NeTS: Small: Collaborative Research: Towards Adaptive and Efficient Wireless Computing Networks

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Today's mobile devices are not merely smart, they are becoming intelligent as artificial intelligence applications such as Facebook Caffe2 and Google Tensor-Flow Lite are being pushed into mobile devices and as mobiles devices are being integrated into the cloud-fog-mobile architecture. This calls for efficient and adaptive computing/communication co-design of wireless networks to optimize application-level latency (including both communication latency and computing times) and to achieve energy efficiency (considering energy consumed by both communications and computing). This project develops fundamental theories and novel architectures of low-latency, energy-efficient, and computing-centric wireless networks to support emerging mobile intelligence applications. Theories and algorithms developed by the PIs are constantly integrated into the undergraduate and graduate courses taught at the two universities. This project also provides hand-on experiences to undergraduate and high school students with state-of-the-art wireless technologies. Computing/communication co-design, while new for wireless networks, is a central topic in data center networks. However, the proposed solutions, while inspiring, are not directly applicable to wireless computing networks because of the unique features of wireless networks such as wireless interference, channel fading and limited energy. This project focuses on provably optimal mechanisms that dynamically and adaptively schedule computing tasks and data transmissions to meet application-level performance requirements, and consists of three interdependent thrusts: (i) Optimal computing/communication co-design. This thrust develops mathematical models and theoretical limits of wireless computing networks, (ii) Robust computing/communication co-design. This thrust focuses on robust computing and communication co-design that achieves desired performance with imperfect state information and under unavoidable short-term system overload and (iii) Learning-aided adaptive computing/communication co-design. This thrust further improves the performance of wireless computing networks by leveraging both historical data and predictable user behaviors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.