Multimedia Sensor Networks(MSNs)have enhanced the ability to analyze the environment and provide responses based on its current status.Generally,MSNs are composed of scalar and multimedia sensors that have fixed locat...Multimedia Sensor Networks(MSNs)have enhanced the ability to analyze the environment and provide responses based on its current status.Generally,MSNs are composed of scalar and multimedia sensors that have fixed locations.However,given the advancement of smart mobile device technologies,it is currently possible to dynamically integrate mobile sensors into MSNs.In this paper,we propose a formal platform to manage MSNs and the data gathered from them to detect complex events.Our main contributions include:M^(2)SSN-Onto,a Mobile and Multimedia Semantic Sensor Networks Ontology;Py-CEMiD,an engine for detecting complex events and generate reactions to them;a mobile device location engine to locate mobile sensors;and a proof-of-concept in the context of detecting emergency situations in smart buildings.Several scenarios are validated for emergency events,combining simulated sensor measurements with real measurements of mobile devices.Results show complex events can be detected in near real time(less than 1 s).展开更多
语义社会网络(Semantic social network,SSN)是一种由信息节点及社会关系构成的复杂网络,也是语义信息时代社会网络技术研究的热点,相较于传统社会网络更具实用价值.其研究内容包含了社会网络的语义分析及社会关系分析,因此,语义社会网...语义社会网络(Semantic social network,SSN)是一种由信息节点及社会关系构成的复杂网络,也是语义信息时代社会网络技术研究的热点,相较于传统社会网络更具实用价值.其研究内容包含了社会网络的语义分析及社会关系分析,因此,语义社会网络的社区挖掘建模具有一定的复杂性.在语义社会网络的社区挖掘研究方面,本文分析了当前基于话题概率模型的语义社区发现方法,并在综述其内容的同时总结了各方法的优缺点,为后续研究提供了理论基础.在语义社会网络社区挖掘结果的评判方面,本文归纳了相关的评价模型,并通过实验分析对比了各模型对拓扑相关性和语义相关性的倾向性.展开更多
文摘Multimedia Sensor Networks(MSNs)have enhanced the ability to analyze the environment and provide responses based on its current status.Generally,MSNs are composed of scalar and multimedia sensors that have fixed locations.However,given the advancement of smart mobile device technologies,it is currently possible to dynamically integrate mobile sensors into MSNs.In this paper,we propose a formal platform to manage MSNs and the data gathered from them to detect complex events.Our main contributions include:M^(2)SSN-Onto,a Mobile and Multimedia Semantic Sensor Networks Ontology;Py-CEMiD,an engine for detecting complex events and generate reactions to them;a mobile device location engine to locate mobile sensors;and a proof-of-concept in the context of detecting emergency situations in smart buildings.Several scenarios are validated for emergency events,combining simulated sensor measurements with real measurements of mobile devices.Results show complex events can be detected in near real time(less than 1 s).
文摘语义社会网络(Semantic social network,SSN)是一种由信息节点及社会关系构成的复杂网络,也是语义信息时代社会网络技术研究的热点,相较于传统社会网络更具实用价值.其研究内容包含了社会网络的语义分析及社会关系分析,因此,语义社会网络的社区挖掘建模具有一定的复杂性.在语义社会网络的社区挖掘研究方面,本文分析了当前基于话题概率模型的语义社区发现方法,并在综述其内容的同时总结了各方法的优缺点,为后续研究提供了理论基础.在语义社会网络社区挖掘结果的评判方面,本文归纳了相关的评价模型,并通过实验分析对比了各模型对拓扑相关性和语义相关性的倾向性.
