In recent years, there has been a substantial uptrend in the popularity of unmanned aerial vehicles (UAVs). These aircraft find application in several areas such as precision farming, infrastructure and environment monitoring, surveillance, surveying and mapping, search and rescue missions, rapid assessment of emergency situations and natural disasters, next generation Internet connectivity, weather determination and more. Given the wide range of possibilities, UAVs represent a growing market in CPS and they are perceived as an "enabling technology" to re-consider the human involvement in many military and civil applications on a global scale. One of the major challenges in enabling this growth is UAV endurance. This is directly related to the amount of energy available to the UAV to perform its mission. This proposal looks to increase UAV endurance by trading off UAV performance with energy efficient computing. This requires mapping of mission and goals into energy needs and computational requirements. The goal of the project is to show that this trade can enable long-duration flight especially when solar energy is utilized as a primary energy source. The ambitious plan is to develop a light weight and efficient aircraft capable of maneuver-aware power adaptation and real-time video/sensor acquisition and processing for up to 12 hours of continuous flight (this limit being set by daylight hours). This project aims to expanding the theoretical and practical foundations for the design and integration of UAVs capable of real-time sensing and processing from an array of visual, acoustic and other sensors. The traditional approach for small size UAVs is to capture data on the aircraft, stream it to the ground through a high power data-link, process it remotely, perform analysis, and then relay commands back to the aircraft as needed. Conversely, this research targets a solar-powered UAV with a zero-carbon footprint that carries a high performance embedded computer system payload capable of budgeting at run-time the available power between the propulsion/actuation subsystems and the computing and communication subsystems. First, a set of accurate power models for the considered UAV will be constructed to establish a mapping between different flight modes (aircraft maneuvers) and the corresponding power requirements at the propulsion/actuation subsystem. Second, software and hardware-level power adaptation mechanisms will be developed to devise a novel Power Adaptive Integrated Modular Avionic (PA-IMA) architecture suitable for UAVs. Safe temporal/spatial partitioning among applications and flexible scheduling to handle unpredictable power/load variations in flight represent key requirements. Once an accurate characterization is available for flight and computation modes, a higher-level supervisory logic will be developed to distribute the available power budget between the propulsion/actuation subsystem and the computation/communication subsystem. While precision farming and land/infrastructure monitoring will immediately benefit from such a technology, the long-term impact of this research is much broader since it explores the very foundations of environment-aware power and computation management. In general, the developed theory will be applicable to autonomous vehicles and robots whose power budget is limited and variable: these are common challenges faced when harvesting solar and wind energy.