| Wireless sensor networks are a collection of small, disposable, low-power devices that monitor vital sensory data for a variety of civil, military, and navigational applications. For instance, some cities have a network of emergency phones scattered across walkways so that citizens in distress can immediately reach emergency services. Using effective localization techniques that are both highly accurate and of low computational cost, 911 services can dispatch police, fire, or medical services to a caller's location as quickly as humanly possible. Hence, from the standpoint of locating a node in a network, every percent of accuracy achieved and every second of time saved can be the difference between life and death.;This thesis presents two novel algorithms for wireless sensor network localization through the incorporation of Dempster-Shafer Evidence Theory. The first technique follows a verbose methodology for node positioning that fuses multiple types of signal measurements, such as received signal strength and angle of arrival, and utilizes the expected value property of DS Theory to geo-locate a node with a moderate accuracy of 78-87%, thereby providing an introductory approach to the previously untapped fusion of WSN localization and DS Theory.;The second approach consists of a low cost, highly accurate data fusion technique that incorporates the plausibility property of DS Theory to establish a high level of accuracy. Due to this unique approach to data fusion and predictive data modeling, this second algorithm achieves an optimal accuracy range of 83-98% in a flexible multitude of simulation scenarios at a fraction of the runtime required under prior established localization techniques. Overall, these two algorithms provide a ground-breaking new application of Dempster-Shafer Theory as well as fast, accurate, and informative new approaches to wireless sensor network localization that can improve a wide range of vital applications. |
