Doctor of Philosophy

dissertationLocation information of people is valuable for many applications including logistics, healthcare, security and smart facilities. This dissertation focuses on localization of people in wireless sensor networks using radio frequency (RF) signals, speci cally received signal strength (RSS) measurements. A static sensor network can make RSS measurements of the signal from a transmitting badge that a person wears in order to locate the badge. We call this kind of localization method radio device localization. Since the human body causes RSS changes between pairwise sensor nodes of a static network, we can also use RSS measurements from pairwise nodes of a network to locate people, even if they are not carrying any radio device. We call this device-free localization (DFL). The rst contribution of this dissertation is to radio device localization. The human body has a major e ect on the antenna gain pattern of the transmitting badge that the person is wearing, however, existing r

Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT)

Dial It In: Rotating RF Sensors to Enhance Radio Tomography

A radio tomographic imaging (RTI) system uses the received signal strength (RSS) measured by RF sensors in a static wireless network to localize people in the deployment area, without having them to carry or wear an electronic device. This paper addresses the fact that small-scale changes in the position and orientation of the antenna of each RF sensor can dramatically affect imaging and localization performance of an RTI system. However, the best placement for a sensor is unknown at the time of deployment. Improving performance in a deployed RTI system requires the deployer to iteratively "guess-and-retest", i.e., pick a sensor to move and then re-run a calibration experiment to determine if the localization performance had improved or degraded. We present an RTI system of servo-nodes, RF sensors equipped with servo motors which autonomously "dial it in", i.e., change position and orientation to optimize the RSS on links of the network. By doing so, the localization accuracy of the RTI system is quickly improved, without requiring any calibration experiment from the deployer. Experiments conducted in three indoor environments demonstrate that the servo-nodes system reduces localization error on average by 32% compared to a standard RTI system composed of static RF sensors.Comment: 9 page