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Robust Classification and Clustering in Wireless Sensor Networks Pro-/Projektseminar or Bachelor Thesis Distributed adaptive signal processing and communication networking are rapidly advancing research areas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement in adverse environments. Today’s wireless sensor networks provide the possibility to monitor physical envi- ronments via small low-cost wireless devices. Given the large amount of sensed data, efficient and robust clustering and classification becomes a critical task in many applications. Typically, the devices must op- erate under stringent power and communication constraints and the transmission of observations to a fusion center is, in many cases, unfeasible or undesired. Furthermore, data recorded by wireless sensor networks may be affected by noise and errors. A challenging research question in such cases is the design of data clustering and classification rules when each sensor collects a set of unlabeled observations. By communicating with each other, the network of sensors achieves improved clustering and classification results compared to single node processing, while being insensitive to a certain amount of outliers or erroneous data. Students participating in this project will learn about clustering and classification techniques with special emphasis on distributed setups as well as robust statistics. They should investigate how to obtain a re- liable clustering or classification of data based on a unique labeling of the sources, even in the presence of faulty or distorted data. Depending on the student’s knowledge and interest, an individual topic for a student project can be discussed. SIGNAL PROCESSING GROUP If you are interested in the project please contact: Patricia Binder, M.Sc. [email protected] Room S3|06 252 Image sources: iStock.com/mstay (left).

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Page 1: Robust Classification and Clustering in Wireless Sensor

Robust Classificationand Clusteringin Wireless Sensor Networks

Pro-/Projektseminar or Bachelor Thesis

Distributed adaptive signal processing and communication networking are rapidly advancing researchareas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement inadverse environments. Today’s wireless sensor networks provide the possibility to monitor physical envi-ronments via small low-cost wireless devices. Given the large amount of sensed data, efficient and robustclustering and classification becomes a critical task in many applications. Typically, the devices must op-erate under stringent power and communication constraints and the transmission of observations to afusion center is, in many cases, unfeasible or undesired. Furthermore, data recorded by wireless sensornetworks may be affected by noise and errors. A challenging research question in such cases is the designof data clustering and classification rules when each sensor collects a set of unlabeled observations. Bycommunicating with each other, the network of sensors achieves improved clustering and classificationresults compared to single node processing, while being insensitive to a certain amount of outliers orerroneous data.

Students participating in this project will learn about clustering and classification techniques with specialemphasis on distributed setups as well as robust statistics. They should investigate how to obtain a re-liable clustering or classification of data based on a unique labeling of the sources, even in the presenceof faulty or distorted data. Depending on the student’s knowledge and interest, an individual topic for astudent project can be discussed.

SIGNAL

PROCESSING

GROUP

If you are interested in the project please contact:

Patricia Binder, [email protected] S3|06 252

Image sources: iStock.com/mstay (left).

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