This paper introduces a distributed and energy-aware algorithm, called Minimum Drone Placement (MDP) algorithm, to determine the minimum number of base stations mounted on resource-rich Unmanned Aerial Vehicles (UAV-BS), commonly referred to as drone-BS, and their possible locations to provide fault tolerance with high network connectivity in heterogeneous wireless sensor networks. This heterogeneous model consists of resource-rich UAV-BSs, acting as gateways of data, as well as ordinary sensor nodes that are supposed to be connected to the UAV-BSs via multi-hop paths. Previous efforts on fault tolerance in heterogeneous wireless sensor networks attempt to determine transmission radii of the sensor nodes based on the already deployed base station positions. They assume that the base stations are stationary and arbitrarily deployed regardless of the position of the sensor nodes. Our proposed MDP algorithm takes into account the desired degree of fault tolerance and the position of ordinary sensor nodes to determine the optimal number of UAV-BSs and their locations. The MDP algorithm consists of two steps. In the first step, each sensor node chooses low-cost pairwise disjoint paths to a subset of candidate UAV-BSs, using an optimization based on the well-known set-packing problem. In the last step, depending on the desired degree of fault tolerance, MDP chooses a subset of these UAV-BS candidates using a novel optimization based on the well-known set-cover problem. Through extensive simulations, we demonstrate that the MDP achieves up to 40% improvement in UAV-connected lifetimes compared to a random and uniform distribution of UAV-BSs.

本文介绍了一种分布式能源感知算法，称为最小无人机放置（MDP）算法，以确定安装在资源丰富的无人机（UAV-BS）上的最小基站数量，通常称为无人机-BS，以及它们的可能位置，以在异构无线传感器网络中提供高网络连接的容错能力。这种异构模型由充当数据网关的资源丰富的UAV-BS以及应该通过多跳路径连接到UAV-BS的普通传感器节点组成。先前在异构无线传感器网络中对容错的努力试图基于已经部署的基站位置来确定传感器节点的传输半径。他们假定基站是固定的并且可以任意部署，而不管传感器节点的位置如何。我们提出的MDP算法考虑了所需的容错程度和普通传感器节点的位置，以确定UAV-BS的最佳数量及其位置。MDP算法包括两个步骤。第一步，每个传感器节点使用基于众所周知的集合打包问题的优化方法，选择通往候选UAV-BS子集的低成本成对不相交路径。在最后一步中，根据所需的容错程度，MDP根据众所周知的集线覆盖问题使用新颖的优化方法，选择了这些UAV-BS候选对象的子集。通过广泛的模拟，