Geometric filter algorithms for device-free localization using received-signal strength in wireless sensor networks

Device-free localization (DFL) is a method of determining the location of a target without requiring the target to wear a device or tag. This capability to track a device-free target is useful in applications where the target may be uncooperative and unwilling to be located and monitored. In radio frequency-based DFL systems that use received-signal strength (RSS) measurements, the changes induced by the target’s presence or motion on the RSS of the network’s links are used to infer his location. A number of RSS-based DFL algorithms have been recently proposed that can locate and track a target accurately, albeit with high computational requirements. This thesis presents new DFL algorithms that have lower computational costs while able to track a single device-free target with high accuracy. In this thesis, a new single target RSS-based DFL algorithm, referred to as the “Geometric Filter” (GF) algorithm is proposed. The GF algorithm uses simple geometric objects to represent radio links, probable target locations, and locational filters. The intersection points of line segments representing the target-affected links are used as probable locations of the device-free target. A locational filter is used to remove outlier links and points. Information about the target’s prior location and induced RSS changes are used to further refine the target location estimates. In order to perform accurate tracking in multipath-rich environments, the GF algorithm was extended further to utilize channel diversity. The “Multi-Channel Geometric Filter” (MCGF) fuses measurements of the RSS changes of each link across different frequency channels, and uses link-specific thresholds to detect the target-affected links. The measurements are then processed by a modified GF algorithm that uses estimates of the overall fade levels of intersecting links as weights to generate the target location estimates. The GF and MCGF algorithms have been evaluated using single-target tracking experiments in both indoor and outdoor environments. In these experiments, the new algorithms have been shown to outperform existing DFL algorithms in both tracking accuracy and execution time.DOCTOR OF PHILOSOPHY (EEE

Maximum likelihood estimation of ground truth for air quality monitoring using vehicular sensor networks

Various works on vehicular sensor networks (VSNs) for air quality monitoring use solid-state gas sensors due to its low cost and compact form factor. However, solid-state gas sensors have poor selectivity and are sensitive to ambient temperature and relative humidity. In addition, the sensitivity and accuracy of solid-state gas sensors degrade over time due to aging effects. Frequent recalibration of these sensors are required to maintain the accuracy of their measurements. In large VSNs, it is impractical to manually calibrate each node. Thus, calibration must be performed automatically and in-field. Assuming that the gas concentration is homogenous within an area, co-located VSN nodes can either: (1) copy measurements from a highly accurate fixed station in their immediate vicinity, or, in the absence of a fixed station, (2) collaboratively estimate the ground truth. In this work, we use maximum likelihood estimation for determining the ground truth gas concentration in an area by fusing information from co-located sensors in a VSN. Through simulations, we show that the absolute errors of the proposed method has lower mean and standard deviation as compared with existing work