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: 0721853
• 负责人: Yao Liang
• 金额: $ 22.09万
• 依托单位: Virginia Polytechnic Institute and State 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 2007-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721853&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极其丰富的观测模式，例如热、光、湿度、压力、运动等。无线传感器网络 (WSN) 的最新进展使得能够以前所未有的高空间密度和长时间连续监测各种物理现象。持续时间，因此为众多科学事业开辟了新的令人兴奋的机会。由于传感器节点由电池供电，因此无线传感器网络中最关键的挑战是最大限度地减少电力的使用，其中最耗能的操作是数据传输。鉴于感测数据在时间和空间上通常具有较高的相关性，相关性研究的分析框架和新的数据收集协议对于通过无线传感器网络中的无损数据压缩来降低通信成本至关重要。该项目致力于利用无线传感器网络中的时间相关性进行基础研究，通过综合的理论和经验方法在恶劣和可能敌对的环境中维持监测。该项目开发了一种基于预测编码的新颖、分析、自适应多模态预测传输框架，用于环境监测无线传感器网络工程，以实现大量节能，从而显着延长无线传感器网络的使用寿命。基于所开发的框架，设计并实现了新的数据采集协议套件。此外，还部署了丘陵流域的真实环境监测 WSN 测试台进行评估和验证。我们的跨学科教育计划利用已构建的WSN测试平台，将我们的研究成果和新见解融入到教育实践中，为环境和IT领域的本科生和研究生提供实践培训和经验。