WiFiUS: Fault-Tolerant Cognitive IoT Systems Using Sensors of Limited Field-of-View: Fundamental Limits and Practical Strategies

提高物联网（IoT）的可靠性和效率是充分发挥其潜力的必要条件。物联网系统将感知、通信和处理数据，以产生可靠的决策和推断。研究人员研究了每个物联网设备将受到限制的三种方式：每个传感器的有限“视野”;严重的能源限制;传感和通信的不可靠性。这项研究通过获得和分析新的跨层物联网传感，通信和计算策略，在能源效率和可靠性方面超过经典策略几个数量级，从而促进了科学的进步。为了验证，研究人员通过神经接口和分布式摄像机推理来研究健康监测的实际相关问题的改进。该研究涉及多个研究生和本科生课程，重点关注女性和少数民族学生。严重的能源限制为“认知”物联网系统的跨层设计提供了令人信服的激励，以联合感知、压缩、通信和计算。然而，朴素的跨层设计可能会降低对系统故障的容忍度，例如，传感/通信/计算错误。研究人员追求系统的理解，利用它来获得认知物联网策略的新颖跨层设计，通过与有限视野兼容的新颖纠错技术，优雅地权衡容错和效率。他们将这种权衡与新的信息理论基本限制进行了比较。所获得的技术增加的鲁棒性的算法，使用，作为第一步，线性投影上的分布式感测的大尺寸数据。研究人员专注于广泛适用的k最近邻（k-NN）问题的算法，获得改进的算法，并分析性能-效率-鲁棒性权衡，桥接信息论，统计学和机器学习。

Improving reliability and efficiency of Internet of Things (IoT) is necessary to harness their full potential. IoT systems will sense, communicate, and process data to produce reliable decisions and inferences. The investigators study three ways in which each IoT device will be limited: limited "field of view" of each sensor; severe energy limitations; unreliability of sensing and communication. This research contributes towards progress of science by obtaining and analyzing novel cross-layer IoT sensing, communication, and computing strategies that outperform classical strategies by several orders of magnitude in energy efficiency and reliability. For validation, the researchers examine improvements on practically relevant problems of health monitoring through neuro-interfaces and distributed camera inference. The research involves several graduate and undergraduate courses, with emphasis on engaging female and minority students.Severe energy constraints provide a compelling incentive for cross-layer designs of "cognitive" IoT systems to jointly sense, compress, communicate, and compute. However, naive cross-layer designs can reduce tolerance to system faults, e.g., sensing/ communication/computation errors. The investigators pursue a systematic understanding, utilizing it to obtain novel cross-layer designs of cognitive IoT strategies that gracefully trade off fault tolerance and efficiency through novel error-correction techniques compatible with limited fields of view. They benchmark this trade off against novel information-theoretic fundamental limits. The obtained techniques increase the robustness of algorithms that use, as a first step, linear projections on distributedly-sensed large dimensional data. Focusing on algorithms for the widely applicable k-Nearest Neighbors (k-NN) problem, the researchers obtain improved algorithms and analyze performance-efficiency-robustness tradeoffs, bridging information theory, statistics, and machine learning.