Mobile sensor networks (MSNs) are good candidates for large-scale unattended surveillance applications. However, it is challenging to track moving targets due to their complex dynamic behaviors. Semiflocking algorithms have been proven to be efficient in controlling MSNs in both area coverage and target tracking applications. While many existing literatures on the study of semiflocking algorithms often assume an area of interest (AoI) to be regular and with unified traversal cost, the uneven and rough landscapes in real-life applications have imposed extra challenges and raised demands for new management strategies. In this article, a mobility map is used to incorporate different costs associated with irregular terrains which results in different maximum allowed speeds on nodes in different regions. In order to reduce target detection time and node energy consumption, a heuristic search algorithm is developed to find time-efficient and feasible paths between nodes and sensing targets. Under the proposed algorithm, nodes can effectively select a target to track or search for new targets in the AoI. Results of extensive experiments show that semiflocking-controlled nodes together with path planning can reach their targets faster with lower energy consumption compared to three exiting flocking-based algorithms.

中文翻译：

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用于移动传感器网络中多目标监视的启用路径规划的半群控制

移动传感器网络（MSN）是大规模无人值守监视应用程序的不错选择。然而，由于其复杂的动态行为，跟踪运动目标具有挑战性。半群算法已被证明可以有效控制区域覆盖和目标跟踪应用中的MSN。尽管许多现有的有关半群聚算法研究的文献通常假设感兴趣的区域（AoI）规则且具有统一的遍历成本，但现实应用中的不平坦和崎rough环境给新管理策略带来了额外的挑战并提出了更高的要求。在本文中，移动性图用于合并与不规则地形相关的不同成本，从而导致在不同区域的节点上具有不同的最大允许速度。为了减少目标检测时间和节点能量消耗，开发了一种启发式搜索算法，以找到节点与感测目标之间的时间高效且可行的路径。在提出的算法下，节点可以有效地选择一个目标来跟踪或搜索AoI中的新目标。大量实验的结果表明，与三种现有的基于植绒的算法相比，半植树控制的节点以及路径规划可以以更低的能耗更快地达到其目标。