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Gallery: A Self-healing Topology Model for Pervasive Sensor Networks

机译:画廊:普及型传感器网络的自修复拓扑模型

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Pervasive Sensor Network(PSN) is identified as rapid dynamic network. PSN environment is viable only through Sensor networks. Fault-tolerance is expected high when handling speedy movements of sensor nodes. Henceforth, we propose a novel self-healing topology named Gallery; a placement model for Pervasive WSNs with dynamic topology control functions. The topology model ensures the localization of sensor nodes and guarantees the scalability by increasing its tracks and slices. The 2D and 3D deployment methods are Gallery is discussed. A wait-n-relay method is introduced for mobility nodes. An informal clustering of nodes within a region is identified as track region. The careful design of track region and relevant parameters are done. A covering map of mobility enabled relay sensor is calculated. The simulation is done with omnetpp simulator. Self-healing is promising by exact data aggregation by relay sensor if an desirable event raised within the region of interest.
机译:普适传感器网络(PSN)被标识为快速动态网络。 PSN环境仅通过Sensor网络才可行。在处理传感器节点的快速移动时,预计容错性很高。此后,我们提出了一种新颖的自我修复拓扑,名为Gallery;具有动态拓扑控制功能的普及型WSN的放置模型。拓扑模型可确保传感器节点的本地化,并通过增加其轨迹和切片来确保可伸缩性。讨论了2D和3D部署方法库。为移动性节点引入了“等待-n-中继”方法。区域内节点的非正式聚类被标识为轨道区域。仔细设计了轨道区域和相关参数。计算出启用了移动性的中继传感器的覆盖图。仿真是使用omnetpp模拟器完成的。如果在感兴趣区域内引发了期望的事件,则通过中继传感器进行精确的数据聚合可以实现自我修复。

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