AI technology presents significant opportunities to enhance home safety and security across Saudi Arabia. Potential applications include intelligent surveillance systems, predictive analytics for detecting anomalies, and automated emergency response mechanisms. These solutions aim to provide comprehensive protection for residents against various threats, including intrusions, fires, and other domestic hazards. Why it matters: This highlights Saudi Arabia's proactive approach to adopting advanced AI solutions to improve the quality of life and enhance public safety within its residential communities.
The KAUST Smart Home has received LEED Platinum certification, ranking second globally with a score of 94. The project retrofitted an existing home with features like 120 solar panels and hydropanels producing drinking water from atmospheric humidity. It also includes a leak detection system and smart fill windows controllable via touch panel. Why it matters: This demonstrates how existing infrastructures in the region can be improved to meet sustainability goals using smart technology.
MBZUAI Professor Fakherddine Karray is developing deep learning algorithms for human activity recognition to monitor the health and safety of elderly people. The AI tools analyze movement, posture, and facial expressions to detect early warning signs of health emergencies. Remote patient monitoring systems integrate smart devices and secure communication to allow elderly patients to stay at home and communicate with healthcare providers. Why it matters: AI-powered smart homes can provide affordable healthcare solutions for the rapidly growing elderly population in the region and worldwide.
Siemens CTO Rainer Speh spoke at KAUST about smart cities, noting that urban populations are growing, especially in cities like Riyadh and Jeddah. Cities consume two-thirds of the world's energy and generate 70% of CO2 emissions. Siemens is working on a driverless subway system in Riyadh as part of its smart city initiatives. Why it matters: Smart city initiatives are crucial for managing resources and reducing emissions in rapidly growing urban centers in Saudi Arabia.
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.
Khaled Alrashed, president and CEO of Saudi Electricity Company for Projects Development, discussed the challenges of future smart cities at a KAUST event. He emphasized the importance of smart grids, AI, and large-scale optimization for improving urban living. The Saudi Electricity Company is partnering with KAUST, including using the Shaheen supercomputer, to develop these technologies and predict grid load. Why it matters: This collaboration highlights Saudi Arabia's ambition to become a leader in smart city technology and renewable energy, leveraging local expertise and resources.