M2M-based Anticipatory Reasoning for Contexts and Services
The goal of this project is to build a context-aware home energy saving system using existing M2M infrastructure that reduces power consumption in a home without compromising on user comfort, in a single- or multi-user environment.
PI: Prof. Li-Chen Fu Co-PI: Prof. 吳兆麟
Champion: Dr. Charlie Tai
Most of traditional energy saving systems often need user interventions to manually preset some static energy-saving rules via not readily accessible interfaces. More importantly, these monolithic systems often fail to take users’ contexts and preferences into account. Therefore, we employ the M2M Infrastructure, which allows cost-effective integration and fast deployment of vast amount of remote devices, to achieve context-aware energy-saving and to minimize user's interventions in the determination of energy saving policies. The overall system architecture is illustrated as below. In this system, there are three major components, which are Distributed Hybrid Context (DHC) Inference Engine, Collaborative Energy-Saving (CES) Decision Support Engine, and Ambient Universal Control Interface (AUCI). Unlike the existing approaches often ignoring the context information such as users’ situations and preferences, the DHC Inference Engine infers users’ activities, preferences, and PMV (Predicted Mean Vote) value through techniques of activity recognition (AR), preference recognition (PR), and PMV evaluation; the system then aggregates them into useful context data. With the context data inferred by the DHC Inference Engine and environment factors extracted from environment, the CES Decision Support Engine deals with conflicts between users’ preferences and energy saving policies. That is, the CES Decision Support Engine will provide recommendations to user who selects his/her preferable policy concerning power saving or comfort level based on that policy while taking into accounts all the extractable factors. Finally, we propose a decentralized and friendly control interface called AUCI to present environment or system information in a coherent way to help users to quickly control/configure multiple devices and provide comprehensive device information via readily accessible interfaces so that efforts for human intervention can be minimized. Besides, AUCI also acts as a bridge between internal networks and cloud services, which will further facilitate service innovation for energy saving.
Fig. 1. Our smart home environment as one testbed for energy-saving evaluationsMembers
Publications
C. Wu et al., "Anticipatory Reasoning for a Proactive Context-Aware Energy Saving System", in 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), pp. 228-234.
W. Chen et al., "An Efficient Data Storage Method of NoSQL Database for HEM Mobile Applications in IoT", in 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), pp. 336-339.
C. Lu et al., "Energy-Responsive Aggregate Context for Energy Saving in a Multi-Resident Environment", IEEE Transactions on Automation Science and Engineering, vol. 11, no. 3, 2014, pp. 715-729.
C. Lu et al., "Hybrid User-Assisted Incremental Model Adaptation for Activity Recognition in a Dynamic Smart-Home Environment", IEEE Transactions on Human-Machine Systems, vol. 43, no. 5, 2013, pp. 421-436.
C. Wu, Y. Tseng and L. Fu, "Spatio-temporal Feature Enhanced Semi-supervised Adaptation for Activity Recognition in IoT-Based Context-Aware Smart Homes", in 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 460-467.
M. Weng et al., "Context-aware home energy saving based on Energy-Prone Context", in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5233-5238.
C. Wu et al., "Hierarchical generalized context inference or context-aware smart homes", in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5227-5232.
Y. Chiao, C. Lu and P. Liu, "First come, First served: Enhancing the Convenience Store Service Experience", International Journal of Automation and Smart Technology, vol. 2, no. 3, 2012.
C. Lu, L. Fu, "Enhancement of Human-Preference Assisted Activity Recognition Using a Cooperative ADL Infrastructure", in 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing, pp. 1009-1014.
C. Liao, H. Cheng and L. Fu, "Unifiable Preference Expressions for Pervasive Service Composition", in 2011 IEEE Asia-Pacific Services Computing Conference, pp. 424-431.
國立臺灣大學
