A Survey on Controller Placement Algorithms for IoT Networks in Smart City Environments

A Survey on Controller Placement Algorithms for IoT Networks in Smart City Environments

Paper in proceedings (conference paper)

The Internet of Things (IoT) is transforming cities into smart cities by improving efficiency, safety, and sustainability through real-time data collection, analysis and decision making. A main challenge in IoT network is the Controller Placement Problem (CPP), which is essential for minimize latency, balance load, ensure reliability, and optimize energy consumption. This paper reviews 10 algorithms and approaches that have been proposed to solve CPP. To solve the CPP, algorithms minimize latency, improve fault tolerance, balance load, enhance energy efficiency, and ensure scalability. Various algorithms have been proposed to tackle these challenges, each algorithm has unique advantages. Among these algorithms the Hybrid Differential Evolution and Whale Optimization (DEWO) algorithm minimizes latency, improves fault tolerance and balance load. It achieved up to 20.25% performance improvement as compared to PSO in Deutsche topology. For energy efficiency, the Fitness Averaged-Rider Optimization Algorithm (FA-ROA) improves cluster head selection in WSN-IoT networks. It achieved up to 50% better energy efficiency as compared to other algorithms. Other approaches such as GWOAP, Louvain Algorithm with Betweenness-Centrality, CPCSA and Tabu Search Algorithm have also been analyzed for CPP. This review paper provides a detailed analysis of these approaches and algorithms in addressing the complexities of controller placement in IoT networks. By exploring these algorithms this review paper aim to contribute to the development of more efficient, reliable, and scalable smart city infrastructure.

The Internet of Things (IoT) is transforming cities into smart cities by improving efficiency, safety, and sustainability through real-time data collection, analysis and decision making. A main challenge in IoT network is the Controller Placement Problem (CPP), which is essential for minimize latency, balance load, ensure reliability, and optimize energy consumption. This paper reviews 10 algorithms and approaches that have been proposed to solve CPP. To solve the CPP, algorithms minimize latency, improve fault tolerance, balance load, enhance energy efficiency, and ensure scalability. Various algorithms have been proposed to tackle these challenges, each algorithm has unique advantages. Among these algorithms the Hybrid Differential Evolution and Whale Optimization (DEWO) algorithm minimizes latency, improves fault tolerance and balance load. It achieved up to 20.25% performance improvement as compared to PSO in Deutsche topology. For energy efficiency, the Fitness Averaged-Rider Optimization Algorithm (FA-ROA) improves cluster head selection in WSN-IoT networks. It achieved up to 50% better energy efficiency as compared to other algorithms. Other approaches such as GWOAP, Louvain Algorithm with Betweenness-Centrality, CPCSA and Tabu Search Algorithm have also been analyzed for CPP. This review paper provides a detailed analysis of these approaches and algorithms in addressing the complexities of controller placement in IoT networks. By exploring these algorithms this review paper aim to contribute to the development of more efficient, reliable, and scalable smart city infrastructure.

IoT Networks, Smart Cities, Controller Placement, Software-Defined Networking (SDN), Optimization Algorithms

IoT Networks, Smart Cities, Controller Placement, Software-Defined Networking (SDN), Optimization Algorithms

MEMON, S.; ANDRIUKAITIS, D.; MARKEVICIUS, V.; NAVIKAS, D.; VALINEVICIUS, A.; ZILYS, M.; RAMANAUSKAS, R.; ŠOTNER, R.; JEŘÁBEK, J.; KLIMENTA, D.

01.06.2025

IEEE

NEW YORK

978-1-6654-6562-5

IEEE Workshop on Advances in Information Electronic and Electrical Engineering

1

6

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https://ieeexplore.ieee.org/document/11050873

A_Survey_on_Controller_Placement_Algorithms_for_IoT_Networks_in_Smart_City_Environments