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Clustering for smart cities in the internet of things: a review

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Abstract

Nowadays, internet of things (IoT) applications, especially in smart cities, are fast developing. Clustering is a promising solution for handling IoT issues such as energy efficiency, scalability, robustness, mobility, load balancing, and so on. The clustering method, which can be applied in IoT, groups sensor nodes into clusters with one node operating as the cluster head. This paper intends to determine the usage of clustering in IoT as a case study for smart cities. Furthermore, this study discusses clustering algorithms on IoT, open issues, and future challenges of clustering in the context of the smart city, and also existing research papers selected by the systematic literature review technique published between 2017 and 2021. Also, we provide a technical taxonomy for clustering categorization in IoT, which includes algorithm, architecture, and application. According to the statistical analysis of 51 chosen research articles in the domain of clustering in IoT, the results show that the number of clusters has a high percentage of 24%, the energy factor has 23%, the execution time factor has 18%, the accuracy has 14%, the delay has 9%, the lifetime has 6%, and throughput has 6%.

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Authors and Affiliations

  1. Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh

  2. Department of Computer Science, University of Human Development, Sulaymaniyah, Iraq

    Atefeh Hemmati

  3. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan

    Amir Masoud Rahmani

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  1. Mehdi Hosseinzadeh
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Hosseinzadeh, M., Hemmati, A. & Rahmani, A.M. Clustering for smart cities in the internet of things: a review. Cluster Comput 25, 4097–4127 (2022). https://doi.org/10.1007/s10586-022-03646-8

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