Roughsets-based Approach for Predicting Battery Life in IoT

Abstract

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.

Forecasting renewable energy for environmental resilience through computational intelligence

“…A hybrid approach combined with computational intelligence can be more effective and widely studied in energy and power forecasting. For instance, Kaluri et al [23] used predictive power of rough sets to forecast battery power life and, Maddikunta el al [24] used hybrid algorithmic strategy for efficient power communication between networks. A hybrid computational intelligence technique can learn effective model features from wind direction and wind speed adjacent to wind turbines before actual wind power forecasting [25].…”

“…Each node in the neural network except input nodes is a neuron that utilizes a non-linear activation function. Several levels, including non-linear stimulus, discriminate against the Hidden layer by a standard DAC classifier to differentiate non-linearly separable data [23,49].…”

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

“…Nonetheless, prediction of financial status is considered a crucial problem for SMEs. For huge enterprises, certified audited financial statement is utilized for evaluating the credit threat and assist financial decision making like permitting loans [4].…”

Source Routing for Distributed Big Data‐Based Cognitive Internet of Things (CIoT)

“…In such networks, a wide range of areas are monitored by thousands of smart sensors where assembled data are sent to the sink node. Unfortunately, IoT impose many challenges compared to other types of networks [11][12][13]. Data management is a mostly demanding task for IoT due to the huge amounts of data gathered in such networks.…”