Hybrid GA-PSO Optimization for Controller Placement in Large-Scale Smart City IoT Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259345" target="_blank" >RIV/61989100:27240/25:10259345 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/25/23/7119" target="_blank" >https://www.mdpi.com/1424-8220/25/23/7119</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s25237119" target="_blank" >10.3390/s25237119</a>
Alternative languages
Result language
angličtina
Original language name
Hybrid GA-PSO Optimization for Controller Placement in Large-Scale Smart City IoT Networks
Original language description
The Internet of Things (IoT) plays an important role in the development of smart cities. IoT forms a large network, and optimal controller placement plays a crucial role in ensuring network performance and resilience. This paper proposes a hybrid optimization approach that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to strategically place controllers. Kaunas (Lithuania) was selected as a real-world smart city model. A large-scale Narrowband Internet of Things (NB-IoT) network with 2000 nodes was simulated, and 10 controllers were optimally placed in the network to minimize latency, balance load, enhance energy efficiency, and redundancy. The performance of the proposed hybrid GA-PSO algorithm was compared with random and K-Means clustering placements under three scenarios: normal operation, node failures, and traffic spikes. Simulation results demonstrate that the hybrid approach outperforms the other two methods in terms of load balancing, packet loss, energy efficiency, scalability, and redundancy. These findings highlight the robustness and effectiveness of the proposed hybrid algorithm in optimizing controller placement for smart city environments. © 2025 by the authors.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sensors
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
25
Issue of the periodical within the volume
23
Country of publishing house
CH - SWITZERLAND
Number of pages
25
Pages from-to
1-25
UT code for WoS article
001635481200001
EID of the result in the Scopus database
2-s2.0-105024644437