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Cost-efficient data collection approach using K-nearest neighbors in a 3D sensor network
Caulfield School of Information Technology, Monash University.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1990-5734
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-4133-3317
2010 (English)In: 11th IEEE International Conference on Mobile Data Management, MDM 2010, Piscataway, NJ: IEEE Communications Society, 2010, p. 183-188Conference paper, Published paper (Refereed)
Abstract [en]

Sensor networks represent an important component of distributed infrastructure supplying raw data to various applications from military to healthcare. A key challenge is costefficient 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 3D sensor 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 3 dimensional 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's trajectory 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

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2010. p. 183-188
National Category
Media and Communication Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Mobile and Pervasive Computing; Industrial Electronics
Identifiers
URN: urn:nbn:se:ltu:diva-30353DOI: 10.1109/MDM.2010.59Scopus ID: 2-s2.0-77955206112Local ID: 4219f7d0-ac30-11df-a707-000ea68e967bISBN: 978-1-4244-7075-4 (print)OAI: oai:DiVA.org:ltu-30353DiVA, id: diva2:1003580
Conference
International Conference on Mobile Data Management : 23/05/2010 - 26/05/2010
Note
Godkänd; 2010; 20100820 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved

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Zaslavsky, ArkadyDelsing, Jerker

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