Big Data Analytic based on Scalable PANFIS for RFID Localization

TL;DR

This paper introduces Scalable PANFIS, a distributed big data analytics framework for RFID localization that efficiently processes non-stationary data streams with high speed and maintains high classification accuracy.

Contribution

The paper presents a novel scalable, distributed algorithm based on PANFIS capable of learning from big RFID data streams with rule merging to reduce redundancy.

Findings

Scalable PANFIS is over 20 times faster than single PANFIS.

It maintains high classification accuracy of 96.67%.

It outperforms some Spark MLlib algorithms in RFID data classification.

Abstract

RFID technology has gained popularity to address localization problem in the manufacturing shopfloor due to its affordability and easiness in deployment. This technology is used to track the manufacturing object location to increase the production efficiency. However, the data used for localization task is not easy to analyze because it is generated from the non-stationary environment. It also continuously arrive over time and yields the large-volume of data. Therefore, an advanced big data analytic is required to overcome this problem. We propose a distributed big data analytic framework based on PANFIS (Scalable PANFIS), where PANFIS is an evolving algorithm which has capability to learn data stream in the single pass mode. Scalable PANFIS can learn big data stream by processing many chunks/partitions of data stream. Scalable PANFIS is also equipped with rule structure merging to eliminate the redundancy among rules. Scalable PANFIS is validated by measuring its performance against single PANFIS and other Spark scalable machine learning algorithms. The result shows that Scalable PANFIS performs running time more than 20 times faster than single PANFIS. The rule merging process in Scalable PANFIS shows that there is no significant reduction of accuracy in classification task with 96.67 percent of accuracy in comparison with single PANFIS of 98.71 percent. Scalable PANFIS also generally outperforms some Spark MLib machine learnings to classify RFID data with the comparable speed in running time.