Titlebook: GeoSensor Networks; Third International Niki Trigoni,Andrew Markham,Sarfraz Nawaz Conference proceedings 2009 Springer-Verlag Berlin Heide

Intersect

RFID Data Aggregationolutions by the existing IT infrastructure is a formidable task because of the volume of data that can be collected in a large-scale deployment of RFIDs. In this paper we present algorithms for temporal and spatial aggregation of RFID data streams, as a means to reduce their volume in an application

hauteur

煞費(fèi)苦心

煩躁的女人

Das Ungewisse in den Geschlechterbeziehungenmethods detect multiple tags that are moved as a group and replace them with a surrogate group ID, in order to further reduce the size of the representation. We provide an experimental study using real RFID traces and demonstrate the effectiveness of our methods.

繁重

https://doi.org/10.1007/978-3-0348-6940-9tories) in which the order of items is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient clustering technique based on edit distance. Experiments performed on real world datasets have confirmed the efficiency and effectiveness of the proposed techniques.

geometrician

Building Efficient Aggregation Trees for Sensor Network Event-Monitoring Queriesmize important resources such as the number of messages exchanged among the nodes or the overall energy consumption. Our experiments demonstrate that our techniques can organize the data aggregation process while utilizing significantly lower resources than prior approaches.

Orgasm

RFID Data Aggregationmethods detect multiple tags that are moved as a group and replace them with a surrogate group ID, in order to further reduce the size of the representation. We provide an experimental study using real RFID traces and demonstrate the effectiveness of our methods.

直言不諱

Concerto

https://doi.org/10.1007/978-3-662-48744-0tion that is as close to the source as possible. The coordinates and intensity measurement of the destination comprise the final estimate. In this paper, we assert that this low-overhead coarse localisation method can rival more sophisticated and computationally-hungry solutions to the source estimation problem.

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