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Updated: Mar 8, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

Shuqiang Huang1, Ming Tao2

  • 1Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China. hsq@jnu.edu.cn.

Sensors (Basel, Switzerland)
|January 25, 2017
PubMed
Summary
This summary is machine-generated.

A new Competitive Swarm Optimizer (CSO) algorithm enhances wireless sensor network gateway deployment for cyber-physical systems. CSO improves network lifetime and quality of service by optimizing gateway placement more effectively than traditional methods.

Keywords:
competitive swarm optimizer (CSO)covering radiuscyber-physical systemgateway deploymentgeometric K-centerparticle swarm optimization (PSO)

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Area of Science:

  • Computer Science
  • Network Engineering
  • Optimization Algorithms

Background:

  • Wireless sensor network (WSN) topology optimization is crucial for efficient data forwarding, energy conservation, and extended network lifespan.
  • Connecting WSNs to the Internet in cyber-physical systems (CPS) presents challenges in gateway deployment.
  • Existing algorithms like Particle Swarm Optimization (PSO) can get trapped in local optima for gateway deployment.

Purpose of the Study:

  • To propose a novel Competitive Swarm Optimizer (CSO) algorithm for geometric gateway deployment in WSNs.
  • To enhance the search mechanism and efficiency of swarm intelligence algorithms for WSN topology control.
  • To improve the quality of service (QoS) and network coverage in CPS by optimizing gateway placement.

Main Methods:

  • Developed a Competitive Swarm Optimizer (CSO) algorithm incorporating an inter-particle competition mechanism.
  • Integrated adaptive opposition-based search and dynamic parameter adjustments into the CSO algorithm.
  • Applied the CSO algorithm to solve the geometric K-center gateway deployment problem for WSNs.

Main Results:

  • The CSO algorithm demonstrated superior global exploration ability and convergence speed compared to PSO and Kmedoids.
  • CSO effectively avoided local optima, leading to more stable and robust gateway deployment.
  • Optimized gateway placement resulted in a minimum network coverage radius, enhancing network QoS in CPS.

Conclusions:

  • The proposed CSO algorithm is a stable, robust, and effective solution for geometric gateway deployment in WSNs.
  • CSO significantly improves upon traditional PSO and Kmedoids algorithms for this specific problem.
  • The optimized deployment enhances the overall performance and reliability of cyber-physical systems.