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Understanding the prediction gap in multi-hop localization

Wireless sensor networks consist of many tiny, wireless, battery-powered sensor nodes that enable the collection of sensor data from the physical world. A key requirement to interpreting this data is that we identify the locations of the nodes in space. To this end, many techniques are being developed for ranging-based sensor localization, in which the positions of nodes can be estimated based on range estimates between neighboring nodes. 
Most work in this area is based on simulation, and only recent applications of ranging-based localization in the physical world have revealed what we call the Prediction Gap: localization error observed in real deployments can be many times worse than the error predicted by simulation. The Prediction Gap is a real barrier to sensor localization because simulation is an essential tool for designing, developing, and evaluating sensor technology and algorithms before they are actually used in costly, large-scale deployments. 
The goals of this dissertation are (1) to close the Prediction Gap and (2) to identify its causes in sensor localization. We first establish the existence and magnitude of the Prediction Gap by building and deploying a sensor localization system and comparing observed localization error with predictions from the traditional model of ranging. We then develop new non-parametric modeling techniques that can use empirical ranging data to predict localization error in a deployment. We show that our non-parametric models do not cost significantly more than traditional parametric models in terms of data collection or simulation, and solve many of the prediction issues present in existing simulations. 
In order to identify the causes of the Prediction Gap in sensor localization, we create hybrid models that combine components of our non-parametric models with traditional parametric models. By comparing localization error from a hybrid model with a purely parametric model, we isolate the effects of that component of our data. We use this technique to identify the causes of the Prediction Gap for six different localization algorithms from the literature, and conclude by developing a new parametric model that captures the true characteristics of our empirical ranging data.