Of recent, Building Information Modelling (BIM) has become an influential paradigm for the development of better project delivery practices to improve construction and operational efficiencies. In the last 6 years, a significant number of studies have been published on the integration of BIM in Internet of Things (IoT). This paper aims to examine the general research productivity, demographics, and trends shaping the research domain. Hence, the paper will also help to identify, categorize, and synthesize important studies in the research domain. In doing so, we adopt an evidence-based systematic mapping methodology to ensure the coverage of key studies through a systematic and unbiased selection and evaluation process which results in the final selection of 55 relevant studies. The results of the mapping study show that the research on the integration of BIM in IoT is gaining more attention in last 6 years with stable and consistent publication output. Prominent application domains, validation methods, contribution facets, research types, and simulation tools in the field of study were identified and presented. Five research types were also identified, i.e. solution proposal, experience paper, evaluation research, validation research, and opinion paper, with solution proposals getting more research attention. In general, the overall demographics of the research domain were presented and discussed.

Building Information Modeling (BIM) has emerged as a prospective technology used to advance the practices of construction projects. Also, Internet of Things (IoT), as a technology that connects sensing devices to share information across platforms, has become essential in building and construction environment. The integration of BIM‐IoT in the construction industry, a high‐risk industry, might increase overall performance and reduce related hazards. However, there is a dearth of studies on the integration of BIM and IoT in the construction industry. Scoping review of literature was performed using various databases such as IEEE Xplore, Science Direct, ACM, Emerald Insight, and Taylors & Francis databases to explore the study demographics, research direction, category, adoption, and performance of the BIM‐IoT integration for the construction industry. Out of 2270 articles identified, a total of 81 key and vital articles were found and collected in scoping review to formulate the research questions. The study results revealed that the literature related to BIM‐IoT integration and adoption is moderately steady, with constant output in the last four years. Twelve of the contributions were identified, and five were identified to be proposed more and conducted by researchers: investigation, evaluation, model, framework, and system. Also, fifteen (18.51%) studies were identified from the selected works that were evaluated using performance measurement. The findings shed light on some of the most significant difficulties in research related to BIM‐IoT integration in the construction industries as well as potential future initiatives.

Fifth-generation (5G) technology is now being established globally and is a central topic of discussion for future generations. The building sector is at risk because the internet of things (IoT) is one of the central components of building 4.0. Building products from IBM, Amazon, Intel, Google, and Apple have demonstrated the sector’s future direction. For example, IoT can be used in crowdsensing applications, where many building inhabitants submit data to building systems via smart devices such as mobile phones, smartwatches, and other means. Building information modeling (BIM) and IoT data are two distinct entities, each filling a gap left by the other. Previous research has addressed many issues, including sustainability, risk, safety, and BIM-IoT integration. This study analyzed previous studies and research on BIM and IoT data integration, as well as the challenges, benefits, and opportunities that come with it. Furthermore, this study also addressed integrating information obtained/transmitted by sensors and actuators in ways distinct from the software and platform used Using IoT in buildings can improve comfort while reducing energy use.

This research focuses on developing an anomaly detection system using machine learning to mitigate Distributed Denial of Service (DDoS) attacks in IoT networks. The study utilizes a diverse dataset from IoT environments to train and evaluate machine learning algorithms for DDoS detection. The dataset includes various IoT device types, communication protocols, and network configurations. The research aims to achieve several objectives, including dataset preprocessing, feature engineering, machine learning model selection, anomaly detection, and performance evaluation. The research team preprocesses the raw Internet of Things (IoT) network data by cleaning and transforming it to prepare it for analysis. They then extract relevant features from the data to effectively characterize normal and abnormal network behavior. Multiple machine learning algorithms are evaluated and compared to determine the most suitable models for DDoS detection in IoT networks. The selected machine learning models are then used to identify and classify abnormal traffic patterns associated with DDoS attacks. The performance of the developed anomaly detection system is evaluated by assessing its accuracy, precision, recall, and F1 score. The significance of this research lies in its potential to enhance the security of IoT networks by proactively detecting and mitigating DDoS attacks. By leveraging machine learning, the study aims to provide a robust defense mechanism against this pervasive threat, ensuring the reliability and availability of IoT services and applications.