RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities-Reference-Cited by-同舟云学术

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Published:2023-05-28 Issue:2 Volume:4 Page:1468-1494
ISSN:2673-4117
Container-title:Eng
language:en
Short-container-title:Eng

Author:

Rathnayake R. M. M. R.¹^ORCID,Maduranga Madduma Wellalage Pasan¹^ORCID,Tilwari Valmik²^ORCID,Dissanayake Maheshi B.³^ORCID

Affiliation:

1. Department of Computer Engineering, General Sir John Kotelawala Defence University, Ratmalana 10390, Sri Lanka

2. School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea

3. Department of Electrical and Electronics Engineering, University of Peradeniya, Kandy 20400, Sri Lanka

Abstract

The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Link

https://www.mdpi.com/2673-4117/4/2/85/pdf

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