A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings - Review

Aug 4, 2026

The Internet of Things (IoT) enables sustainable smart buildings and accelerates digital transformation, making accurate machine learning (ML) classification of IoT devices essential for cyber defense, resource management, and occupant comfort. Feature selection techniques strengthen classification performance and interpretability, supporting diagnosis and effective system management. The study examines the integration of IoT and ML in smart buildings, reviews state-of-the-art feature selection methods and their purposes, and discusses key issues, challenges, optimization processes, and future directions for secure, resilient infrastructure.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Introduction

IoT and smart building context

Importance of device classification

Role of feature selection in ML

A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings Waseem, Q. , Din, W. I. S. B. W. , Rahman, A. B. A. , Khan, S. N. , Busaeed, R. A. A. , & Fairooz, T. (2025) . A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings. Innovative Infrastructure Solutions, 10(9), 1-19 . Article 407. [https://doi.org/10.1007/s41062-025-](https://doi.org/10.1007/s41062-025-)[ ](https://doi.org/10.1007/s41062-025-)02203-7 Link to publication record in Ulster University Research Portal Published in: Innovative Infrastructure Solutions Publication Status: Published (in print/issue): 20/08/2025 DOI: 10.1007/s41062-025-02203-7 Document Version Publisher's PDF, also known as Version of record Document Licence: CC BY For Author Accepted Manuscripts (AAM) published under Ulster University's Rights Retention Policy for Scholarly Works (RRPSW) When citing an AAM published under Ulster University's RRPSW please use the following citation structure: Author, A. A. (Year) . Title of article. Journal Name,[Accepted Author Manuscript] . PURE Portal URL. Licensed under CC BY 4.0. General rights The copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence. Unless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) . If the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) . Take down policy The Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk) Download date: 03/08/2026 Innovative Infrastructure Solutions (2025) 10:407 [https://doi.org/10.1007/s41062-025-02203-7](https://doi.org/10.1007/s41062-025-02203-7) REVIEW A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings QuadriWaseem1,2 · Wan Isni Sofiah BintiWan Din1 · Azamuddin Bin Ab Rahman1 · Sundas Naqeeb Khan3 · Raed Abdullah Abobakr Busaeed1 · Towfeeq Fairooz4 Received: 30 November 2024 / Accepted: 23 July 2025 / Published online: 20 August 2025 © The Author(s) 2025 Abstract The Internet of Things (IoT) has revolutionized modern living and infrastructure by driving the development of sustainable smart buildings and accelerating the digital transformation of buildings. In smart buildings, efficient machine learning (ML) based classification of IoT devices is critical for improving cyber defense, optimizing resource management, and maintaining occupant comfort. Feature selection techniques are vital for boosting the effectiveness of machine learning models when classifying and categorizing Internet of Things (IoT) devices for various reasons. Hence, this study initially provides an in-depth understanding of integrating IoT and ML in smart buildings. We provide the reasons and importance of device classification in smart buildings, which may range from monitoring security, power consumption, resource allocation, maintenance, and rehabilitation scenarios. This study emphasizes the importance of feature selection (FS) models in enhancing the accuracy of classification and interpreta

FAQs

Why is ML-based IoT device classification important in smart buildings?

It improves cyber defense, optimizes resource management, and helps maintain occupant comfort through accurate and effective device categorization.

What role do feature selection techniques play in this study?

Feature selection enhances classification accuracy and interpretability, aiding diagnosis and management of smart building systems.

What does the survey cover regarding feature selection methods?

It reviews the principles and types of feature selection methods, their purposes and applications, and the key issues and challenges in applying them to smart building infrastructures.

Aug 4, 2026

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