A Normalized Cosine Distance Based Regression Model for Data Prediction in WSN

Agarwal, Arun; Dev, Amita

doi:10.1007/978-981-13-1813-9_30

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…However, the time series created by SN amid the process of detecting ambient conditions is typically non-stationary and nonlinear, and linear predictors cannot accurately predict non-stationary and nonlinear time series [9][10]. Simple strategies are employed in several previous studies, such as [11,12], to create predictors for sensor networks to send data from the complete sensor array to the base station. Conversely, if there are considerable and constant changes in data values, the forecasting methods used in these works may need to be revised.…”

Section: Fig 1 Network Model Examplementioning

confidence: 99%

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

2023

JMC

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Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).

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Section: Fig 1 Network Model Examplementioning

confidence: 99%

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

2023

JMC

View full text Add to dashboard Cite

show abstract

A Normalized Cosine Distance Based Regression Model for Data Prediction in WSN

Cited by 1 publication

References 38 publications

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

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