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Investigation of Parameter Effects on Virtual-Spring-Force Algorithm for Wireless-Sensor-Network Applications.

Zhiyong Yu¹, Rongxin Tang^2,3, Kai Yuan¹

¹Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China.

Sensors (Basel, Switzerland)

|July 25, 2019

Summary

This summary is machine-generated.

Virtual-force algorithms (VFAs) optimize wireless sensor network (WSN) node deployment. Parameter tuning enhances coverage and reduces energy consumption, especially with obstacles.

Keywords:

pair correlation diversion parameter analysis virtual spring force wireless sensor network

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Virtual-force algorithms (VFAs) are crucial for efficient node deployment in wireless sensor networks (WSNs).
Previous work introduced a virtual spring force algorithm (VFA-SF) and an optimized version (VFA-SF-OPT) to improve network distribution and eliminate coverage gaps.

Purpose of the Study:

To investigate the impact of key parameters on the performance of VFA-SF and VFA-SF-OPT.
To analyze how node velocity, external central force, and obstacles affect WSN deployment and energy efficiency.

Main Methods:

Statistical analysis of VFA-SF and VFA-SF-OPT performance.
Simulation and evaluation of parameter effects on node deployment convergence and network coverage.
Assessment of algorithm behavior in the presence of various obstacle types.

Main Results:

Node velocity significantly influences the convergence rate of the deployment process.
An optimized external central force improves node equilibrium distance and reduces overall energy consumption.
Both VFA-SF and VFA-SF-OPT demonstrate effectiveness in handling different obstacle configurations.

Conclusions:

Careful parameter selection is vital for optimizing node deployment and energy efficiency in large-scale WSNs.
VFA-SF-OPT offers enhanced network distribution by mitigating coverage holes and twisted structures.
The findings provide critical insights for parameter tuning and information fusion in WSN applications.