Wireless Positioning Network Location Prediction Based on Machine Learning Techniques

Al-Obaidy, Furat; Razfar, Najmeh; Moeini, Peyman; Mohammadi, Fazel

doi:10.1109/ccece47787.2020.9255727

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Furat et al [56] introduced wireless positioning network location prediction depends on machine learning technique. Behaviour in variation of RSSI signal unable to get tag in their range of reading.…”

Section: Review Based On Artificial Intelligence For Wsn Localizationmentioning

confidence: 99%

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Mehta,

Mahajan

2023

ijmst

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The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.

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Section: Review Based On Artificial Intelligence For Wsn Localizationmentioning

confidence: 99%

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Mehta,

Mahajan

2023

ijmst

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Location Prediction Using Bayesian Optimization LSTM for RIS-Assisted Wireless Communications

Hu,

Tian,

Kho

et al. 2024

IEEE Trans. Veh. Technol.

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Position Prediction in Space System for Vehicles Using Artificial Intelligence

et al. 2022

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This paper deals with the prediction of the future location of vehicles, which is attracting attention in the era of the fourth industrial revolution and is required in various fields, such as autonomous vehicles and smart city traffic management systems. Currently, vehicle traffic prediction models and accident prediction models are being tested in various places, and considerable progress is being made. However, there are always errors in positioning when using wireless sensors due to various variables, such as the appearance of various substances (water, metal) that occur in the space where radio waves exist. There have been various attempts to reduce the positioning error in such an Internet of Things environment, but there is no definitive method with confirmed performance. Of course, location prediction is also not accurate. In particular, since a vehicle moves rapidly in space, it is increasingly affected by changes in the environment. Firstly, it was necessary to develop a spatial positioning algorithm that can improve the positioning accuracy. Secondly, for the data generated by the positioning algorithm, a machine learning method suitable for position prediction was developed. Based on the above two developed algorithms, through experiments, we found a means to reduce the error of positioning through radio waves and to increase the accuracy of positioning. We started with the idea of changing the positioning space itself from a three-dimensional space into a two-dimensional one. With changes in the time and space of radio wave measurement, the location was measured by transforming the spatial dimension to cope with environmental changes. This is a technology that predicts a location through machine learning on time series data using a direction angle classification technique. An experiment was conducted to verify the performance of the proposed technology. As a result, the accuracy of positioning was improved, and the accuracy of location prediction increased in proportion to the learning time. It was possible to confirm the prediction accuracy increase of up to 80% with changes. Considering that the accuracy result for location prediction presented by other researchers is 70%, through this study, the result was improved by 10% compared to the existing vehicle location prediction accuracy. In conclusion, this paper presents a positioning algorithm and machine learning methodology for vehicle positioning. By proving its usefulness through experiments, this study provides other researchers with a new definition of space for predicting the location of a vehicle, and a machine learning method using direction angles.

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Wireless Positioning Network Location Prediction Based on Machine Learning Techniques

Cited by 3 publications

References 18 publications

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Location Prediction Using Bayesian Optimization LSTM for RIS-Assisted Wireless Communications

Position Prediction in Space System for Vehicles Using Artificial Intelligence

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