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首页> 外文期刊>Journal of computer sciences >Data Relay Clustering Algorithm for Wireless Sensor Networks: A Data Mining Approach
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Data Relay Clustering Algorithm for Wireless Sensor Networks: A Data Mining Approach

机译：无线传感器网络的数据中继聚类算法：一种数据挖掘方法

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摘要

Problem statement: Nowadays sensors are very essential for today life to monitor environment where human cannot get involved very often. Wireless Sensor Networks (WSN) are used in many real world applications like environmental monitoring, traffic control, trajectory monitoring. It is more challenging for sensor network to sense and collect a large amount of data which are continuous over time, which in turn need to be forwarded to sink for further decision making process. Clustering of sensory data act as a nucleus job of data mining. A clustering in WSN involves selecting cluster heads and assigning cluster members(sensors) to it for efficient data relay. The contraints in power supply, limited communication, bandwidh, storage resoures are the major challenges in WSN facing today. Conclusion: Proposed study presents K-Means Data Relay (K-MDR) clustering algorithm for grouping sensor nodes there by reducing number of nodes transmitting data to sink node, it reduces the communication overhead and in this manner increase the network performance. Furthermore Conserve and Observe Modes (COM) algorithm reduces the number of nodes within the cluster there by without compromising the coverage face major challenges such as limited communication bandwidth, constraints in power supply and storage resources region of it. The contribution of K-MDR is to reduce power consumption finally the simulation experimental results show that the time efficiency of the algorithm is achieved.
机译：问题陈述：如今，传感器对于当今人类来说至关重要，它可以监视人们无法经常参与的环境。无线传感器网络（WSN）被用于许多实际应用中，例如环境监测，交通控制，轨迹监测。对于传感器网络而言，感知和收集大量随时间连续的数据更具挑战性，而继而需要转发这些数据以沉没以进行进一步的决策过程。感觉数据的聚类是数据挖掘的核心工作。 WSN中的群集涉及选择群集头并为其分配群集成员（传感器）以进行有效的数据中继。电源供应，通信受限，带宽不足，存储资源有限的矛盾是当今WSN面临的主要挑战。结论：拟议的研究提出了一种K-Means数据中继（K-MDR）聚类算法，用于通过减少将数据传输到接收器节点的节点数量来对传感器节点进行分组，从而减少了通信开销并以此提高了网络性能。此外，保护和观察模式（COM）算法在不影响覆盖范围的情况下减少了群集中节点的数量，而覆盖范围面临着主要挑战，例如通信带宽受限，电源供应受限以及其存储资源区域。仿真实验结果表明，K-MDR的最终目的是降低功耗，达到了算法的时间效率。

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