A Deep Learning Model Implementation Based on Rssi Fingerprinting for Lora-Based Indoor Localization

Abstract

LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization

A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

“…Perkovic et al have introduced an ML model based on a Neural Network (NN) for accurate indoor localization of EDs using beacon signals received by multiple gateways. Building on this, Anjum et al [128] have investigated ML techniques for RSSI-driven ranging in Lo-RaWAN, whereas another study proposed an approach called DeeoFi-LoRaIN [129] for LoRa indoor localization through DL-based fingerprint data. In addition, Carrino et al [130] have proposed a fingerprinting approach for outdoor geolocation, and another work addressed TDoA positioning errors with a DNN model [131].…”

Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

“…It estimates the mean localization accuracy and energy usage by fingerprinting RSSI-based localization using a ML technique. M. T. Hoang et al [18] use ML with neural network architecture to achieve excellent RSSI localization accuracy. Up to 98% of the device's accuracy can be estimated.…”