  Internet of Things (IoT) and related applications have successfully
contributed towards enhancing the value of life in this planet. The advanced
wireless sensor networks and its revolutionary computational capabilities have
enabled various IoT applications become the next frontier, touching almost all
domains of life. With this enormous progress, energy optimization has also
become a primary concern with the need to attend to green technologies. The
present study focuses on the predictions pertinent to the sustainability of
battery life in IoT frameworks in the marine environment. The data used is a
publicly available dataset collected from the Chicago district beach water.
Firstly, the missing values in the data are replaced with the attribute mean.
Later, one-hot encoding technique is applied for achieving data homogeneity
followed by the standard scalar technique to normalize the data. Then, rough
set theory is used for feature extraction, and the resultant data is fed into a
Deep Neural Network (DNN) model for the optimized prediction results. The
proposed model is then compared with the state of the art machine learning
models and the results justify its superiority on the basis of performance
metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared
Error, and Test Variance Score.

Machine Learning;Signal Processing