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

George Washington University, Washington DC

Investigators

Abstract

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.