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Spatial and Temporal Multi-Aggregation for State-Based Sensor Data in Wireless Sensor Networks

机译:无线传感器网络中基于状态的传感器数据的时空多重聚合

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摘要

Sensor nodes are thrown to remote environments for deployment and constitute a multi-hop sensor network over a wide range of area. Users hardly have global information on the distribution of sensor nodes. Hence, when users request state-based sensor readings such as temperature and humidity in an arbitrary area, networks may suffer unpredictable heavy traffic. This problem needs data aggregation to comply with user requirements and manage overlapped aggregation trees of multiple users efficiently. In this paper, spatial and temporal multiple aggregation (STMA) is proposed to minimize energy consumption and traffic load when a single or multiple users gather state-based sensor data from varions subareas through multi-hop paths. Spatial aggregation builds the aggregation tree with an optimal intermediary between a target area and a sink. The broadcast nature of wireless communication is exploited to build the aggregation tree in the confined area. Temporal aggregation uses the interval so that users obtain an appropriate amount of data they need without suffering excess traffic. The performance of STMA is evaluated in terras of energy consumption and area-to-sink delay in the simulation based on real parameters of Berkeley's MICA motes.
机译:传感器节点被扔到远程环境中进行部署,并在广泛的区域内构成了多跳传感器网络。用户几乎没有关于传感器节点分布的全局信息。因此,当用户请求基于状态的传感器读数(例如任意区域中的温度和湿度)时,网络可能会遭受不可预测的繁重流量。该问题需要数据聚合以符合用户要求并有效地管理多个用户的重叠聚合树。在本文中,提出了时空多重聚合(STMA),以在单个或多个用户通过多跳路径从varions子区域收集基于状态的传感器数据时,最大程度地降低能耗和交通负荷。空间聚集使用目标区域和接收器之间的最佳中介构建聚集树。利用无线通信的广播性质在受限区域中建立聚合树。时间聚合使用该间隔,以便用户获得所需数量的数据而不会遭受过多的流量。在仿真中,基于伯克利的MICA微粒的真实参数,评估了STMA的性能,包括能耗和面积-沉没延迟。

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