文献类型：期刊文献

英文题名：A Semantic Approach with Decision Support for Safety Service in Smart Home Management

作者：Huang, Xiaoci[1,2];Yi, Jianjun[2];Zhu, Xiaomin[2];Chen, Shaoli[2]

机构：[1]Shanghai Univ Engn Sci, Shanghai 201620, Peoples R China;[2]East China Univ Sci & Technol, Dept Mech Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：16

期号：8

外文期刊名：SENSORS

收录：;EI(收录号：20163302704995);WOS:【SCI-EXPANDED(收录号:WOS:000382323200112)】；

基金：This paper was supported by the Research Foundation of Science and Technology Commission of Shanghai under Grant No. 10DZ1500200, the Natural Science Fund of China (NSFC) under Grant Nos. 50975088, 51275173, 51575186, and 51210105018, the Fundamental Research Funds for the Central Universities under Grant No. WH0913009, Shanghai Pujiang Program under Grant No. PJ201000353, and Shanghai Software and IC industry Development Special Fund under Grant No. 120493.

语种：英文

外文关键词：smart homes; decision; safety service

摘要：Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.