Thesis
Quality of service for context awareness in sensorwebs
Washington State University
Master of Science (MS), Washington State University
2009
Handle:
https://hdl.handle.net/2376/102511
Abstract
Wireless Sensor Network (WSN) is characterized by limited resources. There has and continues to be active research in the wireless community to manage limited resources including energy and bandwidth. WSNs are usually connected with a diverse range of sensors, which enables them to sense the surrounding environment and comprehend the current context. This thesis provides a unique approach that uses the ability of WSNs to be context aware in order to achieve a better Quality of Service (QoS). In this work we enhance our proven QoS management algorithm Tiny-DWFQ (Tiny-Dynamic Weighted Fair Queuing) [55] by making it context aware. Tiny-DWFQ is a light weighted algorithm designed to adjust its network parameters dynamically at runtime. It smartly manages the real-time data flows to achieve better QoS. The context aware module in this work is specific to our Optimized Autonomous In-Situ Sensorweb (OASIS) project, where we are building an In-Situ sensorweb to monitor the active volcano Mount St. Helens. Of the many interesting contexts the environment posses, we choose to identify volcanic tremors because of their proven importance in estimating an impending volcanic explosion. We implemented an in-network algorithm using the proven statistical method of cross-correlation to detect volcanic tremors in real-time. The context-awareness module is used to set data priorities, which are subsequently used in Tiny-DWFQ to provide QoS to high priority data during network operations. We evaluated the performance of Tiny-DWFQ on an iMote2 platform. Our results show that Tiny-DWFQ performs better in all test cases delivering a higher throughput and lower packet loss. We also evaluated our context aware module in a controlled environment to test the accuracy of our in-network processing. Evaluation results show that our in-network processing algorithm is highly accurate and reliable in defining context. This work also contributed partially to the development of Panorama, our Google Earth based web interface to monitor OASIS.
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Details
- Title
- Quality of service for context awareness in sensorwebs
- Creators
- Lohith Anusuya Rangappa
- Contributors
- Behrooz A. Shirazi (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; Pullman, Wash. :
- Identifiers
- 99900525042701842
- Language
- English
- Resource Type
- Thesis