Toward More Efficient WMSN Data Search Combined FJLT Dimension Expansion With PCA Dimension Reduction

Abstract

With the rapid development of 5G technology, the scales and dimensions of the data that are processed by Wireless Multimedia Sensor Network (WMSN) applications will be larger than ever before. Such high-dimensional data search becomes very difficult for WMSN applications. This paper proposes a more efficient WMSN data search algorithm that is based on the fruit fly olfactory neural framework, combined with the Fast Johnson-Lindenstrauss Transform (FJLT) and the Principal Component Analysis (PCA), called Fast Johnson-Lindenstrauss Transform Combine Principal Component Analysis-based Fly Locality-Sensitive Hashing (FP-FLSH). First of all, the data features are quantified numerically. Then, the fruit fly olfactory nervous system framework is used to project the data to a higher dimensional metric space using the low distortion projection FJLT. Finally, the dimensionality reduction process adopts PCA strategy to retain the maximum amount of information, and constructs its search index structure. Experiments are conducted on three larger scale benchmark data sets, and the results are as follows. Compared with the current mainstream search algorithms, the proposed method exhibits more efficient performance and can be effectively applied to WMSN applications.

Keywords

• Fruit fly olfactory neural framework
• fast Johnson-Lindenstrauss transform (FJLT)
• principal component analysis (PCA)
• wireless multimedia sensor network~(WMSN) data search