In this project, we empass recent advances in wastewater treatment, food security, data analytics, and machine intelligence.We propose to study novel optimized technology-driven controlled-environment agriculture (CEA) systems that can achieve high-areal vegetable productivity to increase food and nutritional security of urban communities with low operating cost and reduced energy consumption.Our project focuses on two core CPS research areas, i.e., control and data analytics, inspired by the design and operation of a Pilot-Testbed at Georgia Tech to couple the water and nutrients in domestic wastewater (DWW) to high-productivity CEAs. Food production in urban CEAs must overcome land acquisition constraints, requiring DWW-CEA operating cost reductions accompanied by increased productivity and nutrition. However, it is highly challenging to control and optimize this complex system of subsystems. In our case, we need to coordinate the Pilot-Plant and Pilot-Farm, examine their inter-correlation, and support dynamic and robust optimal decisions to achieve the highest yield, while simultaneously satisfying various performance specifications, i.e., nutrient composition, operating cost and energy consumption, with the guarantee that food safety requirements are met. Moreover, the profound impact of numerous operating conditions and parameters on vegetable phenotype, yield and nutrient composition during different growth periods needs to be thoroughly understood. We must progress from model-driven CPS fundamentals to an integrated data-driven model-based approach.