Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of
  Reconnaissance UAVs
  Current state of the art in the field of UAV activation relies solely on
human operators for the design and adaptation of the drones' flying routes.
Furthermore, this is being done today on an individual level (one vehicle per
operators), with some exceptions of a handful of new systems, that are
comprised of a small number of self-organizing swarms, manually guided by a
human operator.
  Drones-based monitoring is of great importance in variety of civilian
domains, such as road safety, homeland security, and even environmental
control. In its military aspect, efficiently detecting evading targets by a
fleet of unmanned drones has an ever increasing impact on the ability of modern
armies to engage in warfare. The latter is true both traditional symmetric
conflicts among armies as well as asymmetric ones. Be it a speeding driver, a
polluting trailer or a covert convoy, the basic challenge remains the same --
how can its detection probability be maximized using as little number of drones
as possible.
  In this work we propose a novel approach for the optimization of large scale
swarms of reconnaissance drones -- capable of producing on-demand optimal
coverage strategies for any given search scenario. Given an estimation cost of
the threat's potential damages, as well as types of monitoring drones available
and their comparative performance, our proposed method generates an
analytically provable strategy, stating the optimal number and types of drones
to be deployed, in order to cost-efficiently monitor a pre-defined region for
targets maneuvering using a given roads networks.
  We demonstrate our model using a unique dataset of the Israeli transportation
network, on which different deployment schemes for drones deployment are
evaluated.
