Abstract
With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency, and their environmental robustness is not strong enough. In this paper, we have introduced an RFID positioning algorithm based on the Glowworm Swarm Optimization (GSO) fused with semi-supervised online sequential extreme learning machine (SOS-ELM), which is called the GSOS-ELM algorithm. The GSOS-ELM algorithm automatically adjusts the regularization weights of the SOS-ELM algorithm through the GSO algorithm, so that it can quickly obtain the optimal regularization weights under different initial conditions; at the same time, the semi-supervised characteristics of the GSOS-ELM algorithm can significantly reduce the number of labeled reference tags and reduce the cost of positioning systems. In addition, the online learning phase of the GSOS-ELM algorithm can continuously update the system to perceive changes in the environment and resist the environmental interference. We have carried out experiments to study the influence factors and validate the performance, both the simulation and testbed experiment results show that compared with other algorithms, our proposed GSOS-ELM localization system can achieve more accurate positioning results and has certain adaptability to the changes of the environment.
Highlights
With the development of Internet of Things technology, people’s demand for applications has grown rapidly
We will present the semi-supervised online sequential extreme learning machine (SOS-ELM); we will introduce the glowworm swarm optimization (GSO) method; and we demonstrate our proposed radio frequency identification (RFID)-based indoor localization system using GSOS-ELM
The average error of our proposed GSOS-ELM method overcomes by a rate of 15.18%, 18.07% and 12.45% over NN-Based method, FA-OSELM method and NMDS
Summary
With the development of Internet of Things technology, people’s demand for applications has grown rapidly. Among these technologies, wireless location-aware technologies have shown great activity in both military and civilian applications. Wireless location-aware technologies and services play a more and more important role in people’s daily life. In outdoor location awareness technologies, Global Positioning System (GPS) [1] is the most famous and most representative of location sensing technology and is widely used in military and civilian applications. The demand for indoor location-aware applications is increasing, and there is great potential for indoor real-time and dynamic location-awareness needs. Due to the advantages of non-line-of-sight, non-contact and rapid identification, radio frequency identification (RFID) technology has become the preferred solution to indoor location-aware applications.
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