Energy consumption minimisation at edge node using $$C_cBPS$$ approach in predicting sensor parameters in WSNs

Owing to limited storage and battery power, wireless sensor nodes often face challenges in maintaining long-term energy sustainability. To address this, only a subset of sensors remains active to monitor different sensor parameters while others get predicted to minimize sensor node energy consumption. In prediction, not all active parameters are equally important, as low-correlated parameters increase computational complexity and decrease accuracy. Researchers use highly correlated active parameters, though existing solutions often use polynomial time and don't ensure optimal parameter set. This paper proposes a cross-correlation-based parameter selection [Formula: see text] approach, ensuring the selected parameter set is stable and Pareto-optimal. Simulations are performed on nine publicly available datasets of environmental data collected from different places and at different sampling intervals to validate the effectiveness of the [Formula: see text] approach. It has been observed that [Formula: see text] approach selects a subset of active parameters faster than existing approaches and reduces energy consumption at the edge node ranges from [Formula: see text] - [Formula: see text] in the prediction of sleep sensor parameters on various datasets.