We discuss surveillance with multiple unmanned aerial vehicles (UAV) that minimize idleness (the time between consecutive visits of sensing locations) and constrain latency (the time between capturing data at a sensing location and its arrival at the base station). This is important in persistent surveillance scenarios where sensing locations should not only be visited periodically, but the captured data also should reach the base station in due time even if the area is larger than the communication range. Our approach employs the concept of minimum-latency paths (MLP) to guarantee that the data reaches the base station within a predefined latency bound. To reach the bound, multiple UAVs cooperatively transport the data in a store-and-forward fashion. Additionally, MLPs specify a lower bound for any latency minimization problem where multiple mobile agents transport data in a store-and-forward fashion. We introduce three variations of a heuristic employing MLPs and compare their performance in a simulation study. The results show that extensions of the simplest of our approaches, where data is transported after each visit of a sensing location, show improved performance and the tradeoff between latency and idleness.

中文翻译：

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具有最小信息空闲和延迟约束的多无人机监视

我们讨论了使用多架无人机 (UAV) 进行的监视，以最大限度地减少闲置（连续访问传感位置之间的时间）和限制延迟（在传感位置捕获数据与其到达基站之间的时间）。这在持续监视场景中很重要，其中不仅应定期访问感测位置，而且即使区域大于通信范围，捕获的数据也应及时到达基站。我们的方法采用最小延迟路径 (MLP) 的概念来保​​证数据在预定义的延迟范围内到达基站。为了到达边界，多架无人机以存储转发的方式协作传输数据。此外，MLP 为任何延迟最小化问题指定了下限，其中多个移动代理以存储转发方式传输数据。我们介绍了采用 MLP 的启发式的三种变体，并在模拟研究中比较了它们的性能。结果表明，我们最简单的方法的扩展（在每次访问传感位置后传输数据）显示出改进的性能以及延迟和空闲之间的权衡。