nCare: Fault-aware edge intelligence for rendering viable sensor nodes

In remote sensor nodes, energy harvesters are used with rechargeable batteries as the power source. As these batteries undergo capacitance reduction, they degrade with each charge–discharge cycle. Most of the existing researches do not consider edge-based computation for battery health prediction. This paper proposes a smart, accurate, and reliable data-driven architecture, nCare, to predict the battery’s health at low power and low computation costs. Three models are used for health prediction, two Gaussian Process Regression (GPR) models deployed at the sensor node — one to predict the State of Health (SoH) of the battery for the first few cycles and the second to predict the Remaining Useful Life (RUL) of battery. An AutoKeras model deployed at the gateway where it will be trained using the first 200 cycles. Then weight matrix and layer information will be sent to sensor nodes to replicate the model without training to predict SoH after 200 cycles. Two main parameters, battery temperature and elapsed time (ET), are used to predict the SoH, which is further used for RUL prediction. Furthermore, to prove the efficiency of the RUL prediction method, the calculated Absolute Error (AE), Relative Mean Square Error (RMSE), and Relative error (RE) are 0.019, 0.142, and 0.005, respectively for a battery of 730 cycles. This method helps the sensor node acquire intelligence that will be battery specific. Blockchain is used for maintaining the data integrity of the architecture. nCare thus ensures an effective and accurate solution for predicting the RUL of a battery.