Improvements in Environmental Monitoring in IoT Networks through Sensor Fusion Techniques

Abstract

With the Internet of Things (IoT), environmental monitoring has been 
revolutionized by the ability to deploy large sensor networks to collect 
real-time environmental data on numerous parameters. But managing data 
from these networks, consisting of an extraordinary amount and breadth 
of information, is both voluminous and unmanageable for anything from 
data integration to understanding and taking action in an efficient and 
timely manner. As these technologies become commonplace, sophisticated 
algorithms have advanced enough to be able to process and analyze huge 
amounts of heterogeneous data in real time, yielding useful knowledge for 
environmental management and decision making. The application of various 
sensor fusion techniques in environment monitoring IoT networks, their 
advantages, challenges, and future prospects are the basis of this article. 
In this work, we explore the various types of sensor fusion algorithms, apply 
them to certain environmental monitoring scenarios, and how machine 
learning can aid in augmenting fusion processes. In addition, we will expose 
implementation challenges and best practices of deploying sensor fusion 
systems in the real world environmental monitoring applications.