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L-SYNC: Larger Degree Clustering Based Time-Synchronisation for Wireless Sensor Network

机译:L-SYNC:无线传感器网络中基于更大程度聚类的时间同步

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In many existing synchronization protocols within wireless sensor networks, the effect of routing algorithm in synchronization precision of two remote nodes is not being considered. In several protocols such as SLTP, this issue is considered for local time estimation of a remote node. Cluster creation is according to ID technique. This technique incurs an increase in cluster overlapping and eventually the routing algorithm will be affected and requires more hops to move from one cluster to another remote cluster. In this article, we present L-SYNC method, which creates large degree clusters for wireless sensor networks synchronization. Using large degree clustering, L-SYNC can reduce path hops. Also, L-SYNC uses linear regression method to calculate clock offset and skew in each cluster. Therefore, it is capable to compute skew and offset intervals between each node and its head cluster and, in other words, it can estimate the local time of remote nodes in future and past. To estimate the local time for remote nodes, routing algorithm is used and conversion technique is performed in each time changing hop. The fewer L-SYNC hops could increase the precision. Simulation results illustrate that monotonous clustering formation can increase the precision in synchronization. However, more overhead and time period are needed for clustering formation
机译:在无线传感器网络中许多现有的同步协议中,没有考虑路由算法对两个远程节点的同步精度的影响。在诸如SLTP之类的几种协议中,对于远程节点的本地时间估计考虑了此问题。群集创建是根据ID技术进行的。此技术导致群集重叠的增加,最终路由算法将受到影响,并且需要更多的跃点才能从一个群集移动到另一个远程群集。在本文中,我们提出了L-SYNC方法,该方法创建了用于无线传感器网络同步的大型度簇。使用大型聚类,L-SYNC可以减少路径跳数。此外,L-SYNC使用线性回归方法来计算每个群集中的时钟偏移和偏斜。因此,它能够计算每个节点与其头簇之间的偏斜和偏移间隔,换句话说,它可以估计将来和过去远程节点的本地时间。为了估计远程节点的本地时间,使用路由算法,并在每个时间变化的跃点中执行转换技术。较少的L-SYNC跳数可以提高精度。仿真结果表明,单调聚类可以提高同步精度。但是,集群形成需要更多的开销和时间段

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