@article {69,
	title = {A robust outlier detection scheme for collaborative sensor networks},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2007},
	pages = {12 - 18},
	abstract = {In-networks, Data Aggregation is usually warranted for distributed wireless sensor networks, owing to reliability and energy efficiency reasons. Sensor nodes are usually deployed in unattended and unsafe environments and hence are vulnerable to intentional or unintentional damages. Individual nodes are prone to different type of faults such as hardware faults, crash faults etc and other security vulnerabilities wherein one or more nodes are compromised to produce bogus data so as to confuse the rest of the network in collaborative sensing applications. The availability of constrained resources and the presence of faulty nodes make designing fault tolerant information aggregation mechanisms in large sensor networks particularly challenging. In our work, we consider Byzantine type of faults, which encompasses most of the common sensor node faults [9]. Faulty nodes are assumed to send inconsistent and arbitrary values to other nodes during information exchange process. These values are termed as outliers and we use a statistical test called Modified Z-score method to reliably detect and remove outliers. We show by simulation that the proposed strategy works well for 2 major classes of collaborative sensor network applications viz. (i) Target/Event detection and (ii) Continuous data gathering.},
	keywords = {Data aggregation, Detection accuracy, Distributed information processing, False positives, Fault tolerance, Outliers},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33947305265\&partnerID=40\&md5=2fae284d218d7852052fabf8a0ef68ca},
	author = {Jayashree, L.S. and Arumugam, S. and Vijayalakshmi, K.}
}
