Zone-Aware Service Platform: A New Concept of Context-Aware Networking and Communications for Smart-Home Sustainability

Abstract

Recent advances in networking and communications removed the restrictions of time and space in information services. Context-aware service systems can support the predefined services in accordance with user requests regardless of time and space. However, due to their architectural limitations, the recent systems are not so flexible to provide device-independent services by multiple service providers. Recently, researchers have focused on a new service paradigm characterized by high mobility, service continuity, and green characteristics. In line with these efforts, improved context-aware service platforms have been suggested to make the platform possible to manage the contexts to provide the adaptive services for multi-user and locations. However, this platform can only support limited continuity and mobility. In other words, the existing system cannot support seamless service provision among different service providers with respect to the changes of mobility, situation, device, and network. Furthermore, the existing context-aware service platform is significant reliance on always-on infrastructure, which leads to great amounts of energy consumption inevitably. Therefore, we subsequently propose a new concept of context-aware networking and communications, namely a zone-aware service platform. The proposed platform autonomously reconfigures the infrastructure and maintains a service session interacting with the middleware to support cost-and energy-efficient pervasive services for smart-home sustainability.

Application of Affordance Factors for User-Centered Smart Homes: A Case Study Approach

“…Studies have been conducted on recognizing users' movements, locations, and behavioral patterns using sensors and cameras [9][10][11][12][13], or using voice control or remote control for smart homes based on the IoT and cloud computing [14][15][16][17]. Research has also been conducted on methods for predicting and providing services to users by collecting information about context, using methods such as machine learning [18][19][20][21]. Furthermore, a number of studies have proposed 2 of 23 energy use management systems to increase energy efficiency in smart homes [22][23][24][25][26] or to provide healthcare services remotely by collecting and monitoring residents' movements and physiological signals [27][28][29][30][31].…”