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An accurate and reliable technique for predicting Remaining Useful Life (RUL)for battery cells proves helpful in battery-operated IoT devices, especially inremotely operated sensor nodes. Data-driven methods have proved to be the mosteffective methods until now. These IoT devices have low computationalcapabilities to save costs, but Data-Driven battery health techniques oftenrequire a comparatively large amount of computational power to predict SOH andRUL due to most methods being feature-heavy. This issue calls for ways topredict RUL with the least amount of calculations and memory. This paperproposes an effective and novel peak extraction method to reduce computationand memory needs and provide accurate prediction methods using the least numberof features while performing all calculations on-board. The model canself-sustain, requires minimal external interference, and hence operateremotely much longer. Experimental results prove the accuracy and reliabilityof this method. The Absolute Error (AE), Relative error (RE), and Root MeanSquare Error (RMSE) are calculated to compare effectiveness. The training ofthe GPR model takes less than 2 seconds, and the correlation between SOH frompeak extraction and RUL is 0.97.

iThing: Designing Next-Generation Things with Battery Health Self-Monitoring Capabilities for Sustainable IoT in Smart Cities