Data Collection and Preprocessing for Environmental Monitoring Using Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) play a pivotal role in environmental monitoring, providing real-time data for assessing and managing natural resources. However, the data collected from these networks often need careful handling due to challenges such as noise, calibration issues, and missing data. Techniques for data acquisition in WSNs, methods for preprocessing to reduce noise and calibrate readings, and strategies to treat missing data and to ensure the quality of environmental monitoring data analysis. Missing data presents a prevalent challenge in datasets. Data generation gaps can arise due to sensor damage, power loss etc. Logical imputation of data representing a crucial pre-processing step for sequential data-driven modeling endeavors. We scrutinize methodologies aimed at enhancing the precision of data imputation models tailored for variate time-series sensor data afflicted by significant data gaps