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Hybrid GA-PSO Optimization for Controller Placement in Large-Scale Smart City IoT Networks.

Sheeraz Ali Memon1, Darius Andriukaitis1, Dangirutis Navikas1

  • 1Department of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentu g. 50-438, LT-51368 Kaunas, Lithuania.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

This study introduces a hybrid optimization algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for efficient controller placement in smart city networks. The hybrid approach significantly enhances network performance, load balancing, and redundancy.

Keywords:
GA-PSONB-IoTcontroller placementhybrid optimizationsmart city

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Area of Science:

  • Computer Science
  • Network Engineering
  • Smart City Technology

Background:

  • The Internet of Things (IoT) is integral to smart city development.
  • Optimal controller placement is critical for IoT network performance and resilience.
  • Existing placement strategies may not adequately address the complexities of large-scale smart city networks.

Purpose of the Study:

  • To propose a hybrid optimization approach for strategic controller placement in IoT networks.
  • To enhance network performance metrics including latency, load balancing, energy efficiency, and redundancy.
  • To validate the proposed algorithm in a real-world smart city model.

Main Methods:

  • A hybrid optimization algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) was developed.
  • A large-scale Narrowband Internet of Things (NB-IoT) network simulation with 2000 nodes was conducted.
  • Controller placement was optimized to minimize latency and balance load, focusing on energy efficiency and redundancy.

Main Results:

  • The hybrid GA-PSO algorithm demonstrated superior performance compared to random and K-Means clustering placements.
  • Significant improvements were observed in load balancing, reduced packet loss, and enhanced energy efficiency.
  • The algorithm proved robust and effective across normal operation, node failure, and traffic spike scenarios.

Conclusions:

  • The proposed hybrid GA-PSO algorithm offers an effective solution for optimizing controller placement in smart city IoT networks.
  • This approach enhances network resilience, scalability, and overall performance.
  • The findings underscore the algorithm's potential for practical implementation in smart city infrastructure.