NSF-BSF:CIF: Small: A Unified View of Estimation and Information Relationships for Networks and Beyond

预计新兴的物联网（IoT）范式将支持具有大量设备的超大型网络。鉴于物联网设备在未来通信基础设施中的重要性，了解此类大型网络性能的基本限制非常重要。 传统上，通信网络的性能极限是通过使用信息论的框架来研究的，信息论是由先驱工程师和科学家克劳德·香农提出的一门科学。本研究的目标是扩展未来通信网络分析所需的工具集。具体来说，其目的是从估计理论-一个处理噪声数据恢复的统计学分支中带来必要的工具。拟议中的工作将通过信息和估计理论的透镜发展通信网络的统一观点来推进知识。 总之，该项目的成功完成预计将贡献新的数学和工程工具，从根本上新的观点和模型，为了使下一代通信基础设施的有效设计。本研究的一个具体目标是通过使用网络变换来研究大型网络上的信息流，该网络变换将任意网络上的信息流问题映射到一个良好的网络上。结构化估计理论问题。换句话说，而不是在经典的信息理论域分析的问题，建议的变换提供了一个新的估计理论域的通信网络的性能分析。 建议的网络变换的关键吸引人的特点是，基本的估计理论优化问题是一个易于处理的。 通过估计理论技术为研究网络上的最佳信息流提供新的见解和工具，这项研究不仅有可能推进理论，而且有可能改善新兴物联网应用的延迟，能源效率和鲁棒性。该项目是美国和以色列研究人员的合作成果，以色列研究人员的资金由两国科学基金会（BSF）提供。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The emerging internet of things (IoT) paradigm is predicted to support extremely large networks with a massive number of devices. Given the importance that IoT device are expected to play in the future communication infrastructure, it is important to understand the fundamental limits of performance of such large networks. Classically the performance limits of communications networks are studied by using a framework of information theory -- a science put forth by the pioneering engineer and scientist Claude Shannon. The goal of this research is to expand the set of tools needed for the analysis of future communication networks. Specifically, the aim is to bring necessary tools from estimation theory---a branch of statistics dealing with the recovery of noisy data. The proposed work will advance knowledge by developing a unified view of communication networks through the lens of information and estimation theories. In summary, the successful completion of the project is expected to contribute new mathematical and engineering tools, fundamentally new perspectives and models, in order to enable efficient design of the next generation of communication infrastructure.A specific goal of this research is to study information flow over large networks by using a network transform that maps a problem of information flow over an arbitrary network to a well-structured estimation theoretic problem. In other words, instead of analyzing the problem in the classical information theoretic domain, the proposed transform offers a new estimation theoretic domain for analyzing performance of communication networks. The key attractive feature of the proposed network transform is that the underlying estimation theoretic optimization problem is a tractable one. By providing new insights and tools for studying optimal information flow over the networks via estimation theoretic techniques, this research has the potential not only to advance theory but also to improve latency, energy efficiency and robustness of emerging IoT applications. This project is a collaborative effort between researchers in the US and Israel, with funding for Israeli researchers provided by the Bi-National Science Foundation (BSF).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.