NeTS-NOSS: Collaborative Research: Investigating Temporal Correlation for Energy Efficient and Lossless Communication in Wireless Sensor Networks

NetS-NOSS：协作研究：研究无线传感器网络中节能和无损通信的时间相关性

• 批准号: 0758372
• 负责人: Yao Liang
• 金额: $ 22.09万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0758372&HistoricalAwards=false
• 关键词: NeTS; NOSS; Collaborative; Research; Investigating

Our physical world presents an incredibly rich set of observation modalities, such as heat, light, moisture, pressure, motion, etc. Recent advances in wireless sensor networks (WSNs) enable the continuous monitoring of various physical phenomena at unprecedented high spatial densities and long time durations and, hence, open new exciting opportunities for numerous scientific endeavors. Because sensor nodes are battery-powered, the most critical challenge in WSNs is minimizing the use of power, of which the most energy-consuming operation is data transmission. Given the commonly high correlations of sensed data in time and space, an analytical framework for correlation studies and new data gathering protocols is fundamentally important to reduce communication costs through lossless data compression in WSNs. This project is devoted to the fundamental investigation of exploiting temporal correlation In WSNs, for sustaining monitoring in harsh and possibly hostile environments, through an integrated theoretical and empirical approach. From this project, a novel, analytical, adaptive multimodal predictive transmission framework based on predictive coding is developed, for environmental monitoring WSN engineering, to achieve substantial energy savings and, hence, to significantly extend the lifetime of WSNs. Based on the developed framework, a new data gathering protocol suite is designed and implemented. Furthermore, a real-world environmental monitoring WSN testbed in a hilly watershed is deployed for evaluation and validation. Our interdisciplinary education plan uses the built WSN testbed and integrates our research results and new insights into education practice to provide hands-on training and experience for undergraduate and graduate students in both environmental and IT fields.

我们的物理世界呈现出令人难以置信的丰富观察方式，例如热，光，水分，压力，运动等。无线传感器网络（WSN）的最新进展使得能够在前所未有的高空间密度和长时间的长时间和长时间的持续时间和长时间的高时期，因此，为众多科学师提供新的激动人心的机会，可以不断地监测各种物理现象。由于传感器节点是电池供电的，因此WSN中最关键的挑战是最大程度地减少功率的使用，其中最耗能的操作是数据传输。鉴于时空和空间中相关数据的相关性通常很高，相关研究和新数据收集协议的分析框架对于通过WSN中的无损数据压缩降低通信成本至关重要。该项目致力于通过综合的理论和经验方法来维持WSN中利用时间相关性的基本调查，以维持严酷和可能的敌对环境中的监测。从这个项目中，开发了一种基于预测性编码的新型，分析，自适应的多模式预测传输框架，用于环境监测WSN工程，以实现大量的能源节省，从而显着延长WSN的寿命。基于开发的框架，设计和实施了新的数据收集协议套件。此外，在丘陵流域中测试的现实世界环境监测被部署以进行评估和验证。我们的跨学科教育计划使用了内置的WSN测试床，并将我们的研究结果和新见解整合到教育实践中，以为环境和IT领域的本科和研究生提供动手培训和经验。