CoFEE: Context‐aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Abstract

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth.Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs).It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internetconnected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.

Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques

“…In order to analyze the accuracy of the prediction results of the MLR and MLP algorithms, the precision rate is calculated. The prediction precision rate is the percentage of the root of the squared difference rate between the predicted and observed results calculated using the Euclidean distance 38 . It is given in Equations (2), (3) as, where, EUC Dis denotes the Euclidean distance and EUC max represents the maximum Euclidean distance among predicted and observed serum sodium results 38 .…”

“…The prediction precision rate is the percentage of the root of the squared difference rate between the predicted and observed results calculated using the Euclidean distance. 38 It is given in Equations ( 2), (3) as, . Such that the proposed MLP algorithm has 27%-50% of higher precision rate on predicting the future sodium range of the patients.…”

“…EUC Dis denotes the Euclidean distance and EUC max represents the maximum Euclidean distance among predicted and observed serum sodium results 38. Table8gives the accuracy of the prediction results using precision rate analysis.…”

Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model

“…To estimate the stationary of the predicted dataset, it assessed with the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Correlated Akaike Information criterion (AICc) metrics [18].…”

Routing Protocols for the Aircraft AD HOC Networks

“…The propagation parameters are calculated and configured in the file named threshold.cc from the propagation folder of the ns 2.25 directory. The transmission range parameters such as capture threshold (CPThresh_), carrier sense threshold (CSThresh_), receiver sensitivity threshold (RXThresh_), bandwidth (Rb_), transmission power (Pt_) values are computed for the AANET environment are they updated in the corresponding files of the NS2 [14,21].…”