Calibrating Low-cost Environmental Sensors Using Optimised Artificial Neural Networks

Low-cost sensors play a vital role in diverse applications, yet their accuracy limitations hinder widespread adoption. To address this problem, this study proposes a calibration technique using Artificial Neural Networks (ANNs) optimised with metaheuristic algorithms. We apply five well-known and widely used metaheuristic algorithms: Particle Swarm Optimisation (PSO), Harris Hawk Optimisation (HHO), Driving Training Based Optimisation (DTBO), Squirrel Search Optimisation (SSO) and Whale Optimisation Algorithm (WOA). These algorithms are used to tune the hyperparameters of an ANN to improve sensor accuracy via calibration. In this study, data collection was conducted alongside corresponding ground truth, resulting in a comprehensive dataset suitable for various applications such as environmental monitoring and sensor calibration. A comparative analysis among the algorithms revealed that WOA consistently outperformed the other metaheuristic optimisation techniques with the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This study provides valuable insights into combining metaheuristic optimisation techniques with ANNs for sensor calibration, potentially enhancing the development of more resilient and precise sensor technologies in the future.