Semantic localization
Semantic localization
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DOI：
10.1142/s242503841630010x
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Shang Ma;Qiong Liu
中科院分区：
文献类型：
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作者：
Shang Ma;Qiong Liu
传感器和无线网络的改进使得能够准确、自动、即时地确定和传播用户或物体的位置。除了当前无处不在的网络基础设施之外，基于位置的服务（LBS）的新使能器是不同系统的语义信息（例如时间、位置、个人能力、偏好等）的丰富。这种语义丰富的系统建模旨在开发具有增强功能和高级推理能力的应用程序。这些系统能够提供更个性化的服务，用户的领域知识与先进的推理机制，并提供解决方案的问题，否则是不可行的。这种方法还考虑了用户的偏好和位置属性，可以用来实现一个全面的个性化服务，如广告，推荐，或投票。本文概述了室内定位技术，流行的模型，从位置数据中提取语义，语义信息和位置数据相关联的方法，和应用程序，可以启用位置语义。为了使演示文稿易于理解，我们将使用博物馆场景来解释不同技术和模型的优缺点。更具体地说，我们将首先探索博物馆场景中的用户需求。基于这些需求，我们将讨论使用不同的本地化技术来满足这些需求的优点和缺点。从这些讨论中，我们可以突出真实的应用需求与现有技术之间的差距，并指出有前途的本地化研究方向。通过识别各种模型和真实的应用需求之间的差距，我们可以为未来的位置语义研究绘制路线图。
Improvements in sensor and wireless network enable accurate, automated, instant determination and dissemination of a user's or objects position. The new enabler of location-based services (LBSs) apart from the current ubiquitous networking infrastructure is the enrichment of the different systems with semantics information, such as time, location, individual capability, preference and more. Such semantically enriched system-modeling aims at developing applications with enhanced functionality and advanced reasoning capabilities. These systems are able to deliver more personalized services to users by domain knowledge with advanced reasoning mechanisms, and provide solutions to problems that were otherwise infeasible. This approach also takes user's preference and place property into consideration that can be utilized to achieve a comprehensive range of personalized services, such as advertising, recommendations, or polling. This paper provides an overview of indoor localization technologies, popular models for extracting semantics from location data, approaches for associating semantic information and location data, and applications that may be enabled with location semantics. To make the presentation easy to understand, we will use a museum scenario to explain pros and cons of different technologies and models. More specifically, we will first explore users' needs in a museum scenario. Based on these needs, we will then discuss advantages and disadvantages of using different localization technologies to meet these needs. From these discussions, we can highlight gaps between real application requirements and existing technologies, and point out promising localization research directions. By identifying gaps between various models and real application requirements, we can draw a roadmap for future location semantics research.