In this paper, a new layered architecture is proposed for big data-driven processing and management of future smart homes. The proposed Representational State Transfer (REST)-based architecture includes seven layers: physical, fog-computing, network, cloud-computing, service, session, and application for efficient data exchange and processing tasks of future smart homes. The smart home physical layer includes all the sensing technologies and smart devices within the smart home, which monitors the home environment and its residents. The data of these sensors will be sent to the smart home fog-computing layer that can do limited data storage and processing. Then, all the required data will be sent to the cloud-computing layer using smart home network layer. The cloud-computing layer provides the scalable solution for data processing and storage. The processed data in the cloud-computing layer will be provided as the data-driven services to different smart home and third-party (e.g., smart city) applications via smart home service layer. Based on the proposed architecture, the applications will utilize the session layer and RESTFUL APIs to use the data-driven services of the smart home. The proposed smart home architecture can provide a ubiquitous and shared data environment as the key aspect of Internet-of-Things systems. INDEX TERMS Big data management, Internet of Things, smart home, layered architecture, data-driven services.
Smart home platforms show promising outcomes to provide a better quality of life for residents in their homes. One of the main challenges that exists with these platforms in multi-residential houses is activity labeling. As most of the activity sensors do not provide any information regarding the identity of the person who triggers them, it is difficult to label the sensor events in multi-residential smart homes. To deal with this challenge, individual localization in different areas can be a promising solution. The localization information can be used to automatically label the activity sensor data to individuals. Bluetooth low energy (BLE) is a promising technology for this application due to how easy it is to implement and its low energy footprint. In this approach, individuals wear a tag that broadcasts its unique identity (ID) in certain time intervals, while fixed scanners listen to the broadcasting packet to localize the tag and the individual. However, the localization accuracy of this method depends greatly on different settings of broadcasting signal strength, and the time interval of BLE tags. To achieve the best localization accuracy, this paper studies the impacts of different advertising time intervals and power levels, and proposes an efficient and applicable algorithm to select optimal value settings of BLE sensors. Moreover, it proposes an automatic activity labeling method, through integrating BLE localization information and ambient sensor data. The applicability and effectiveness of the proposed structure is also demonstrated in a real multi-resident smart home scenario.
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