Accurate data aggregation created by neural network and data classification processed through machine learning in wireless sensor networks

Sudha, C.; Suresh, D.; Nagesh, Ajay

doi:10.1016/j.tcs.2022.04.020

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…The topic of data aggregation in WSNs is covered by Sudha et al [20], who highlight that sensor data should be corrected before aggregation to avoid errors and noise. This study proposes the Accurate Data Aggregation framework.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

Alkanhel,

Khafaga,

Zaki

et al. 2024

CMC

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The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor's specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

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Section: Introductionmentioning

confidence: 99%