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Maximizing data gathering capacity of wireless sensor networks using mobile relays

机译:使用移动中继最大化无线传感器网络的数据收集能力

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Recently, the availability of numerous low-cost robotic units (e.g., Packbot, Robomote, and Khepera) has made it possible to massively deploy mobile sensors in a network and use them in a disposable manner. It has been shown that the controlled mobility offered by sensors can be exploited to improve the energy efficiency of a network. In this paper, we study a new problem called max-data mobile relay configuration (MMRC) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. Different from previous controlled mobility approaches, we account for several characteristics of existing practical mobile sensing platforms including limited mobility and the high energy consumption of locomotion. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies. Our extensive simulations based on realistic energy models of existing mobile sensing platforms show that our approach can increase the data gathering capacity by a factor of at least 2 in most scenarios. Moreover, our distributed algorithms converge quickly and incur low messaging overhead.
机译:近来,众多低成本机器人单元(例如Packbot,Robomote和Khepera)的可用性使在网络中大规模部署移动传感器并以一次性方式使用它们成为可能。已经表明,可以利用传感器提供的受控移动性来改善网络的能量效率。在本文中,我们研究了一个称为最大数据移动中继配置(MMRC)的新问题,该问题可以找到一组称为中继器的移动传感器的位置,这些位置可以最大化网络在其生命周期内收集的数据总量。与以前的受控移动性方法不同,我们考虑了现有实用移动感应平台的几个特征,包括移动性有限和运动的高能耗。我们表明,由于对无线通信和机械运动的能耗的共同考虑,即使对于琐碎的网络拓扑,MMRC问题也令人惊讶地复杂。我们为几种重要的网络拓扑提供了最佳的MMRC算法和实用的分布式实现。我们基于现有移动感测平台的真实能量模型进行的广泛模拟显示,在大多数情况下,我们的方法可以将数据收集能力提高至少2倍。此外,我们的分布式算法收敛迅速,并且消息传递开销较低。

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