific application.

Higher data quality often requires faster sampling, higher resolution, lower noise, and more frequent transmissions—all of which consume more power. To conserve energy, the first step is determining what the quality requirements truly are. The data must be good enough to enable the product to accomplish its current and future clinical goals, but superfluous quality only consumes power for no end benefit.

Optimization involves selecting the right sampling rate, minimizing sensor activation time, and balancing analog and signal processing to achieve the necessary signal quality—not the maximum possible signal quality. For wearable or implantable sensors, the goal is to capture meaningful data while keeping energy consumption minimal.