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Connectivity preserving task allocation in mobile robotic sensor network

机译:移动机器人传感器网络中保持连接性的任务分配

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Robotic sensor network (RSN) contains mobile sensors and robots provides feasible solution for many multiagent applications. One of the most critical issues in RSN and its application is how to effectively assign tasks. This paper presents a novel threshold-based task allocation strategy to answer the question particularly for RSN. Firstly, we model the task allocation in RSN to distinguish the discovering and allocating processes. Secondly, a fully distributed simple Threshold-based Unoccupied Neighbor Algorithm, namely TUNA, is developed to allocate tasks with only partial view of the network topology. A connectivity controller is finally developed and integrated into the strategy to guarantee the global connectivity of entire RSN, which is critical in most RSN applications. Experimental simulations are also provided to evaluate the performance of the proposed strategy.
机译:机器人传感器网络(RSN)包含移动传感器,而机器人为许多多代理应用程序提供了可行的解决方案。 RSN及其应用中最关键的问题之一是如何有效地分配任务。本文提出了一种新颖的基于阈值的任务分配策略来回答特别是对于RSN的问题。首先,我们在RSN中对任务分配建模,以区分发现和分配过程。其次,开发了一种完全分布式的,基于阈值的简单无占用邻居算法,即TUNA,以仅使用网络拓扑的部分视图来分配任务。最后开发了一个连接控制器并将其集成到策略中,以确保整个RSN的全局连接,这在大多数RSN应用程序中至关重要。还提供了实验仿真来评估所提出策略的性能。

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