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Multi-hop localization system for environmental monitoring in wireless sensor and actor networks

机译:用于无线传感器和actor网络中的环境监控的多跳定位系统

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Location estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks. The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring relies on an accurate position estimation to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth, and geographical features. The actors are not equipped with positioning adaptors, and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actors, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the nodes can be efficiently positioned using a multi-hop approach with local information exchange only. The introduced approach is also applied to a scenario, where monkey swarm monitoring is simulated, to test the generalizability of the algorithm.
机译:传感器节点的位置估计是无线传感器和参与者网络的大多数应用程序的重要组成部分。位置信息不明确通常会使收集的数据在这些应用程序中无用。环境监视依赖于准确的位置估计来处理或评估收集的数据。在本文中,我们提出了一种新颖且可扩展的方法来定位移动传感器节点,以监控亚马逊河。场景中的角色是固定的,位于河边土地上的可到达位置,而传感器节点被扔到河中以收集水温,深度和地理特征等数据。演员没有配备定位适配器,他们只知道自己与其他演员的距离。传感器节点收集数据并将其转发给参与者。当漂浮在河中时,传感器节点通常距离演员多数步路程,这使得应用传统定位技术具有挑战性。通过广泛的仿真,我们表明可以使用仅具有本地信息交换的多跳方法来有效定位节点。引入的方法还适用于模拟猴子群监视的场景,以测试算法的可推广性。

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