WEPPE: Wireless Edge-Computing Personal Protective Equipment for Large-Scale Health Monitoring

WEPPE：用于大规模健康监测的无线边缘计算个人防护设备

• 批准号: 2201447
• 负责人: Mohammad Haider
• 金额: $ 49.96万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201447&HistoricalAwards=false
• 关键词: WEPPE; Wireless; Edge; Computing; Personal

The research objective of this proposal is to provide a general-purpose, scalable edge-computing architecture critically needed to support the next generation of personal protective equipment (PPE) technology. The proliferation of sensors and wireless sensor networks (WSNs) results in high-volume data generation, increases the computational burden at the central data center, creates data transmission bottlenecks, and hinders the real-time decision-making process. These challenges arise due to the existing limits of IoT devices on computational power, memory, and wireless bandwidth (BW) allocation. The case study chosen as a framework for developing such a system is motivated by the recent and urgent need for better tracking of the spread of transmittable diseases over large areas. The WEPPE project will resort to two-phase approaches to address the challenges mentioned earlier. In the first phase, the project will investigate a low-cost inkjet-printable nonlinear-element and develop a machine-learning platform on a flexible substrate for low-level sensor data processing or in-situ computation. In the second phase, the project will integrate an efficient analog pulse-based data encoding and decoding scheme to wirelessly relay the processed sensor data from the first phase to a data center without requiring extended network bandwidth. The proposed WEPPE project is expected to produce a unique machine learning framework that hinges on the fundamentals of reservoir computing, novel inkjet-printed sensors and nonlinear elements, and wireless data telemetry scheme with secure communication. Customized hardware and low-level computing will enable in situ edge computing while maintaining quality data abstraction for real-time network-level or big data processing for rapid decision-making. The education goal is to broaden the participation of female, minority, and African-American students and train and educate them for the next era of engineering challenges.This proposed project will investigate how edge computing via hardware-based machine learning and data encryption/decryption schemes may effectively resolve the IoT problems of limited bandwidth, secure data transmission, high-density data throughput, and power-efficient in-situ computation. The project has targeted mainly four research goals - (i) Research on Reservoir Computing Architectures for Sensor Network Analysis, (ii) Research on Inkjet-Printed Devices for Sensing and Physical Computing, (iii) Investigate Energy-Efficient Orthogonal Pulses and Multi-bit Data Mapping, and (iv) Research on Orthogonal Analog Pulse Based Data Compression and Decompression. A reservoir computing architecture-based machine learning platform, especially the Echo State Network (ESN), will be investigated for its simplicity, less training time with relatively reduced training data volume, and ease of deployment. As an integral part of this effort, the project will also investigate an inkjet-printed low-cost nonlinear element, which will be a core building block for developing a machine-learning platform on a flexible substrate. The reservoir will generate a state vector, which is a hyper-dimensionalized encrypted representation of the raw data, and as a result, will provide data compression and security. Fault detection and sensor fusion will occur by training the reservoir and merging the state vectors. The state vectors from the reservoirs will then be further encrypted and spectrally compressed in the "Wearable Hub" by a k-bit encoding scheme using analog orthogonal pulses (AOP). At the "Local Server," the encoded AOPs from all the wearable hubs will be compressed by an n-pulse compression technique and transmitted to the "Data Center." The secured receiver at the "Data Center" will decode the state vectors using secured read-out neurons, providing predictions to be sent back to the end users for monitoring or large-scale processing by deep learning and other machine learning methods.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.

