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Multi-objective hierarchical algorithms for restoring Wireless Sensor Network connectivity in known environments

机译:用于在已知环境中恢复无线传感器网络连接的多目标分层算法

摘要

A Wireless Sensor Network can become partitioned due to node failure, requiring the deployment of additional relay nodes in order to restore network connectivity. This introduces an optimisation problem involving a tradeoff between the number of additional nodes that are required and the costs of moving through the sensor field for the purpose of node placement. This tradeoff is application-dependent, influenced for example by the relative urgency of network restoration. We propose a family of algorithms based on hierarchical objectives including complete algorithms and heuristics which integrate network design with path planning, recognising the impact of obstacles on mobility and communication. We conduct an empirical evaluation of the algorithms on random connectivity and mobility graphs, showing their relative performance in terms of node and path costs, and assessing their execution speeds. Finally, we examine how the relative importance of the two objectives influences the choice of algorithm. In summary, the algorithms which prioritise the node cost tend to find graphs with fewer nodes, while the algorithm which prioritise the cost of moving find slightly larger solutions but with cheaper mobility costs. The heuristic algorithms are close to the optimal algorithms in node cost, and higher in mobility costs. For fast moving agents, the node algorithms are preferred for total restoration time, and for slow agents, the path algorithms are preferred.
机译:由于节点故障,无线传感器网络可能会变得分区,需要部署其他中继节点才能恢复网络连接。这就引入了一个优化问题,涉及在所需的附加节点数量与出于节点放置目的而经过传感器字段的成本之间进行权衡的问题。这种权衡取决于应用程序,例如受网络恢复的相对紧迫性的影响。我们提出了一系列基于分层目标的算法,其中包括完整的算法和启发式方法,这些方法将网络设计与路径规划相集成,从而认识到障碍物对移动性和通信的影响。我们在随机连接性和移动性图上对算法进行了实证评估,显示了它们在节点和路径成本方面的相对性能,并评估了它们的执行速度。最后,我们研究了两个目标的相对重要性如何影响算法的选择。总而言之,优先考虑节点成本的算法往往会找到节点数较少的图形,而优先考虑移动成本的算法会找到稍大的解决方案,但移动成本较低。该启发式算法在节点成本上接近最佳算法,并且在移动性成本上更高。对于快速移动的代理,对于总恢复时间,首选节点算法,而对于慢速代理,则首选路径算法。

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