| Type of Document |
Master's Thesis |
| Author |
Anantharaju, Srinath , |
| Author's Email Address |
srinath.anantharaju@gmail.com |
| URN |
etd-07282005-161557 |
| Title |
Resilient Data Aggregation in Wireless Sensor Networks |
| Degree |
Master of Science |
| Graduate Program |
Computer Science |
| Advisory Committee |
| Advisor Name |
Title |
| Peng Ning |
Committee Chair |
| Douglas Reeves |
Committee Member |
| Ting Yu |
Committee Member |
|
| Keywords |
- MMSE
- Multiple Events
- Event Detection Model
- Distributed Outlier Detection
- Spatial correlation
- Event-centric Outlier Detection
- Event Localization
|
| Date of Defense |
2005-07-25 |
| Availability |
unrestricted |
Abstract
Sensor nodes are low-cost and low-power devices that are prone to node compromises, communication failures and malfunctioning of sensing hardware. As a result, some nodes may report outlying data values, introducing significant deviations in the aggregated sensor readings. This thesis presents a practical resilient outlier detection technique to filter out the influence of the outlying data reported by faulty or compromised nodes. The proposed outlier detection algorithm is based on event localization using minimum mean squared error (MMSE) estimation combined with threshold-based consistency checking to detect outliers. Data aggregation is one of the key techniques commonly used to develop lightweight communication protocols applicable to wireless sensor networks. The proposed approach handles localization of multiple events by grouping the sensor readings into spatially correlated clusters and performing an event-centric detection of outliers. In the entire process of data aggregation, the outlier detection technique fits as a preprocessing stage for reducing the effect of outliers on the aggregated result. Suitable extensions to the basic outlier detection algorithm are proposed to effectively apply the algorithm to both centralized and decentralized sensor network architectures. This thesis further includes studies that test the effectiveness of the proposed approach, including the detection rate, the false positive rate, degree of damage and the resilience to malicious readings introduced by the attackers. The experimental results show that on average the proposed approach detects as high as 80-90% of the outliers while resulting in 5-15% false positive rate when the network consists of 40-45% outliers. The experiments also show that the extent of damage on the aggregated result is below 50% due to the elimination of outliers before aggregation. Finally, the resilient data aggregation process requires modest computational and memory requirements with zero communication overhead in the centralized case and about 20% overhead in the decentralized settings.
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