Data aggregation for Vehicular Ad-hoc Network using particle swarm optimization

M. Shoaib, Wang-Cheol Song
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引用次数: 14

Abstract

The data aggregation process can be considered a problem of multi-objective optimization which reduces the size of data in such a way that its relevance with original data remains as closer as possible. Data Aggregation is of great importance in Wireless Sensor Networks, Vehicular Ad-hoc Networks to transmit the recorded data in time over low bandwidth. In this regard, data aggregation solutions have been developed; however, their actual usage has been limited, for the reason of low accuracy and high processing time. In this paper, particle swarm optimization (PSO) is used to optimize process of multi-objective data aggregation in vehicular ad-hoc network. In our work processing time for aggregation and aggregation quality have been set as objectives. The proposed method has been compared with state of the art existing aggregation techniques. Experimental results show that our method simplifies aggregation effectively and obtains a higher aggregation accuracy compared to the other data aggregation methods.
基于粒子群算法的车载自组网数据聚合
数据聚合过程可以被认为是一个多目标优化问题,它减少了数据的大小,使其与原始数据的相关性尽可能接近。在无线传感器网络、车载自组织网络中,数据聚合是实现记录数据在低带宽下及时传输的重要手段。在这方面,已经制订了数据汇总解决办法;然而,由于精度低、加工时间长等原因,其实际应用受到了限制。本文将粒子群算法(PSO)应用于车载自组网中多目标数据聚合过程的优化。在我们的工作中,聚合的处理时间和聚合质量都被设定为目标。所提出的方法已与最先进的现有聚合技术进行了比较。实验结果表明,与其他数据聚合方法相比,该方法有效地简化了聚合,获得了更高的聚合精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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