Relevant opportunistic information extraction and scheduling in heterogeneous sensor networks

T. Gulrez, S. Challa, T. Yaqub, J. Katupitiya
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引用次数: 7

Abstract

Determining the output of the most relevant sensor is of crucial importance when heterogeneous sensors are available for measuring a given process in an environment. In this paper, we describe an IEEE 1451 TEDS (transducer electronic data sheets) compliant sensor model for heterogeneous sensor networks. The proposed model uses the relevance feedback method to understand the context of a sensor learning application. We present results of a real time implementation of heterogeneous sensor networks using distributed multi-sensing 3D real-time robotics software player/gazebo on an autonomous mobile robot's navigation problem. The results show that the proposed model can be utilised in the real-time scenario and can help reduce the computational cost of a system
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异构传感器网络中相关机会信息的提取与调度
当异构传感器可用于测量环境中的给定过程时,确定最相关传感器的输出是至关重要的。在本文中,我们描述了一个IEEE 1451 TEDS(传感器电子数据表)兼容的异构传感器网络传感器模型。提出的模型使用相关反馈方法来理解传感器学习应用的上下文。本文介绍了利用分布式多传感3D实时机器人软件player/gazebo对自主移动机器人导航问题进行异构传感器网络实时实现的结果。结果表明,该模型可用于实时场景,并有助于降低系统的计算成本
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