Multisensor Data Fusion Technique For Environmental Awareness In Wireless Sensor Networks

Abstract

Sensing in forest area is the most widely used application concerning investigation studies of climate change. Wireless sensor networks with spatially scattered sensors enable the application to record climate change disturbance detecting condition changes in temperature, humidity, sound, wind, etc. The highly automated method herein can pass the observed information bi-directionally to the system sink, empowering sensor activity control. It is common knowledge that a multisensor environment has hundreds or thousands of sensor nodes connected. With recent innovations, the remarkable challenge faced in a multisensor climate is to rapidly acquire specific information from a reliable route exhibiting high data accuracy. This proposed ADKF-DT-MF algorithm for multisensor data fusion combines sensor information in-continuous time, providing a rapid information exchange on climate change for environmental awareness. The findings significantly show a better RMSE of 0.85 than the previous results reported in the literature MHT-EnKF. The quality of estimation was explored, calculating the best costs, ensuring an increase in data fusion accuracy for active awareness. The tests on simulated data applying fuzzy membership optimization function show improvement in the ADKF-DT-MF multisensor fusion system's performance.