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首页> 外文会议>2012 International Conference on Wireless Communications amp; Signal Processing >A new data gathering scheme for trajectory constrained mobile sink in WSN
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A new data gathering scheme for trajectory constrained mobile sink in WSN

机译：WSN中轨迹受限移动汇的新数据采集方案

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摘要

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme — implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.
机译：最近，在无线传感器网络（WSN）中部署移动接收器（MS）的优势吸引了极大的关注，因为接收器的移动性提供了一种有效的方法来改善WSN中的数据收集性能。然而，在这种WSN中，数据分发的效率显然受到MS的轨迹的影响。因此，在许多研究中已经研究了MS的最佳移动方案。然而，在许多情况下，MS的轨迹无法像以前的研究那样理想地改变，而是取决于环境条件。在本文中，我们重点关注轨迹无法理想变化的MS在WSN中的数据收集效率，并提出一种新的数据扩散方案-在分层WSN中实施新的Pre-Diffusion算法（PDA），旨在提高数据收集效率。这个分层的WSN由几个群集组成，数据传输由功率更高的群集头（CH）节点完成。我们的方案预先确定了MS在接下来的一段时间内最有可能到达的群集区域。 MS会在群集头（CH）中完成数据聚合，当数据收集开始时，与其他CH相比，MS到MS的距离可能最小。因此，可以用更少的MS轨迹长度收集更多的数据。仿真结果表明，与传统方案相比，本文提出的方案提高了数据收集效率。

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