Although there has been a considerable amount of research done by using cross-layer optimization, fundamental understandings on the design limits of cross-layer optimization are still missing. The overarching goal of this project is to fill this knowledge gap. The objectives are to 1) qualitatively characterize multi-scale temporal-spatial behaviors of cross-layer optimization; 2) develop a novel complexity-scalable framework to support cross-layer optimization over heterogeneous network elements with different computation capabilities; and 3) evaluate the proposed framework by using extensive simulations as well as field test based on our existing testbed.
The intellectual merits and transformative significance of this project include: 1) to provide a theoretical framework to understand major design tradeoffs in cross-layer design and optimization and 2) to provide a common ground to quantitatively evaluate and compare different cross-layer design schemes. By developing a new theoretical framework to quantitatively analyze design tradeoffs in complex system optimization, the broader impacts of this project are: 1) to benefit wide applications in social networks, economics, physics, and biology, leading to a profound impact on all societal levels and 2) to expand the existing collaborations with industrial partners and enhance the ongoing effort on STEM education. Results, outcomes, software tools, benchmarks, and educational materials will be disseminated through a project?s web site as well as journal and conference publications. The research results of cross-layer optimizations will also be integrated into courses taught in the PI's university.
