An Optimized K-Nearest Neighbor Algorithm for Extending Wireless Sensor Network Lifetime

被引：6

作者：

Ahmed, Mohammed M. ^{[1
,3
]}

Taha, Ayman ^{[2
]}

Hassanien, Aboul Ella ^{[2
,3
]}

Hassanien, Ehab ^{[2
]}

机构：

[1] Minia Univ, Fac Comp & Informat, Al Minya, Egypt

[2] Cairo Univ, Fac Comp & Informat, Giza, Egypt

[3] Sci Res Grp Egypt SRGE, Giza, Egypt

来源：

INTERNATIONAL CONFERENCE ON ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS (AMLTA2018) | 2018年 / 723卷

关键词：

K-nearest neighbor algorithm; Energy-efficient; Classification; Wireless sensor networks; Swarm optimization;

D O I：

10.1007/978-3-319-74690-6_50

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an optimized K-nearest neighbors (KNNs) classification algorithm using the metaheuristic whale optimization to searches for sink node in wireless sensor networks. Sink node aggregate data from all sensor nodes and reducing the energy consumption network to prolong network lifetime. To reach aforementioned, a fitness function has formulated to choose the best location of sink node with high residual neighbor's sensor nodes energy to leads to maximizing the network lifetime. Eventually, the experimental results have been conducted whereas sensor nodes are propagated in a random location within the desired network area. The system has 11% improvement on the network's energy consumption that increases the lifetime of the network.

引用

页码：506 / 515

页数：10

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