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Data Collection by Observations

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative

Li Cao¹, Yinggao Yue^1,2, Yong Zhang²

¹School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

Computational Intelligence and Neuroscience

|October 11, 2021

Summary

This study introduces an improved sparrow search algorithm-optimized self-organizing maps (ISSA-SOM) for selecting cluster heads in heterogeneous wireless sensor networks (HWSNs). The new method balances energy consumption, significantly extending network lifetime.

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Clustering routing protocols are crucial for extending wireless sensor network (WSN) lifetime.
Current cluster head selection in heterogeneous WSNs often neglects node energy and distribution, causing imbalanced energy consumption.
This imbalance limits the overall operational duration of WSNs.

Purpose of the Study:

To propose an optimized cluster head selection strategy for heterogeneous wireless sensor networks (HWSNs).
To enhance the lifetime of WSNs by addressing energy consumption imbalances.
To improve the efficiency of clustering routing protocols in WSNs.

Main Methods:

Developed an Improved Sparrow Search Algorithm-optimized Self-Organizing Maps (ISSA-SOM) for cluster head selection.
Utilized a competitive neural network model at the base station for adaptive learning.
Input vectors for selection included remaining node energy, distance to base station, and neighbor node count.

Main Results:

The ISSA-SOM strategy effectively balances energy consumption across HWSNs.
Simulation experiments demonstrated reduced network energy consumption compared to basic competitive neural networks.
The proposed method significantly prolongs the operational lifetime of sensor networks.

Conclusions:

The ISSA-SOM approach offers a superior method for cluster head selection in HWSNs.
This strategy effectively mitigates energy imbalance issues, leading to extended network longevity.
Optimized cluster head selection is vital for maximizing the lifespan of wireless sensor networks.