2025/05/08 by Diego Corrêa da Silva, Denis Robson Dantas Boaventura, Mayki dos Santos Oliveira +14 · 3 citations
Computer Science · Engineering · Social Sciences · #Artificial intelligence #Computer science #Context (archaeology) #Data Stream Mining Techniques #Data mining #Data pre-processing #Embedded system #Home automation #Human Mobility and Location-Based Analysis #Internet of Things #Machine learning #Preprocessor #Process (computing) #Smart Grid Energy Management #Smart environment
paper · pdf · doi:10.1002/spe.3428
published in Software Practice and Experience 55(9), 1427-1444 (Wiley)
openalex publication_date 2025/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/05/21
ABSTRACT Context Smart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home profile”. To comprehend the complete IoT ecosystem dynamics of an intelligent environment, it is necessary to learn from each IoT device to predict its status in the future time. Nevertheless, dealing with real‐world IoT data structure requires considerable preprocessing tasks and the employment of classifiers that can learn multiple IoT inputs from a single IoT message. Objective Aware of these challenges, this paper proposes a novel methodology to process multi‐label IoT data and provide a comprehensive comparison of multi‐label classifiers for forecasting the status of smart devices, considering their efficiency and accuracy. Method We propose a data transformation method to preprocess the IoT data to be used by multi‐label classifiers. This method is based on real data structure. Results We evaluate our proposal in two real‐world scenarios and various multi‐label classifiers. The promising findings indicate that efficient classifiers can generate many correct predictions for a comprehensive IoT ecosystem in a small fraction of a second. Conclusions Our proposed data transformation can fit the context of prediction to smart homes and work with multi‐label classifiers to understand user behavior.