Real-Time Sampling, Estimation, and Inference in Networked Systems
The Internet of Things (IoT) and social networks have provided unprecedented platforms for the generation, dissemination and collection of real-time information. The information is often governed by processes that evolve over time and/or space (e.g. on an underlying network). Estimation and inference for such processes, especially in time-critical applications, require efficient real-time and adaptive sampling strategies. This project will develop theoretical foundations and algorithmic designs for real-time sampling, estimation and inference in networked systems and considers applications such as estimation and control in IoT, testing and quarantine for COVID-19, and timely detection and control of the spread of misinformation in social networks. The overarching goal of this project is to develop novel and foundational frameworks for real-time (sequential) sampling strategies that exploit partial information for decision making in networks. It establishes fundamental tradeoffs between information extraction and reward maximization with the former traditionally rooted in information theory and the latter in networking, and proposes novel solutions by a merge of ideas from information theory, stochastic processes, network sciences, graphical models, and learning.
Practical and Timely Caching for Dynamic and Volatile Networks
Caching has become a key component of Internet-of-Things (IoT), particularly in information-centric network (ICN) architectures where the focal point is content (or data) rather than where it can be retrieved from. As a result, in ICN networks one can replicate and store (or cache) content at various nodes or storage units throughout the network so that it can be accessed faster locally without burdening the server and the global network. Until recently, caching was mainly studied on the network layer. Information-theoretic approaches to the problem, however, opened new avenues of research through what is now known as coded caching. Compared to traditional works in the networking community, these studies are primitive in the sense that they consider idealized/simplistic network models and user demands, have high complexity of design, do not adapt to dynamic and volatile networks, and do not ensure timeliness of information. Nevertheless, they capture fundamentally new network coding opportunities and attain scalable gains in lowering latency. Our main goal in this project is to unify two important aspects of caching: information theory and networking in order to devise practical and timely caching strategies for networks.
Attaining the New Frontier of Spectral Efficiency with Tradeoffs in Computation Through Cloud Radio Access Networks
NSF award #1850356
In the next generation of 5G mobile networks, cloud radio access networks (C-RANs) are among the most promising technologies to attain a leap forward in spectral efficiency. This project which is collaborative with Rutgers University undertakes a cross-layer investigation of the C-RAN technology and targets spectral efficiency with tradeoffs in complexity. For more details, click here