This paper introduces the idea of activity-aware cyber-physical systems (CPS). Activity-aware systems allow smart city services to adapt to the needs of individual residents by being sensitive to their daily tasks. The paper first defines activity recognition and activity prediction algorithms that form the foundation of activity-aware CPS and implement a prototype activity-aware building automation system, called CASAS activity aware resource learning (CARL). Evaluation of CARL on real sensor data shows not only an accurate ability to sense and predict activities but an effective means of automation buildings that reduces energy consumption while being sensitive to user activities in the building. Our ideas are demonstrated in the context of a smart home but can be utilized in a variety of smart city settings including smart offices, smart hospitals, and smart communities.