Intelligent Sensing in Orbit: A Multi-Stage Framework for Energy-Aware Adaptive Sampling in CubeSat Networks

Severe power constraints present a primary obstacle to extending the mission lifetime and enhancing the scientific return of small satellites. Conventional fixed-rate sampling protocols exacerbate this issue, creating an untenable trade-off between data fidelity and power conservation. This paper introduces the Intelligent Energy-Aware Sampling (IEAS-SSN) framework, a novel solution designed to overcome this limitation. The proposed framework utilizes a five-stage decision pipeline that, unlike conventional methods, co-optimizes the sampling rate by holistically integrating real-time signal dynamics with the system’s available energy state and operational mode. This multi-objective approach enables an intelligent balance between performance and power preservation. Validation through simulation demonstrates that the IEAS-SSN framework reduces energy consumption by 25-33% against a fixed-rate baseline. Crucially, this efficiency gain is achieved with negligible impact on data quality, maintaining a root-mean-square error (RMSE) below 0.05 and a signal correlation coefficient exceeding 0.95. The IEAS-SSN framework thus represents a significant advancement for power-constrained autonomous systems, offering a practical methodology to extend the operational envelope of small satellite missions without sacrificing scientific data integrity.