In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

Electric vehicle (EV) adoption is rapidly growing worldwide, driven by global decarbonisation goals that place growing pressure on charging infrastructure and transport systems. As EV charging demand is spatially distributed and temporally dynamic, allocating vehicles to optimal charging stations (CSs) is a complex, multi-objective decision-making problem shaped by mobility patterns, infrastructure availability, and user behaviour at scale. Existing research models EVs as homogeneous agents or only considers limited combinations of their dynamic factors, including queue time at charging infrastructure, charging cost, or the energy feasibility in guiding to CSs limiting their ability to support scalable and interpretable decision-making. We propose a digital twin (DT) framework that integrates real-world mobility patterns, heterogeneous user behaviour, and diverse CS characteristics into a Multi-Criteria Decision Analysis (MCDA) approach capable of real-time CS selection. The resulting decision-making solution guides EV charging choices based on travel time, queueing time, charging time, charging cost, and energy feasibility. To ensure scalability and support near real-time operation, the proposed DT relies on a machine learning model (LSTM) trained with data generated from simulations of different experimental setups to predict these CSs decisions. The Explainable Artificial Intelligence (XAI) method, SHapley Additive exPlanations (SHAP), is incorporated into the framework to provide real-time explanations, transparency, and interpretability for the prediction of charging decisions. A case study of Orkney, UK, demonstrates the ability of the proposed LSTM that achieved an average accuracy for real-time CS selection of 98.7\% compared to heavier digital models.