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A Search Strategy of Level-Based Flooding for the Internet of Things

机译:基于层次的物联网泛洪搜索策略

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摘要

This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales.
机译:本文讨论了物联网(IoT)中的查询问题。泛洪是一种重要的查询策略。但是,原始的洪水很容易造成沉重的网络负载。为了解决此问题,我们提出了一种泛洪的变种,称为基于级别的泛洪(LBF)。使用LBF,根据传感器节点和宿节点之间的距离(即跃点)将整个网络分为几个级别。接收节点知道每个节点的级别信息。查询数据包根据节点的级别在网络中广播。接收到查询数据包后,传感器节点会根据已处理查询数据的邻居的百分比来决定如何处理它。当目标节点接收到查询数据包时,它会通过随机游走将其数据发送回接收节点。我们通过广泛的仿真表明,就成本和延迟而言,LBF的性能要比原始洪泛性能好得多,并且LBF可以用于不同规模的IoT。

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