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Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective

Zhiyou Wang1,2, Maojin Wang1,2, Ying Chen1,2

  • 1School of Electronic Communication and Electrical Engineering, Changsha University, Kaifu District, Changsha, China.

Computational Intelligence and Neuroscience
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

This study optimized wireless sensor networks (WSN) using surface plasmon resonance imaging (SPRi) sensors. Multi-objective optimization achieved ideal performance by balancing sensor design and network parameters for practical deployment.

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Area of Science:

  • Photonics and Sensor Technology
  • Wireless Communication Systems
  • Optimization Algorithms

Background:

  • Surface Plasmon Resonance Imaging (SPRi) sensors offer high sensitivity for detecting refractive index changes.
  • Wireless Sensor Networks (WSN) require optimized node density and transmission reliability for efficient data collection.
  • Balancing sensor characteristics and network performance is crucial for effective WSN deployment.

Purpose of the Study:

  • To optimize the performance of a WSN utilizing plain silver SPRi sensors.
  • To establish models for sensor parameters like depth-width ratio (DWR) and penetration depth (PD).
  • To determine optimal WSN configurations balancing transmission success rate and information dimension.

Main Methods:

  • Developed refractive index-thickness models for gold and silver SPRi sensors.
  • Utilized Jones transfer matrix and Kriging interpolation to calculate DWR and PD.
  • Employed Multi-Objective Optimization Genetic Algorithm (MOGA) for simultaneous optimization of sensor and network parameters.
  • Calculated information dimension and transmission success rate for Pareto optimal solutions.

Main Results:

  • Optimized DWR and PD for silver and gold SPRi sensors.
  • Identified optimal number of nodes and transmission failure probability for WSN deployment.
  • Demonstrated the utility of Pareto optimal solutions for trade-off decisions in WSN design and deployment.

Conclusions:

  • The MOGA approach effectively optimizes SPRi sensor parameters and WSN configurations.
  • Pareto optimal solutions provide a framework for balancing competing performance objectives in WSNs.
  • This research facilitates informed decision-making for practical WSN deployment and sensor design.