Routing topology recovery for wireless sensor networks
Abstract
In this dissertation, we consider an important problem of wireless sensor network (WSN) routing topology inference/tomography from indirect measurements observed at the data sink. Previous studies on WSN topology tomography are restricted to static routing tree estimation, which is unrealistic in real-world WSN time-varying routing due to wireless channel dynamics. We study general WSN routing topology inference where the routing structure is dynamic. We formulate the problem as a novel compressed sensing problem. We then devise a suite of decoding algorithms to recover the routing path of each aggregated measurement. The algorithm's complexity is analyzed and provided. Our approach is tested and evaluated though both simulations and a real-world testbed. WSN routing topology inference capability is essential for routing improvement, topology control, anomaly detection and load balance to enable effective network management and optimized operations of deployed WSNs.
Degree
Ph.D.
Advisors
Liang, Purdue University.
Subject Area
Computer science
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