Task-Specific Energy Profiling for Microcontroller Selection in Energy-Autonomous Wireless Sensor Nodes

The vigilant monitoring of climate change, especially through advanced environmental sensing and the deployment of distributed sensor systems, establishes a crucial link to the well-being of forest ecosystems. To ensure long term deployment and maintenance-free operation, such sensor systems need to be energy-autonomous, for example, be powered by harvested solar energy. However, given the complexity of the data processing required at the sensor node, the incoming harvested energy is limited with respect to the energy demands. Additionally, the size and the weight of the solar cells is further reduced from the need to have compact sensor systems, which do not interfere with the ecological processes. Under these circumstances, the electronics are forced to consume as low energy as possible and thus, selecting an ultra-low-power microcontroller, as the WSN’s central building block, that effectively balances minimal energy consumption and computational capabilities becomes critical to ensure reliable, autonomous operation. However, existing literature lacks practical methodologies for selecting ultra-low-power microcontrollers based on realistic, task-specific workloads. To address this gap, we introduce a novel task-specific profiling methodology tailored specifically for evaluating ultra-low-power microcontrollers in environmental WSNs. Two representative microcontrollers—the Ambiq Apollo 3 (ARM Cortex M4F-based) and Texas Instruments’ MSP430FR5994—were rigorously assessed in sensor data acquisition, intensive mathematical computations, memory operations, and LoRa-based wireless communication. Unlike conventional datasheet-based evaluations, our method offers empirical, workload-specific insights and enables the derivation of additional metrics such as energy per CPU cycle, which developers can leverage to make application-aware optimization decisions. Our results show clear trade-offs: MSP430FR5994 excels in integrated memory tasks, while Apollo 3 significantly outperforms in sensing and wireless communication, reducing total active-mode energy consumption by around 19.84%. Moreover, Apollo 3 achieved approximately 62% lower deep-sleep energy consumption when paired with an external ultra-low-power real-time clock (RTC). Overall, this study not only identifies the Apollo 3 as the optimal choice for our specific forest-based environmental monitoring case study but also introduces a platform-agnostic profiling framework. As this methodology quantifies energy per task and even energy per CPU cycle, it can be readily applied to diverse deployment contexts, from urban air quality monitoring to agricultural applications, and can be applied to a wide range of microcontroller architectures, including ARM Cortex, MSP430, and emerging RISC-V platforms. However, while the methodology is broadly applicable, it is not intended as a universal design methodology. Instead, it serves as a practical decision-support framework that complements system-level optimization strategies and enables developers to make energy-aware design choices in resource-constrained environments.