NeTS: Small: Exploring the Signal Sparsity in Sensor Networks Based on Compressive Sampling

该项目的主要目标是研究将新兴的压缩感知（CS）理论应用于典型网络设置下的各种基本传感器网络问题的挑战和技术。建议的研究活动包括：i）时间和空间压缩采样，以减少传输的数据量，同时保持信息水平; ii）压缩数据收集，以减少通信开销，同时保持高保真数据恢复; iii）使命-关键的传感器网络应用，如离群值检测和目标计数，以说明CS公式的问题和CS作为一种技术的优势方法;及iv）测试平台及实地部署以作验证用途。这些研究活动的动机是观察a）传感器网络通常被部署来测量各种自然信号，这些信号通常是可压缩的并且在时间和空间上是相关的; B）基本的传感器网络问题，例如拓扑控制和网络内数据聚合和压缩，研究传感器读数之间的可压缩性和相关性以节省资源;以及c）CS提供了通过经由非自适应随机投影仅获取重要信息来恢复可压缩数据的方法。预期的结果包括新的算法，可以大大有助于CS理论和传感器网络。我们的研究可能会激发一个新的探索浪潮，通过稀疏信号恢复广泛的基本传感器网络问题，已通过传统的方法研究了多年。研究成果将通过高质量的出版物以及在重点讲习班和会议上的介绍加以传播。

The major objective of this project is to study the challenges and techniques of applying the emerging Compressive Sensing (CS) theory to various fundamental sensor network problems under typical network settings. The proposed research activities include: i) temporal and spatial compressive sampling to decrease the transmitted data volume while preserve the information level; ii) compressive data gathering to decrease the communication overhead while preserve high-fidelity data recovery; iii) mission-critical sensor network applications such as outlier detection and target counting to illustrate the CS formulations of the problems and the strength of CS as a technical approach; and iv) testbed and field deployment for validation purpose. These research activities are motivated by the observations that a) sensor networks are typically deployed to measure various natural signals that are usually compressible and are temporally and spatially correlated; b) fundamental sensor network problems such as topology control and in-network data aggregation and compression investigate the compressibility and correlation among sensor readings for resource conservation; and c) CS provides an approach to recover the compressible data by acquiring just the important information via non-adaptive random projections. The expected results include novel algorithms that can contribute significantly to both CS theory and sensor networking. Our research could motivate a new wave of exploration via sparse signal recovery on a wide range of fundamental sensor network problems that have been investigated through traditional approaches for many years. Research outcomes will be disseminated through high-quality publications as well as presentations in focused workshops and conferences.