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.

我们的物理世界提出了一个令人难以置信的丰富的观测模式，如热，光，水分，压力，运动等无线传感器网络（WSNs）的最新进展，使各种物理现象的连续监测在前所未有的高空间密度和长时间的持续时间，因此，打开新的令人兴奋的机会，众多的科学工作。由于传感器节点是电池供电的，无线传感器网络中最关键的挑战是最大限度地减少功率的使用，其中最耗能的操作是数据传输。由于传感数据在时间和空间上的高度相关性，相关性研究和新的数据收集协议的分析框架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的基础研究，通过综合的理论和经验方法，在恶劣和可能敌对的环境中进行持续监测。从这个项目中，一种新的，分析，自适应的多模式预测传输框架的基础上预测编码的开发，环境监测无线传感器网络工程，实现大幅节能，因此，显着延长生命周期的无线传感器网络。在此基础上，设计并实现了一个新的数据采集协议集。此外，一个真实世界的环境监测无线传感器网络测试床在丘陵流域部署的评估和验证。我们的跨学科教育计划使用构建的WSN测试平台，并将我们的研究成果和新见解融入教育实践，为环境和IT领域的本科生和研究生提供实践培训和经验。