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Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network

机译:基于智能查询的数据聚合模型和高效无线传感器网络的优化查询排序

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摘要

Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency.
机译:数据聚合算法在WSN中发挥着主要作用,因为它以节能方式收集和聚合数据,以便延长网络的预期寿命。本文旨在使用称为组搜索优化(GSO)的高级优化算法为WSN开发基于查询的聚合模型。所提出的模型以这样的方式构造,即可以基于延迟和吞吐量对查询顺序(Qo)进行排序。因此,所提出的基于GSO的Qo的主要目的是最小化等待时间并最大化WSN的吞吐量。所提出的数据聚合模型有助于网络管理员了解最佳查询,以便可以提高基站的性能。在绘制模型后,它比较了GSO的Qo与传统的基于PSO的Qo,基于FF的Qo,基于GA的Qo,基于ABC的Qo和GSO的Qo在空闲时间和吞吐量方面的性能。因此,基于GSO的Qo的数据聚合性能通过获得高吞吐量和低延迟来优于传统算法。

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