A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments

TL;DR

This paper introduces a unified deep transfer learning model for IoT localization that effectively operates across diverse indoor and outdoor environments, reducing costs and complexity in smart city applications.

Contribution

It proposes a novel transfer learning-based deep model that supports accurate IoT localization in multiple environments with a single system.

Findings

Improved localization accuracy by 17.18% indoors

Enhanced outdoor localization by 9.79%

Unified model reduces costs and complexity

Abstract

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about 17.18% in indoor environments and 9.79% in outdoor environments.