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

该提案的研究目标是提供支持下一代个人防护装备(PPE)技术所必需的通用、可扩展的边缘计算架构。传感器和无线传感器网络(WSN)的激增导致了海量数据的产生，增加了中心数据中心的计算负担，造成了数据传输瓶颈，并阻碍了实时决策过程。这些挑战源于物联网设备在计算能力、内存和无线带宽(BW)分配方面的现有限制。之所以选择案例研究作为发展这一系统的框架，是因为最近迫切需要更好地跟踪传染病在大片地区的传播情况。Weppe项目将采取两个阶段的办法来应对前面提到的挑战。在第一阶段，该项目将研究一种低成本的可喷墨打印的非线性元件，并在柔性衬底上开发一个机器学习平台，用于低水平传感器数据处理或现场计算。在第二阶段，该项目将集成高效的基于模拟脉冲的数据编码和解码方案，以无线方式将处理后的传感器数据从第一阶段传递到数据中心，而不需要扩展网络带宽。拟议的Weppe项目预计将产生一个独特的机器学习框架，该框架取决于油藏计算的基本原理、新型喷墨打印传感器和非线性元件，以及具有安全通信的无线数据遥测方案。定制的硬件和低级别计算将支持现场边缘计算，同时保持高质量的数据抽象，以便进行实时网络级或大数据处理，以便快速决策。教育目标是扩大女性、少数族裔和非裔美国学生的参与，并为下一个时代的工程挑战培训和教育他们。这个拟议的项目将调查基于硬件的机器学习和数据加密/解密方案的边缘计算如何有效解决物联网问题，如有限的带宽、安全的数据传输、高密度的数据吞吐量和高能效的现场计算。该项目主要针对四个研究目标-(I)用于传感器网络分析的水库计算体系结构的研究，(Ii)用于传感和物理计算的喷墨打印设备的研究，(Iii)节能的正交脉冲和多比特数据映射的研究，以及(Iv)基于正交模拟脉冲的数据压缩和解压缩的研究。基于油藏计算体系结构的机器学习平台，特别是回声状态网络(ESN)，将因其简单、训练时间短、训练数据量相对较少以及易于部署而受到研究。作为这项工作的组成部分，该项目还将研究一种喷墨打印的低成本非线性元件，这将成为在柔性衬底上开发机器学习平台的核心构件。储存库将生成状态向量，该状态向量是原始数据的超维化加密表示，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存库和合并状态向量来进行。然后，通过使用模拟正交脉冲(AOP)的k比特编码方案，将来自储存库的状态向量进一步加密并在“可穿戴集线器”中进行频谱压缩。在“本地服务器”，来自所有可穿戴集线器的编码AOP将通过n脉冲压缩技术进行压缩，并传输到“数据中心”。数据中心的安全接收器将使用受保护的读出神经元对状态向量进行解码，提供预测并发送回最终用户，以便通过深度学习和其他机器学习方法进行监控或大规模处理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.