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Balancing message delivery latency and network lifetime through an integrated model for clustering and routing in Wireless Sensor Networks

机译:通过用于无线传感器网络中的群集和路由的集成模型来平衡消息传递延迟和网络生存时间

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摘要

One common approach to extend Wireless Sensors Networks (WSN) lifetime is to use mobile sinks to gather sensed information through the network, avoiding that sensor nodes spend their limited energy in relaying other nodes' messages to the sinks. Such approach, however, tends to significantly increase message delivery latency. On the other hand, it is widely recognized that the optimization of any Quality of Service parameter in WSN, message delivery latency included, must always be conducted bearing in mind the implied impact in the network lifetime. In this paper, we introduce a network model to seek for a good solution for this inherent multi-objective optimization problem. In our approach, optimization algorithms are used to define optimal (or near-optimal) density control policies, sensors clustering and sink routes to collect sensed data. We deal with the multi-objective nature of the design in WSN by explicitly minimizing message delivery latency and by imposing topology constraints that help to reduce energy consumption. Our proposal differs from most studies in the literature by the integrated way in which we tackle clustering and routing decisions. Various metaheuristic based heuristics that solve the integrated problem were incorporated into a dynamic simulation environment. Through extensive simulation experiments, we compared our approach to others in the literature, in terms of Quality of Service parameters. Our results indicate that the integrated model proposed here compares favorably to other approaches, allowing a good balance among conflicting parameters like message delivery latency, network lifetime and rate of messages received.
机译:延长无线传感器网络(WSN)寿命的一种常用方法是使用移动接收器通过网络收集感测到的信息,从而避免传感器节点将有限的精力用于将其他节点的消息中继到接收器。然而,这种方法倾向于显着增加消息传递等待时间。另一方面,众所周知的是,必须始终考虑对网络寿命的隐含影响,始终对WSN中的任何服务质量参数(包括消息传递延迟)进行优化。在本文中,我们介绍了一种网络模型,以寻求针对此固有的多目标优化问题的良好解决方案。在我们的方法中,使用优化算法来定义最佳(或接近最佳)的密度控制策略,传感器群集和汇入路由以收集感测数据。通过显着最小化消息传递延迟并施加有助于降低能耗的拓扑约束,我们应对WSN中设计的多目标性质。我们的建议与文献中大多数研究的不同之处在于,我们采用综合方法来处理聚类和路由决策。解决集成问题的各种基于元启发式的启发式方法已合并到动态仿真环境中。通过广泛的模拟实验,我们在服务质量参数方面将我们的方法与文献中的其他方法进行了比较。我们的结果表明,此处提出的集成模型与其他方法相比具有优势,可以在冲突的参数(如消息传递延迟,网络生存期和收到消息的速率)之间取得良好的平衡。

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