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- W2005241496 abstract "Sensor networks represent an important component of distributed infrastructure supplying raw data to various applications from military to healthcare. A key challenge is cost-efficient collection of distributed data streaming from those sensor networks. In this paper we propose the use of mobile data collectors that employ K-NN queries as a cost-efficient approach to collect data within the sensor network. We investigate a 3Dsensor network and propose a cost-efficient 3D-KNN algorithm that uses minimal energy and communication overheads to compute k-nearest neighbors. The 3D-KNN algorithm uses a 3dimensional plane rotation algorithm that maps sensor nodes on a 3D plane to a reference plane identified by the mobile data collector We propose a cost-efficient KNN boundary estimation algorithm that computes KNN boundary based on network density We also propose a neighbor prediction algorithm that uses distance, signal to noise ratio and mobile data collector’strajectory information to identify sensor nodes along the mobile data collector’s path. We simulate the proposed 3D-KNN algorithm using GlomoSim and validate its cost efficiency by evaluating its energy efficiency and query latency. Lessons and results of extensive simulation conclude the paper." @default.
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- W2005241496 date "2010-01-01" @default.
- W2005241496 modified "2023-09-24" @default.
- W2005241496 title "Cost-Efficient Data Collection Approach Using K-Nearest Neighbors in a 3D Sensor Network" @default.
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- W2005241496 doi "https://doi.org/10.1109/mdm.2010.59" @default.
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