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

我们的物理世界呈现出一套令人难以置信的丰富的观察模式，如热、光、湿度、压力、运动等。无线传感器网络（WSNs）的最新进展使我们能够以前所未有的高空间密度和长时间持续监测各种物理现象，从而为许多科学努力开辟了新的令人兴奋的机会。由于传感器节点是由电池供电的，因此无线传感器网络最关键的挑战是最小化功耗，其中最耗能的操作是数据传输。考虑到感知数据在时间和空间上的高度相关性，一个相关性研究的分析框架和新的数据采集协议对于通过无损数据压缩降低无线传感器网络的通信成本至关重要。该项目致力于通过综合的理论和经验方法，在恶劣和可能敌对的环境中利用WSNs的时间相关性进行基础研究，以维持监测。基于该项目，开发了一种基于预测编码的新型、分析性、自适应多模态预测传输框架，用于环境监测WSN工程，实现了大量的节能，从而大大延长了WSN的使用寿命。在此基础上，设计并实现了一套新的数据采集协议。此外，在丘陵流域部署了实际环境监测WSN试验台进行评估和验证。我们的跨学科教育计划使用建成的WSN测试平台，并将我们的研究成果和新见解整合到教育实践中，为环境和IT领域的本科生和研究生提供实践培训和经验。

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.