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首页> 外文会议>Proceedings of the IASTED International Conference on Networks and Communication Systems >SYNCHRONOUS MULTI-TRAINING IN SENSOR NETWORKS
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SYNCHRONOUS MULTI-TRAINING IN SENSOR NETWORKS

机译：传感器网络中的同步多重训练

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摘要

The network considered in this paper consist of tiny energy-constrained sensors massively deployed in an area of interest. We assume that several sinks referred as aggregating and forwarding nodes (AFN, for short) provide the interface to the outside world. Since it is impossible to endow individual sensors with GPS capabilities, the best that can be done is to endow the sensors with coarse-grain location awareness. This task is referred to as training. The AFNs are responsible for training the sensors in a disk of a known radius around them. Since some sensors may be within the coverage of two or more AFNs, it is very important to schedule times at which these sensors can be trained by these AFNs. The main contribution of this work is to propose a multiple-training protocol for massively-deployed sensor networks. Training the sensor nodes with a single sink and double sink are already a challenging task. In this paper, training multiple sinks has additional complexities. There will be an overlap area between three AFNs that require a protocol to proceed the multi-training. We show analytically that individual sensors are trained by AFNs energy-efficiently.
机译：本文考虑的网络由大规模部署在关注区域的受能量限制的微型传感器组成。我们假设几个汇聚点（称为聚合和转发节点，简称AFN）提供了与外界的接口。由于不可能为单个传感器提供GPS功能，因此最好的办法是使传感器具有粗粒度的位置感知功能。此任务称为培训。 AFN负责在传感器周围已知半径的磁盘中训练传感器。由于某些传感器可能在两个或多个AFN的覆盖范围内，因此安排这些AFN可以训练这些传感器的时间非常重要。这项工作的主要贡献是为大规模部署的传感器网络提出了一种多训练协议。用单水槽和双水槽训练传感器节点已经是一项艰巨的任务。在本文中，训练多个接收器具有额外的复杂性。三个AFN之间需要一个协议进行多重训练的重叠区域。我们通过分析表明，单个传感器受AFN高效地训练。

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