Overview of Sensing and Data Processing Technologies for Smart Building Services and Applications

Hamza Elkhoukhi (), Abdellatif Elmouatamid (), Achraf Haibi, Mohamed Bakhouya and Driss El Ouadghiri
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Hamza Elkhoukhi: Department of Mathematics and Computer Science, University Toulouse 2 Jean Jaurès, 31100 Toulouse, France
Abdellatif Elmouatamid: Department of Sciences and Technologies, UMR Espace-Dev, University of French Guiana, 97300 Cayenne, France
Achraf Haibi: IA Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes 11201, Morocco
Mohamed Bakhouya: LERMA Lab, College of Engineering and Architecture, International University of Rabat, Sala El Jadida 11103, Morocco
Driss El Ouadghiri: IA Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes 11201, Morocco

Sustainability, 2025, vol. 17, issue 9, 1-23

Abstract: Internet of things (IoT) and big data technologies are increasingly gaining significance in the implementation of various services and applications. Consequently, much of the research focused on energy efficiency and building management revolves around integrating IoT and big data technologies for data collection and processing. Occupancy detection, comfort, and energy management are the most important services for optimizing building energy consumption in smart buildings, and environmental data play a key role in improving these services. Furthermore, the integration of advanced and recent techniques, such as IoT, big data, and machine learning, is progressively becoming more vital for both researchers and industries. This paper presents and discusses various emerging technologies that will contribute to designing novel IoT-based architectures to improve smart building services. These technologies offer innovative solutions to address the challenges of interoperability, scalability, and real-time data processing within intelligent environments, paving the way for more efficient, adaptive, and user-centric smart building systems. The main aim of this research is to help researchers define an optimal architecture that presents all layers, from sensing to big data stream processing. We established comparative criteria between the most popular data processing techniques to select the appropriate framework for developing intelligent platforms for managing building services, such as occupancy detection systems and occupants’ comfort management, and further, to enhance the deployment of digital twins for critical environment monitoring and anomaly detection. The proposed architecture uses Apache Kafka, Apache Storm, and Apache SAMOA as its core components, creating a comprehensive platform for efficient data collection, monitoring, and processing with high performance in terms of fault tolerance and low latency.

Keywords: internet of things; big data; occupancy detection; smart building; energy efficiency in building (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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