Investigation of Features for Classification RFID Reading Between Two RFID Reader in Various Support Vector Machine Kernel Function

Chun Sern Choong*, Ahmad Fakhri Ahmad, Anwar P.P. Abdul Majeed, Muhammad Aizzat Zakaria, Mohd Azraai Mohd Razman

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Radio Frequency Identification (RFID) is the primary technology for tripartite logistics information and automation. The RFID-based logistics system able to increase logistic operating capacity and improve the efficiency of worker to minimize the logistic operation failure. However, the precise location of the RFID device is still a problem in a specific area due to the interference of the radiofrequency. An indoor positioning using RFID technology based on various kernel function of the support vector machine (SVM), and feature extraction are proposed to determine the location of the goods. SVM classifier is utilized the acquire received signal strength indicator (RSSI) data for trained the model from the indoor moving objects as well as relationship between RSSI and distance is constructed to boost RSSI accuracy. Instead, the distance verses RSSI algorithm is used to determine the objects to be located based on the distance of the tag to be located to each reader. The feature of RSSI is extracted to nine single statistical features and three combinations of different statistical features for evaluated the classification performance in different kernel functions of the SVM classifier. The Polynomial-SVM model is capable of delivering a classification accuracy of 84.81 and 20.00% of the error rate in test data by using the function MIN extracted. The experimental results show that the algorithm improves the positioning accuracy of indoor localization with select the suitable feature combination.

Original languageEnglish
Title of host publicationRecent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
EditorsAhmad Fakhri Ab. Nasir, Ahmad Najmuddin Ibrahim, Ismayuzri Ishak, Nafrizuan Mat Yahya, Muhammad Aizzat Zakaria, Anwar P. P. Abdul Majeed
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789813345966
Publication statusPublished - 2022
Externally publishedYes
EventInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 - Gambang, Malaysia
Duration: 6 Aug 20206 Aug 2020

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020


  • Feature extraction
  • Kernel function
  • Radio frequency identification
  • Support vector machine


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