Transferring activity recognition models in FOG computing architecture

Abstract A major focus of research in the field of in-home activity recognition (AR) and home automation (HA) is the ability to transfer data models to other homes for the purpose of applying new services, annotating classified data, and generating datasets due to lack of training ones. The wide spread of fog computing as an architecture for organizing edge devices in Internet-of-Things (IoT) systems lends support to the sharing of different environmental characteristics between different fogs (smart homes). In this paper, we propose a framework that serves the transfer of data models between different smart homes in a bid to overcome the lack of training data, which prevents the development of high-performance models that utilize fog computing characteristics. Our technique incorporates the sharing of environmental characteristics (by Fogs) in order to analyze the data features at the source and target smart homes. The features, then, are mapped onto each other using a fusion method that guarantees to keep the variations between different homes by reducing the divergence between them. The hidden Markov model has also been applied in order to model activities at target homes. Three experiments have been conducted to measure the performance of the proposed framework: first, against the accuracy of feature-mapping techniques; second, measuring the performance of classifying data at target homes; and, third, the ability of the proposed framework to function well due to noise data. The results show promising indicators and highlight the limitations of the proposed methodology.