{"title":"An Explainable Artificial Intelligence empowered energy efficient indoor localization framework for smart buildings","authors":"Zeynep Turgut","doi":"10.1016/j.iot.2025.101586","DOIUrl":null,"url":null,"abstract":"<div><div>The indoor localization problem remains a prominent and extensively debated area of research, lacking a universally accepted solution, especially within the context of smart buildings. A major concern revolves around the energy consumption associated with indoor localization systems. This study presents a proposed framework for an energy-efficient indoor localization system designed for smart buildings. The approach focuses on a fingerprinting indoor localization technique that involves constructing a signal map. To address challenges arising from distinct signal effects and the environment-specific structure of signal maps, the study introduces a framework incorporating an adaptive filter selection scheme. This scheme includes Kalman, particle, and Savitzky–Golay filters in the pre-processing stage to enhance the signal map. Rather than resorting to additional hardware for improved localization accuracy, the study advocates for optimizing the signal map to minimize energy consumption. Additionally, the research emphasizes the selection of effective features for machine learning techniques to enhance performance and boost localization accuracy. The findings are subjected to analysis using Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) Explainable Artificial Intelligence (XAI) models. The investigation delves into the impact of each signal and filter on positioning estimation, providing a comprehensive understanding of the system’s functionality.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101586"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S254266052500099X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The indoor localization problem remains a prominent and extensively debated area of research, lacking a universally accepted solution, especially within the context of smart buildings. A major concern revolves around the energy consumption associated with indoor localization systems. This study presents a proposed framework for an energy-efficient indoor localization system designed for smart buildings. The approach focuses on a fingerprinting indoor localization technique that involves constructing a signal map. To address challenges arising from distinct signal effects and the environment-specific structure of signal maps, the study introduces a framework incorporating an adaptive filter selection scheme. This scheme includes Kalman, particle, and Savitzky–Golay filters in the pre-processing stage to enhance the signal map. Rather than resorting to additional hardware for improved localization accuracy, the study advocates for optimizing the signal map to minimize energy consumption. Additionally, the research emphasizes the selection of effective features for machine learning techniques to enhance performance and boost localization accuracy. The findings are subjected to analysis using Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) Explainable Artificial Intelligence (XAI) models. The investigation delves into the impact of each signal and filter on positioning estimation, providing a comprehensive understanding of the system’s functionality.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.