ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Research issues in data stream association rule mining
ACM SIGMOD Record
Computational Geometry: Theory and Applications
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Counting birds with wireless sensor networks
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Acoustic counting algorithms for wireless sensor networks
Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Spatio-temporal association rule mining framework for real-time sensor network applications
Proceedings of the 18th ACM conference on Information and knowledge management
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Hi-index | 0.00 |
More and more animal species are endangered every day on earth. In order to study their adaptation to world and climate change and their chances of survival, numerous initiatives have been taken that mostly need human intrusion into animal communities. Today mobile devices enable researchers to go beyond this limit. In this paper, we propose an original solution that consists on a new framework for detecting individual songs in a bird population and identifying remotely by this way their collective behavior in movements without human interaction. Movement patterns are elicited by analyzing data collected via wireless sensors fitted with microphone. Whereas similar methods use mobile devices fitted on some specimens, we rather propose fixed sensors. We demonstrate that this solution provides a good answer to technical constraints assessed by the context and we discuss results of experimental simulations that allow to define optimized parameters for the architecture to be set up on the ground. Experimental results are provided and show the relative impact of different parameters such as the number of sensors or the population size on the detection rate.