CAREER: Energy-Efficient Sensor Networks Using Analog Signal Processing

职业：使用模拟信号处理的节能传感器网络

• 批准号: 1148815
• 负责人: David Graham
• 金额: $ 40万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1148815&HistoricalAwards=false
• 关键词: CAREER; Energy; Efficient; Sensor; Networks

Wide-scale deployment of wireless sensor networks has been hindered primarily by the inability to endure for long durations on small power sources. The focus of this project is on creating computationally efficient electronics to extend the lifetimes of wireless sensor nodes. Ultra-low-power analog circuitry is used to provide additional computational resources at each sensor node while simultaneously reducing the total power consumed. This project systematically addresses how to effectively use analog signal processing in a wide variety of wireless sensor network applications by investigating (1) the creation of modifiable, easy-to-use smart-sensor architectures for ultra-low-power computation, (2) continuous-time signal-processing approaches for event-detection systems (including biologically inspired processing), and (3) ultra-low-power memory storage and data conversion techniques. The importance of this research is in providing the knowledge necessary to create drastically improved nodes for wireless sensor networks in terms of power consumption and computational ability. The same low-power design techniques can be used for a variety of other power-constrained applications such as consumer electronics and biomedical/implantable devices. Integrating education and outreach with the research of this project will lead to (1) close mentoring of undergraduate and graduate students in a team-centric environment, (2) cross-disciplinary coursework, and (3) high-school outreach activities to expose many new students, including those from underrepresented groups, to engineering and electronic systems.

无线传感器网络的大规模部署主要受到小电源无法长时间持续的阻碍。 该项目的重点是创建计算效率高的电子产品，以延长无线传感器节点的寿命。 超低功耗模拟电路用于在每个传感器节点提供额外的计算资源，同时降低总功耗。 本计画系统地探讨如何有效地将类比讯号处理应用于各种无线感测器网路应用中，研究（1）创造可修改且易于使用的智慧型感测器架构，以进行超低功率运算，（2）事件侦测系统的连续时间讯号处理方法（包括生物启发处理），和（3）超低功率存储器存储和数据转换技术。 这项研究的重要性是在提供必要的知识，以创建大幅改善无线传感器网络节点的功耗和计算能力。 相同的低功耗设计技术可用于各种其他功耗受限的应用，如消费电子和生物医学/植入式设备。 将教育和推广与本项目的研究相结合，将导致（1）在以团队为中心的环境中密切指导本科生和研究生，（2）跨学科课程，（3）高中外展活动，以使许多新学生，包括来自代表性不足的群体的学生，接触工程和电子系统。