A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach

Yin, Yuqing; Song, Changze; Li, Ming; Niu, Qiang

doi:10.3390/s19132998

Cited by 11 publications

(9 citation statements)

References 30 publications

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“…Such method is top-down Deep Q-Network in [5] having accuracy of 75%. Another research uses Deep Neural Network and have 95% accuracy in empty locations [2].…”

Section: Discussionmentioning

confidence: 99%

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Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison

Vidya¹,

Sakthidharan²

2021

E3S Web Conf.

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Internet of Things connects various physical objects and form a network to do the services for sensing the physical things without any human intervention. They compute the data, retrieve the data by the network connections made through IoT device components such as Sensors, Protocols, Address, etc., The Global Positioning System (GPS) is used for localization in outer areas such as roads, and ground but cannot be used for Indoor environment. So, while using Indoor Environment, finding or locating an object is not possible by GPS. Therefore by using IoT devices such as Wi-Fi routers in Indoor Environment can localize the objects. It can be done by using Received Signal Strengths (RSSs) from a Wi-Fi router. But by using RSSs in Wi-Fi, there are disturbances, reflections, interferences are caused. By using Outlier detection techniques for localization can identify the objects clearly without any interruptions, noises, and irregular signal strengths. This paper produces research about Indoor Situating Environment and various techniques already used for localization and form the effective solution. The several methods used are compared and form a result to make the further computation in the Indoor Environment. The Comparison is done in order to find the effective and more accurate Machine Learning algorithms used for Indoor Localization.

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“…Such method is top-down Deep Q-Network in [5] having accuracy of 75%. Another research uses Deep Neural Network and have 95% accuracy in empty locations [2].…”

Section: Discussionmentioning

confidence: 99%

“…As a result of area is split into many little regions, classification is used to locate them. As a result, the [2] paper recommends a completely unique localization algorithmic rule based on Deep Neural Networks (DNN) and a multi-model integration technique. There are three steps to the strategy.…”

Section: Methodsmentioning

confidence: 99%

Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison

Vidya¹,

Sakthidharan²

2021

E3S Web Conf.

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“…For simplicity, we selected CSI amplitude as the data feature. The traditional localization method based on CSI-MIMO requires multiple APs or terminals to construct the MIMO system [ 29 , 30 , 31 ]. In SICD, we use a single AP and terminal with multiple antennas to construct MIMO system.…”

Section: System Modelmentioning

confidence: 99%

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Zhang

Wang

et al. 2021

Sensors

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With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.

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“…The use of CSI was also followed by researchers [23], but by using an integrated approach. The approach consists of three stages.…”

Section: Related Workmentioning

confidence: 99%

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

2021

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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

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A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach

Cited by 11 publications

References 30 publications

Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison

Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

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

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