Big Data Analytic Based on Scalable PANFIS for RFID Localization

RFID technology has gained popularity to address localization problem in the manufacturing shopfloor by tracking the manufacturing object location to increase the production's efficiency. However, the signals (data) used for localization task is not easy to analyze because it is generated from the nonstationary 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's 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.