An Experimental EACI-Based Localization Framework Using LQI and CNN for Consumer IoT
Date
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Precise indoor localization remains a challenge in wireless sensor networks (WSNs) due to multipath fading, interference, and signal fluctuations in different environments. Traditional methods depend on Received Signal Strength (RSS) also often struggle with accuracy in indoor scenario. This study presents an experimental localization framework that utilizes Link Quality Indicator (LQI) values and Convolutional Neural Networks (CNNs) within an Edge Computing-Assisted Consumer IoT (EACI) model. The proposed approach segments the network using a pyramid-loop algorithm and employs LQI-based measurements for more stable and accurate distance estimation. A CNN classifier is trained on normalized LQI data, including statistical features such as kurtosis, to predict node locations. The system is authenticated by a real-world testbed using Zigbee XB24C nodes. The experimental results show an overall localization error of 0.12m at zone 1 with a standard deviation of 0.89m. This reflects an improved localization accuracy and reduced error compared to RSS-based and existing CNN-based methods. The proposed technique effectiveness is observed for indoor localization in consumer IoT environments.Description
Keywords
40 Engineering, 4008 Electrical Engineering, 4009 Electronics, Sensors and Digital Hardware, Networking and Information Technology R&D (NITRD), 0906 Electrical and Electronic Engineering, 1005 Communications Technologies, Networking & Telecommunications, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware
Source
IEEE Transactions on Consumer Electronics, ISSN: 0098-3063 (Print); 1558-4127 (Online), Institute of Electrical and Electronics Engineers (IEEE), PP(99), 1-1. doi: 10.1109/TCE.2025.3631740
Publisher's version
Rights statement
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.