With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing scenarios, the measurements generated by sensor networks are noisy and usually annotated with some measure of uncertainty. The problem we address in this thesis is how to estimate the accuracy of the sensor systems based on the probabilistic measurements they provide. This problem is increasingly common in many settings, such as multiple sensing services are competing for the same group of users, detecting faults in large scale networks, or establishing trustworthiness of different individuals in social sensing. It is also challenging in many ways, for instance, the ground truth of the monitored states is absent, the users often lack a clear view of the implementation details of the sensor systems, and the reported accuracy can be misleading. To address theses challenges, in this thesis we formulate the problem of estimating the accuracy of sensor systems in a general manner that applies to a broad spectrum of sensing scenarios. We then propose an accuracy estimation framework that breaks the problem into layers, which can be implemented in different ways. We present a novel inference-based accuracy estimation approach, which assesses the accuracy of sensor systems by comparing the reported measurements with the states inferred with the probabilistic measurements from all systems and available prior knowledge. We also propose a new learning-based approach for accuracy estimation, which employs novel parameter learning techniques. The learned parameters are either used to improve estimating the accuracy of sensor measurements, or to derive the accuracy of sensor systems directly in certain cases. We perform a systematic experimental evaluation on two datasets collected from real-world sensor deployments, where an array of different approaches are juxtaposed and compared extensively. We discuss how they trade accuracy for computation cost, and how this trade-off largely depends on the knowledge of the sensing scenarios. We also show that the proposed approaches outperform the competing ones in estimating accuracy and ranking the sensor systems.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635270 |
| Date | January 2014 |
| Creators | Wen, Hongkai |
| Contributors | Trigoni, Niki |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:4ce383e9-7223-48bc-a461-83e19b1afe64 |
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