CSR: Small: System and Network Support for Harvesting-aware Solar Sensor Networks

CSR：小型：对采集感知太阳能传感器网络的系统和网络支持

• 批准号: 0916577
• 负责人: Deepak Ganesan
• 金额: $ 45万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916577&HistoricalAwards=false
• 关键词: CSR; Small; System; Network; Support

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Many sensor network deployments are in harsh terrain that lack infrastructure but also need to use solar energy harvesting for self-sustained operation. The design of harvesting-aware solar sensor networks raises numerous systems and networking challenges. From a systems perspective, a design should be able to operate under a wide dynamic range of energy harvesting scenarios, from plentiful sunlight to very limited light (e.g., thickly forested areas). From a network perspective, a protocol stack should be able to adapt to spatio-temporal variability in harvesting rates due to foliage and day-to-day vagaries.In this project, we will design systems and network support for harvesting-aware solar sensor networks. Our systems contributions include novel sensor platforms that use thin-film batteries for harvesting under low-light conditions, predictive duty-cycling under highly variable harvesting conditions, and lightweight checkpointing and restart to handle intermittent loss of power. Our networking contributions include a link layer that is optimized for duty-cycle variability, a network layer that uses harvesting-aware path metrics, and a transport layer can offer high throughput under diverse harvesting conditions.Our educational plans consist of K-12 teaching through a summer high school outreach program, and inter-disciplinary REU programs in collaboration with Harvard Forest for deploying solar-powered sensor networks for ecological monitoring.

该奖项是根据2009年的《美国回收与再投资法》（公共法第111-5号）资助的。许多传感器网络部署处于缺乏基础设施的严酷地形，但也需要使用太阳能收集进行自我维持的操作。 收获感知太阳能传感器网络的设计增加了许多系统和网络挑战。 从系统的角度来看，设计应该能够在能量收集的各种动态范围内进行操作，从丰富的阳光到非常有限的光线（例如，茂密的森林地区）。 从网络的角度来看，协议堆栈应能够适应由于叶子和日常变化而导致的收获率时空变异性。在该项目中，我们将设计用于收获太阳能传感器网络的系统和网络支持。 我们的系统贡献包括使用薄膜电池在低光条件下收获的新型传感器平台，在高度可变的收获条件下进行预测税收循环以及轻巧的检查点和重新启动以处理间歇性损失的权力损失。 我们的网络贡献包括针对税收周期可变性进行优化的链路层，使用收获感知路径指标的网络层，运输层可以在不同的收获条件下提供高通量。夏季高中宣传计划和跨学科的REU计划与哈佛森林合作，用于部署太阳能传感器网络进行生态监测。