Intelligent RF-Based Indoor Localization through RSSI of LoRa Communication Technology

Abstract

Requirements on high-quality indoor localization approaches and the increase in ubiquitous computing and context-dependent have led to an emphasis on a continuous search for promising localization technologies and techniques. Typical RF-Based localization technologies such as Cellular, RFID, Bluetooth, Wi-Fi, Zigbee, and UWB have been widespread studied over the past decades. Recently, LoRa communication technology has suggested as a potential alternative to those of exiting wireless communication standards with low power consumption and low implementation costs. This paper therefore presents an indoor localization technique through the use of Received Signal Strength Indicator (RSSI) of LoRa Technology. The LoRa chip from SEMTECH was implemented on a compact board with built-in antenna. The Arduino microcontroller was employed as a core processor with a step-down switching regulator. Five sets of LoRa nodes were implemented and four of which were utilized as statistic nodes, radiating a signal power from 5meter high from the floor. The receiving node is placed in a particular coordinate on the floor. The RSS values were employed as inputs for Artificial Neural Network (ANN) for estimation of the coordinate of the receiving node. The accuracy was approximately 95%. The result provides satisfactory accuracy with cost-effective and low-power operation as for an alternative for large scales deployments of indoor localization.

Performance Evaluations of LoRa Wireless Communication in Building Environments

“…Numerous researches have studied the application of LoRa technology in SB under different scenarios, such as the safety monitoring [ 20 ], healthcare [ 21 , 22 , 23 ], power consumption monitoring [ 24 ], environmental quality monitoring [ 25 ], smart metering [ 26 ] and indoor personnel positioning [ 27 ].…”

“…A smart metering as developed based on the LoRa technology, which was applied to smart buildings and smart grid systems [ 26 ]. In [ 27 ], an indoor positioning technology was presented based on the RSSI intensity of LoRa. The measurement accuracy was further improved by ANN training and the positioning accuracy can reach 95%.…”

A Critical Review of the Propagation Models Employed in LoRa Systems

“…Studies on propagation have also proposed models of ray tracing model [ 69 , 73 ], the 3GPP indoor [ 32 ], the Indoor Dominant Path (IDP) [ 75 ], and linear decaying [ 76 ]. The works presented in [ 77 , 78 ] introduce machine learning techniques on LoRa indoor propagation studies.…”