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Energy Efficient Mobility Prediction based Localization Algorithm for Mobile Sensor Networks

机译:基于能效移动性预测的移动传感器网络定位算法

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Aim of study is to develop energy efficient mobility prediction based localization algorithm for mobile sensor networks. Mobile sensors are being deployed in the environment like underwater sea monitoring, where they can move to any location to meet coverage requirements. Sensor networks choose to follow data centric approach to track the exact location of the nodes. Location information should be identified for application tasks and network operations. Novel localization algorithms need to be developed for mobile sensor networks with high accuracy and minimal energy consumption. The proposed localization algorithm for mobile sensor networks uses the mobility prediction localization and Mobility Prediction Localization (MPL) algorithm with Maximum Likelihood Estimation (MLE) technique for low and high accuracy respectively. The performance analysis shows that proposed MPL and MPL along with MLE achieve accuracy above 95.5% in the simulated environment. This study also analyses the number of anchor nodes to be deployed to achieve desired level of accuracy. The proposed algorithm uses minimal number of nodes for tracking while rest are in sleep mode for energy saving, thereby increasing lifetime of the nodes.
机译:研究的目的是为移动传感器网络开发基于能效移动性预测的定位算法。移动传感器正被部署在水下海底监测等环境中,它们可以移动到任何位置以满足覆盖范围的要求。传感器网络选择遵循以数据为中心的方法来跟踪节点的确切位置。应该为应用程序任务和网络操作标识位置信息。需要为移动传感器网络开发高精度和低能耗的新型定位算法。提出的用于移动传感器网络的定位算法分别将移动性预测定位和移动性预测定位(MPL)算法与最大似然估计(MLE)技术结合使用,分别用于低精度和高精度。性能分析表明,所提出的MPL和MPL以及MLE在模拟环境中均达到了95.5%以上的精度。这项研究还分析了要实现所需精度水平要部署的锚节点的数量。所提出的算法使用最少数量的节点进行跟踪,而其余部分处于睡眠模式以节省能源,从而延长了节点的寿命。

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