NESL Technical Report #: 2007-3-5
Abstract: Sensors and devices used in wireless sensor networks are state-of-the-art technology with the lowest possible price. The sensor measurements we get from these devices are therefore often noisy, incomplete and inaccurate. Researchers studying wireless sensor networks hypothesize that much more information can be extracted from hundreds of unreliable measurements spread across a field of interest than from a smaller number of high-quality, high-reliability instruments with the same total cost. This thesis offers a basis for exploring that hypothesis in some detail. We make four contributions. First, we describe sensor faults commonly seen in recent sensor network deployments, and we formulate statistical models to assist in the analysis of those faults. Second, we present some basic tools for assessing the robustness of aggregation algorithms to these common faults. We then address, in two separate ways, the issue of finding linear calibration parameters while sensors are deployed. Our third contribution is an approach to calibration using state space models and non-linear, non-Gaussian filtering techniques to calibrate sensors without groundtruth knowledge or controlled stimuli. We evaluate this calibration on simulated sensor data with a simple dynamical model based on the physical process of soil moisture. Fourth, we present a general problem formulation for blind calibration which assumes that the n sensor measurements lie in a subspace of n-dimensional space. We prove the identifiability of the sensor offsets and gains under this assumption, and we evaluate implementations on both simulated and real sensor data.
Publication Forum: UCLA EE Department M.S. Thesis
Page (Count): 99
Public Document?: Yes
NESL Document?: Yes
Document category: Other