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On optimisation of cluster-based sensor network tracking system

机译:基于集群的传感器网络跟踪系统的优化

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Tracking mobile targets using low-cost wireless sensor network (WSN) requires not only good tracking accuracy but also network longevity. Cluster-based tracking protocols leverage the fact that only sensors in the vicinity of the target can contribute to target detection, while other sensors should sleep to save energy, which provides good tradeoff between energy efficiency and tracking accuracy. However, for the complexity of cluster-based tracking protocols, it is challenging to quantify the tradeoff between energy efficiency and tracking accuracy. In this paper, a convolution-based method is presented to quantify the relationship between the cluster parameters and the energy-quality metrics of the tracking system, which provides Pareto optimal parameters to jointly optimise the energy efficiency and the tracking accuracy of cluster-based WSN tracking system. The presented results are verified in popular cluster-based tracking protocols via extensive simulations, which shows the effectiveness of the optimisation framework.
机译:使用低成本无线传感器网络(WSN)跟踪移动目标不仅需要良好的跟踪精度,而且还需要网络寿命。基于集群的跟踪协议利用了以下事实:只有目标附近的传感器才有助于目标检测,而其他传感器应休眠以节省能量,这在能量效率和跟踪精度之间提供了良好的折衷。但是,由于基于集群的跟踪协议的复杂性,量化能量效率和跟踪精度之间的折衷是一项挑战。本文提出了一种基于卷积的方法来量化集群参数与跟踪系统能量质量指标之间的关系,该方法提供了帕累托最优参数,以共同优化基于集群的无线传感器网络的能效和跟踪精度跟踪系统。通过广泛的仿真,在流行的基于群集的跟踪协议中验证了提出的结果,这表明了优化框架的有效性。

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