A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases - Special Emphasis on Machine Learning and Deep Learning Models

Aug 4, 2026

A comprehensive review examines how Global Navigation Satellite System (GNSS) technologies integrate with Internet of Things (IoT) applications through real-world use cases, with special focus on machine learning (ML) and deep learning (DL) models. The review highlights the role of rapidly growing GNSS data produced by interconnected low-power devices, enabling ML/DL-driven solutions for navigation. Key GNSS challenges in dense urban areas—multipath effects, signal outages, and communication link loss—are addressed alongside the advantages and limitations of ML-based approaches for accurate, seamless positioning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

GNSS overview and major systems

IoT evolution and data growth

Integration of GNSS with IoT applications

ML and DL for GNSS intelligence

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models Komal Agarwal1, Dr. Bharati Ainapure2, Dr. Ashish Shukla3 1Research Scholar, Vishwakarma University Pune, India [komalgbansal@gmail.com](komalgbansal@gmail.com) 2Associate Professor, Vishwakarma University Pune, India [bharati.ainapure@vupune.ac.in](bharati.ainapure@vupune.ac.in) 3Scientist/Engineer-SG, Space Applications Center, ISRO, Ahmedabad, India [ashishs@sac.isro.gov.in](ashishs@sac.isro.gov.in) Abstract— This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and lowpower processing technologies-which is majorly due to the evolution ofIoT-have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT. Keywords- GNSS, GPS, IoT, RTK, machine learning, deep learning. I. INTRODUCTION Global Navigation Satellite System (GNSS) is a satellite navigation system that provides autonomous geospatial position, navigation, and timing services with regional or global coverage [1] . It encompasses popular systems like Global Positioning System (GPS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema in Russian (GLONASS), Galileo, Beidou, Indian Regional Navigation Satellite System (IRNSS), and other regional and augmented navigation systems [2] . Over the past two decades, GNSS technology has rapidly advanced, becoming more user-friendly and intuitive, enabling wireless connectivity with low power consumption. Its integration with smartphones and other mass-market devices has made it one of the most widely used technologies today [3][4] . On the other hand, the rise of the Internet of Things (IoT) has brought an evolution in applications and devices, enhancing connectivity and interoperability. This has resulted in an exponential increase in the volume of data collected by these interconnected devices [5] . As per the facts, the production of global data is projected to grow from 33 zettabytes in 2018 to 175 zettabytes by 2025. While data centers currently handle 80% of data processing and analysis, the remaining 20% is managed by other computing facilities, smart connected objects, and IoT devices. Compared to GNSS, which provides specific hardware or object positions, IoT focuses on monitoring and providing realtime information and statistics on device operations. The combination of GNSS and IoT enables the generation of comprehensive and usable interconnected data, paving the way for smarter cities, self-driving cars, tracking devices, and wearable health technologies [6] . Improvements in the position accuracy of IoT devices like smartphones and wearables will expand their usage in the mass market and unlock new consumer applications [7] . The integration of GNSS with IoT has led to the deployment of numerous IoT GNSS receivers, including smartphones, and the establishment of permanent GNSS stations worl

FAQs

What is the purpose of combining GNSS with IoT in navigation systems?

The combination generates comprehensive interconnected data that supports smarter cities, self-driving vehicles, tracking devices, and wearable health technologies, improving real-time monitoring and positioning services.

Why do ML and DL models become important for GNSS with IoT data?

GNSS time-series data can be used to uncover time-dependent trends and correlations, enabling classification, prediction, and optimization tasks that enhance accuracy, continuity, and robustness.

What major challenges hinder GNSS navigation in crowded environments?

Multipath, reduced satellite visibility leading to signal outages, and lost communication links are major factors that degrade navigation performance in dense cities and forests.

Aug 4, 2026

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