Title Hybrid metaheuristic approach for multi objective controller placement for Smart City IoT networks
Authors Memon, Sheeraz Ali ; Andriukaitis, Darius ; Grimaila, Vitas ; Sledevič, Tomyslav ; Markevičius, Vytautas ; Žilys, Mindaugas ; Valinevičius, Algimantas ; Navikas, Dangirutis ; Konecny, Jaromir ; Prauzek, Michal
DOI 10.1016/j.aeue.2026.156522
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Is Part of AEU - International journal of electronics and communications.. Munich : Elsevier. 2026, vol. 216, art. no. 156522, p. 1-29.. ISSN 1434-8411. eISSN 1618-0399
Keywords [eng] Internet of Things (IoT) smart city networks ; multi-objective optimization Controller Placement Problem (CPP) ; particle swarm optimization and genetic algorithm
Abstract [eng] The rapid growth of the Internet of Things (IoT) in smart city environments has introduced significant challenges in controller placement. In Software Defined Networking (SDN) based IoT architectures, controller placement plays a critical role in overall network performance, as it directly influences scalability, latency, reliability, energy efficiency, and load balancing, making optimal controller deployment an important research problem. This paper proposes a realistic, multi-objective controller placement framework for large-scale smart city IoT networks. To improve practical relevance, the model incorporates real geographic data from Kaunas city (Lithuania), including city boundaries, building footprints, and water regions, enabling realistic urban deployment analysis. The proposed hybrid PSO-GA algorithm combines the global search capability of genetic techniques with the fast convergence characteristics of swarm intelligence to identify efficient controller placements. The performance of the proposed method is compared with random placement, K-Means, PSO, GA, and APDQN (Affinity Propagation Deep Q-network) based placement methods. The results demonstrate that the proposed method achieves a better balance among coverage efficiency, reliability, energy utilization, load balancing, and network stability. These findings indicate that the proposed method provides a scalable, and robust solution for large-scale smart city IoT controller placement optimization.
Published Munich : Elsevier
Type Journal article
Language English
Publication date 2026
CC license CC license description