10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Toufiq Aziz, Shafi Ullah Khan, Insoo Koo Abstract - Smart building functionalities are crucial for optimizing energy consumption through accurate occupancy detection in indoor environments. This study explores a novel approach to occupancy detection by leveraging Radio Environment Maps (REMs). Unlike conventional methods, the proposed technique involves creating detailed REMs across diverse indoor environments, considering variables such as room size, furniture arrangements, and the presence of the occupants. A predictive model is then applied to the REM data to detect occupancy and validate the accuracy of the predictions. The method was tested across multiple scenarios, including rooms with and without occupants, demonstrating its adaptability to different environmental conditions. By analyzing signal behavior variations, this method provides insights into how environmental factors impact detection performance. The results demonstrate the robustness of the proposed REM-based approach, achieving high prediction accuracy and confirming the effectiveness of the REMs in capturing the environmental nuances that influence signal behavior. This approach offers a flexible and scalable alternative for occupancy detection, with potential applications in energy-efficient building management and smart environment design.
