CSR:Small: High Data Density Short Range Wireless Telemetry for Next Generation IoT Applications

下一代物联网（IoT）应用（switch -IoT）的高数据密度短距离无线遥测这项switch -IoT项目的长期目标是建立一个变革性的多学科研究和创新的高数据密度短距离无线遥测和节能计算平台，该平台集成了用于物联网（IoT）应用的定制模拟射频（analog- rf）电路和系统架构。无线传感器和传感器网络的激增使得物联网应用中的多传感器得以广泛部署。随着无线传感器数量的增加，大量传感器的数据量的累积给有限的无线频谱下的实时数据处理和数据传输带来了瓶颈。为了缓解这些问题，瑞士-物联网研究项目将研究一个两阶段的方法。在第一阶段，该项目将研究本地处理或计算单元，用于原位低水平信号处理、数据减少，并在指定带宽内实现大量数据通信。在第二阶段，该项目将研究一种新的脉冲编码方案，利用正交脉冲进一步压缩数据量。这种双重数据减少（本地处理和脉冲编码）可以在指定带宽内支持高数据速率。然后将压缩的数据流送入注入锁定的功率振荡器，以驱动天线进行无线传输。提出的变革性大容量无线数据采集方案将影响各种与传感器相关的应用和决策过程，如交通、公共卫生、皮质测绘、智能家居等。教育目标是整合代表性不足的K-12学生的艺术技能，通过充满乐趣的环境激励，参与和帮助学习STEM材料，并为他们未来的STEM职业做好准备。该项目将继续培训本科生和研究生，举办夏令营，并通过“It's Electric”、“C3-STEM”和UAB CORD项目推广外展项目，以增加代表性不足和少数族裔接受高等教育的人数。为了实现瑞士-物联网项目的研究目标，主要有三个研究目标：(i)高效节能的本地数据压缩处理，（ii）高效节能的多阶正交脉冲发生器模拟射频芯片级实现，以及（iii）基于频谱高效的正交脉冲模拟脉冲序列编码。首先，将采用振荡神经网络（ONN）和改进的尖峰检测器算法来研究局部处理单元。通过利用超低功耗自振荡节点，ONN将通过节点间的同步或不同步来执行类关联模式识别任务，并且只中继同步和收敛时间的识别指标频率的值，而不中继原始数据。研究了一种改进的基于非线性能量算子的节能尖峰检测器算法，用于检测信号事件，如冲击载荷、动作电位、突变等，并中继事件戳以减少数据量。其次，switch - iot项目将研究基于修正Hermite多项式的多阶正交脉冲生成方案。创新的神经启发架构将降低系统复杂性，提高能源效率，并以更小的外形实现集成电路级实现。第三，一种新的组合脉冲序列编码将允许同时进行多通道无线遥测，具有极好的频谱效率数据密度和数据安全性。在该方案中，将使用n个不同的多阶正交脉冲为每个通道创建脉冲序列。对每个通道使用（n-1）个冗余正交脉冲将支持大量（= n!）通道和（n-1）！-数据速率提高的次数。该奖项反映了美国国家科学基金会的法定使命，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。

High Data Density Short Range Wireless Telemetry for Next Generation Internet-of-Things (IoT) Applications (SWiT-IoT)The long-term goal of this SWiT-IoT project is to establish a transformative and multidisciplinary research and innovation on high data-density short-range wireless telemetry and energy-efficient computational platform integrated with custom analog radio-frequency (analog-RF) circuit and system architectures for Internet-of-Things (IoT) applications. Proliferation of wireless sensors and sensor network has enabled widespread deployment of multi-sensors for IoT applications. With the increase of wireless sensors, the cumulative data volume from a large group of sensors is creating bottleneck for real-time data processing and data transmission through a limited wireless spectrum. To alleviate these problems, the SWiT-IoT research project is going to investigate a two-phase approach. On the first phase, the project will investigate a local processing or computational unit for in-situ low-level signal processing, data reduction, and enable high volume of data communication within the specified bandwidth. On the second phase, the project will investigate a novel pulse encoding scheme using orthogonal pulses to compress the data volume further. This two-fold data reduction (local processing and pulse encoding) enables high data-rate support within the specified bandwidth. The compressed data stream will then be fed to an injection-locked power oscillator to drive the antenna for wireless transmission. The proposed transformative large volume wireless data acquisition scheme will influence various sensor related applications and decision-making processes, such as transportation, public health, cortical mapping, smart homes, etc. The education goal is to integrate the artistic skills of underrepresented K-12 students to motivate, engage and help learning STEM materials through a fun loving environment and get them prepared for future STEM careers. The project will continue training undergraduate and graduate students, conduct summer camps, and promote outreach programs through "It's Electric", "C3-STEM" and UAB CORD program, to increase the number of underrepresented and minorities towards higher education.In pursue of the research objectives in the SWiT-IoT project, mainly three research goals are targeted: (i) Energy-Efficient Local Processing for Data Reduction, (ii) An energy-efficient analog-RF chip level implementation of multi-order orthogonal pulse generator, and (iii) Spectral efficient orthogonal pulse based analog pulse-sequence encoding. First, a local processing unit will be investigated by employing oscillatory neural network (ONN) and improved spike detector algorithms. By utilizing ultra-low-power self-oscillating nodes, the ONN will perform class-associative pattern recognition task by synchrony or desynchrony among the nodes, and only relay the values of the recognition indicators frequency of synchronization and convergence time, instead of the raw data. A modified nonlinear energy operator based energy-efficient spike detector algorithm will be investigated for detecting signal events e.g. impact loading, action potentials, abrupt changes, etc. and relay the event stamp to reduce the data volume. Second, the SWiT-IoT project will investigate a Modified Hermite Polynomial based multi-order orthogonal pulse generation scheme. An innovative neuro-inspired architecture will be utilized with reduced system complexity, better energy-efficiency, and integrated circuit level implementation with smaller form factor. Third, a novel combinatorial pulse-sequence encoding will allow simultaneous multichannel wireless telemetry with superb spectrum-efficient data density and data security. In this scheme, n distinct multi-order orthogonal pulses will be used to create pulse-sequence for each channel. The use of (n-1) redundant orthogonal pulses for each channel will enable supporting a large number (= n!) of channels and (n-1)!-times of data rate improvement.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.