k-Nearest Neighbor Search based on Node Density in MANETs | Zendy

Yuka Komai | Zendy; Yuya Sasaki | Zendy; Takahiro Hara | Zendy; Shojiro Nishio | Zendy

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k-Nearest Neighbor Search based on Node Density in MANETs

Author(s) -

Yuka Komai,

Yuya Sasaki,

Takahiro Hara,

Shojiro Nishio

Publication year - 2014

Publication title -

mobile information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.346

H-Index - 34

eISSN - 1875-905X

pISSN - 1574-017X

DOI - 10.1155/2014/158737

Subject(s) - computer science , node (physics) , k nearest neighbors algorithm , data mining , range (aeronautics) , range query (database) , point (geometry) , mobile ad hoc network , search engine , information retrieval , sargable , computer network , artificial intelligence , web search query , mathematics , materials science , geometry , structural engineering , network packet , engineering , composite material

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

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