Distributed Spatial Reasoning for Wireless Sensor Networks

Distributed Spatial Reasoning for Wireless Sensor Networks
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无线传感器网络的分布式空间推理

DOI:
10.1007/978-3-642-24279-3_28
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发表时间:
2011
期刊:
影响因子:
--
通讯作者:
Michael Beigl
Michael Beigl
中科院分区:
--
文献类型:
--
作者:
Hedda R. Schmidtke;Michael Beigl

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位置感知系统是移动或空间分布式计算系统,如智能手机或无线传感器网络中的传感器节点,能够灵活应对不断变化的环境。由于这些平台的计算能力和实时需求的严格限制,目前大多数解决方案不支持高级空间推理。定性空间推理(QSR)和粒度是两种被提出的机制,以使空间环境的推理易于处理。我们提出了一种结合这两种技术的方法,从而获得一种轻量级的QSR机制,称为偏阶QSR(为简洁:PQSR),其速度足够快,可以在小型,低成本的计算设备上应用。PQSR的关键思想是使用典型QSR关系的核心片段,该片段可以用偏序及其线性化表示,并通过基于大小的粒度机制对这些关系的推理进行划分。
Location-aware systems are mobile or spatially distributed computing systems, such as smart phones or sensor nodes in wireless sensor networks, enabled to react flexibly to changing environments. Due to severe restrictions of computational power on these platforms and real-time demands, most current solutions do not support advanced spatial reasoning. Qualitative Spatial Reasoning (QSR) and granularity are two mechanisms that have been suggested in order to make reasoning about spatial environments tractable. We propose an approach for combining these two techniques, so as to obtain a light-weight QSR mechanism, calledpartial order QSR(for brevity: PQSR), that is fast enough to allow application on small, low-cost computing devices. The key idea of PQSR is to use a core fragment of typical QSR relations, which can be expressed with partial orders and their linearizations, and to additionally delimit reasoning about these relations with a size-based granularity mechanism.
DOI: --
发表时间: 2003
期刊: Conference On Spatial Information Theory
影响因子: --
作者:
H. Schmidtke
通讯作者: H. Schmidtke
DOI: 10.1007/978-3-642-01516-8_21
发表时间: 2009-05
期刊: --
影响因子: --
作者:
H. Schmidtke;Woontack Woo
通讯作者: H. Schmidtke;Woontack Woo
粒度作为上下文参数
DOI: --
发表时间: 2005
期刊: Context
影响因子: --
作者:
H. Schmidtke
通讯作者: H. Schmidtke
DOI: --
发表时间: 2007
期刊: International Joint Conference on Artificial Intelligence
影响因子: --
作者:
H. Schmidtke;Woontack Woo
通讯作者: Woontack Woo
位置、区域和集群:位置建模中的粒度层
DOI: --
发表时间: 2010
期刊: Deutsche Jahrestagung für Künstliche Intelligenz
影响因子: --
作者:
H. Schmidtke;M. Beigl
通讯作者: M. Beigl