A Support Infrastructure for the Smart Kindergarten
IEEE Pervasive Computing
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Sensor network-based countersniper system
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
IEEE Communications Magazine
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The growing advance in wireless communications and electronics makes the development of low-cost and low-power sensors possible. These sensors are usually small in size and are able to communicate with other sensors in short distances wirelessly. A sensor network consists of a number of sensors which cooperates with one another to accomplish some tasks. In this paper, we address the problem of resource inventory applications, which means a class of applications involving population calculation of a specific species or object type. To reduce energy consumption, each sensor only reports the number of sensed objects to the server, and the server will estimate the object number according to the received reports of all sensors. To address this problem, we design in this paper a population estimation algorithm, called algorithm Estimation, to estimate the object numbers. Several experiments are conducted to measure the performance of algorithm Estimation. The experimental results show that algorithm Estimation is able to obtain closer approximations of object numbers than prior algorithms.