Dr. Silvia Nittel

Spatial Informatics Cluster and NCGIA
School of Computing and Information Science
University of Maine, USA      


Silvia Nittel’s main research interests are in spatial database systems, distributed systems, spatio-temporal data streams, and data management for stationary and mobile ad-hoc sensor networks for environmental applications (geosensor networks).  Dr. Nittel received her Ph.D. in 1994 from the Computer Science Department of the University of Zurich where she worked on high-performance storage techniques for extensible and object-oriented DBMS. She joined the UCLA Computer Science Department as postdoctoral researcher in 1995,  and worked on  high-performance integration platforms for massive amounts of heterogeneous remote sensing data used for data mining applications in climate research. From 1998 to 2001, she was the Co-Director of the UCLA Data Mining Lab. Her research focused on high performance data stream-based tools for scientific data mining and scientific collaboration (e.g. Conquest system). 

Her current research focuses on data management for ad-hoc geosensor networks. Geosensor networks comprise sensor network technology (such as Intel Motes and TinyOS/TinyDB) deployed for environmental applications.  The current main research interest is in the detection, monitoring and tracking of continuous phenomena such as toxic plumes or regions of toxic algae blooms in the ocean. She developed several approaches using discrete measurements of sensor network nodes to estimate the continuous nature of such phenomena. Another research approach is constituted the fundamental idea of switching from processing quantitative information about such phenomena to a qualitative approach. In this case, information, communication and message size between neighboring nodes can be significantly reduced while still being able to track the boundary and/or behavior of continuous phenomena (ACMGIS 2005 publication). 

For more information see the following publications:

S. Nittel A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring,  Sensors 2009, 9(7), 5664-5678; doi:10.3390/s90705664, published: 15 July 2009.

G. Jin and S. Nittel: Towards Spatial Window Queries Over Continuous Phenomena in Sensor Networks,  IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol 19(4), pp. 559-571, April 2008.  

G. Jin and S. Nittel, Efficient tracking of 2D objects with spatio-temporal properties in wireless sensor networks, Journal of Parallel and Distributed DatabasesVol 29(1-2), pp.3-30. February 2011

J. Jiang, M. Worboys and S. Nittel, Qualitative Change Detection in Sensor Networks based on Connectivity Information,  Geoinformatica, Vol 15, Issue 15(2), 2010, pp.305.

M. Duckham, S. Nittel and M. Worboys: Monitoring dynamic spatial fields using responsive geosensor networks,  ACM-GIS 2005, Bremen, Germany, November 2005. 

A second larger research focus is on mobile ad-hoc geosensor networks. In this domain, most of the nodes in the sensor networks are mobile in geographic space. Dr. Nittel developed several approaches of efficient information dissemination in mobile geosensor networks detecting different types of spatio-temporal events. A simple type of such an event is e.g. an icy patch on a street network, and automobiles detecting the event and strategies to efficiently and effectively disseminate the information to other (mobile) nodes in the network (GiScience 2004 publication). This work was eventually extended to the problem ad-hoc shared ride systems in an urban transportation network consisting of pedestrians, cars, taxi cabs and public transportation. In this case, the information of spatio-temporal 'event' (the pedestrian in need of a ride to a certain destination) has to be disseminated to transportations hosts (cars, taxis, etc). At the same time, ride offers have to be routed back to clients in time, and clients need to perform routing planning based on the current, dynamically changing situation (IJGIS 2006 paper, "Societies in the Age of Instant Access" 2006 book chapter). Currently, Dr. Nittel is collaborating with students and faculty of the School of Marine Science at the University of Maine to develop a mobile sensor network consisting of so-called small-scale drifter buoys to track ocean currents in the Gulf of Maine.

For more information see the following publications:

S. Nittel, N. Trigoni, K. Ferentinos, F. Neville, A. Nural, and N. Pettigrew A drift-tolerant model for data management in ocean sensor networks, MobiDE'07, in conjunction with SIGMOD, Bejing, China, 2007.

S. Nittel, S. Winter, A. Nural, and T. Cao. Shared Ride Trip Planning using Geosensor Networks, In: H. Miller (Ed.), Societies and Cities in the Age of Instant Access, Springer, NL,  2007.

S. Winter,  and S. Nittel: Ad-hoc shared ride trip planning by mobile geosensor networks, International Journal of Geographic Information Science, Vol 20(8):899-916 (2006).

S. Nittel, M. Duckham, and L. Kulik: Information Dissemination in Mobile Ad-hoc Geosensor Networks,  Third International Conference on Geographic Information Science (GIScience 2004), College Park, Maryland, October 2004. 

S. Nittel, C. Dorr, J.C. Whittier: LocalAlert: Simulating decentralized ad-hoc collaboration in emergency situations accepted for publication in GIScience 2012.

The third research focus in on extending existing data stream management systems for sensor data streams. First work in this area comprised an approach to use a data stream paradigm to perform statistical compression of massive amounts of remote sensing data in order to reduce the data sets but to preserve interesting characteristics within the data sets using a data stream based k-means algorithm (ICDE'04, SSDBM'04 and Journal of Computational and Graphical Statistics 2004).  Currently, we focus on real-time processing of massive amounts of distributed sensor streams as found in crowd sensing applications over large metropolitan areas. Thereby, we focus on supporting continuous environmental phenomena with a high-;level abstract dynamic spatial field data model and query language. Furthmore, we investigate efficient data stream operator frameworks to achieve near real-time spatial interpolation of up 10 250,000 updates per second. 

For more information see the following publications:

S. Nittel, and Leung, K.:Parallelizing Clustering of Geoscientific Data Sets using Data Streams, International Conference on "Scientific and Statistical Data Base Management" (SSDBM), Santorini, Greece, June, 2004.

A. Braverman, E. Fetzer, A. Eldering, S. Nittel, K. Leung: Semi-Streaming Quantization for Remote-Sensing Data Journal of Computational and Graphical Statistics, Special Issue on Massive Data Streams, Volume 12 Number 4, Issue Dec 2003.

S. Nittel, Q. Liang and J.C. Whittier. Real-time Spatial Interpolation of Continuous Environmental Phenomena using Mobile Sensor Data Streams, accepted for publication as short paper, SIGSPATIAL 2012


This research has been  funded with grants via a NSF Early CAREER award (2005-2010), a NSF grant on "Monitoring Dynamic Spatial Fields using adaptive geosensor networks" (2006-2009) and a NSF IGERT in the area of "Sensor Science, Engineering and Informatics" (2005-2010). Dr. Nittel also served on the Executive Committee of the Sensor IGERT program (2005-2011).. 

Dr. Nittel is the lead of the Geosensor Networks Laboratory at the School of Computing and Information Science at the University of Maine.  She currently adves 3 Ph.D. students, and 1 MS student.

updated Sept 2012