该提案的研究目标是提供一种通用，可扩展的边缘计算结构，以支持下一代个人保护设备（PPE）技术。传感器和无线传感器网络（WSN）的扩散会导致大量数据生成，增加了中央数据中心的计算负担，创建数据传输瓶颈并阻碍实时决策过程。这些挑战是由于物联网设备在计算能力，内存和无线带宽（BW）分配中的现有限制而出现的。选择作为开发这种系统的框架的案例研究是由于最近迫切需要更好地跟踪可传播疾病在大面积上的传播的动机。 Weppe项目将采用两阶段的方法来应对前面提到的挑战。在第一阶段，该项目将研究一个低成本的油墨打印机非线性元素，并在灵活的基板上开发用于低级传感器数据处理或原位计算的机器学习平台。在第二阶段，该项目将集成一个有效的基于模拟脉冲的数据编码和解码方案，以无线传感器数据从第一阶段无线传输到数据中心，而无需扩展网络带宽。拟议的Weppe项目预计将产生一个独特的机器学习框架，该框架取决于储层计算的基础知识，新型的喷墨印刷传感器和非线性元素，以及具有安全通信的无线数据遥测方案。定制的硬件和低级计算将启用原位边缘计算，同时维持实时网络级别或大数据处理的优质数据抽象以快速决策。 The education goal is to broaden the participation of female, minority, and African-American students and train and educate them for the next era of engineering challenges.This proposed project will investigate how edge computing via hardware-based machine learning and data encryption/decryption schemes may effectively resolve the IoT problems of limited bandwidth, secure data transmission, high-density data throughput, and power-efficient in-situ computation.该项目主要针对四个研究目标 - （i）用于传感器网络分析的储层计算体系结构的研究，（ii）用于感应和物理计算的喷墨印刷设备的研究，（iii）研究能效节能正交脉冲以及多站数据映射，以及（IV）对基于脉动脉搏脉冲脉冲数据的研究。基于水库计算体系结构的机器学习平台，尤其是Echo State Network（ESN），将以简单性，减少培训数据量和易于部署的方式进行调查。作为这项工作不可或缺的一部分，该项目还将研究喷墨印刷的低成本非线性元素，该元素将是在柔性基板上开发机器学习平台的核心构建块。储层将生成一个状态向量，该状态向量是原始数据的超维加密表示形式，因此，将提供数据压缩和安全性。故障检测和传感器融合将通过训练储层并合并状态向量而发生。然后，使用模拟正交脉冲（AOP），将进一步加密储层中的状态向量，并通过K-BIT编码方案在“可穿戴轮毂”中进行频谱压缩。在“本地服务器”中，所有可穿戴轮毂的编码AOP将被N-Pulse压缩技术压缩并传输到“数据中心”。 The secured receiver at the "Data Center" will decode the state vectors using secured read-out neurons, providing predictions to be sent back to the end users for monitoring or large-scale processing by deep learning and other machine learning methods.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.

项目成果

A low-cost inkjet-printed heart sound sensor for telehealth application

用于远程医疗应用的低成本喷墨打印心音传感器

• 发表时间: 2023
• 期刊: A low-cost inkjet-printed heart sound sensor for telehealth application
• 作者: M. R. Opu, S. D.

An Inkjet-Printed Capacitive Sensor for Ultra-Low-Power Proximity and Vibration Detection

用于超低功耗接近和振动检测的喷墨印刷电容式传感器

• 发表时间: 2023
• 期刊: IEEE 23rd Annual Wireless and Microwave Technology Conference (WAMICON
• 作者: S. D. Gardner, M. M.

Spectrum-Efficient Analog Pulse Index Modulation for High-Volume Wireless Data Telemetry

• DOI: 10.1109/jiot.2023.3234262
• 发表时间: 2023-06
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: M. K. Hossain;Muhammad Masud Rana;M. Haider

An Affordable Inkjet-Printed Foot Sole Sensor and Machine Learning for Telehealth Devices

用于远程医疗设备的经济实惠的喷墨印刷脚底传感器和机器学习

• DOI: 10.1109/lsens.2023.3279392
• 发表时间: 2023
• 期刊: IEEE Sensors Letters
• 影响因子: 2.8
• 作者: Gardner, Steven;Porbanderwala, Adnan;Haider, Mohammad R.
• 通讯作者: Haider, Mohammad R.