EE-SAMS: An adaptive, SNN based energy-efficient data aggregation framework for agrovoltaic monitoring systems - Blessina Preethi R; Berin Shalu S; Saranya Nair M; Vergin Raja Sarobin M (Results in Engineering)
The emerging trend of agrovoltaic farming monitoring system aims to the reduce energy depletion in battery constrained wireless sensor network. The proposed EE-SAMS model introduces an energy-efficient approach to reduce sensor node energy depletion caused by redundant data transmissions in agrovoltaic farming systems. At the node level, redundancy is minimized using a Euclidean distance-based threshold, forwarding only non-redundant data to the aggregator. At the aggregator level, feature extraction and data classification are conducted through Conv1D and MaxPooling layers, with classification powered by a modified Spiking Neural Network (SNN) using the Adaptive Exponential Integrate-and-Fire (AdEx) model, achieving a high classification accuracy of 99.27%. Selective forwarding further enhances energy efficiency by transmitting only prioritized, non-redundant data to the base station. The performance of EE-SAMS is compared with the MLEM, MLELMAKF, SOF-SVM, and MRMR-KNN models in terms of efficiency and accuracy. The proposed model outperforming the other models and proven that the EE-SAMS is highly suitable for sustainable agrovoltaic monitoring.