UAV networks consisting of low SWaP (size, weight, and power), fixed-wing UAVs are used in many applications, including area monitoring, search and rescue, surveillance, and tracking. Performing these operations efficiently requires a scalable, decentralized, autonomous UAV network architecture with high network connectivity. Whereas fast area coverage is needed for quickly sensing the area, strong node degree and base station (BS) connectivity are needed for UAV control and coordination and for transmitting sensed information to the BS in real time. However, the area coverage and connectivity exhibit a fundamental trade-off: maintaining connectivity restricts the UAVs' ability to explore. In this paper, we first present a node degree and BS connectivity-aware distributed pheromone (BS-CAP) mobility model to autonomously coordinate the UAV movements in a decentralized UAV network. This model maintains a desired connectivity among 1-hop neighbors and to the BS while achieving fast area coverage. Next, we propose a deep Q-learning policy based BS-CAP model (BSCAP-DQN) to further tune and improve the coverage and connectivity trade-off. Since it is not practical to know the complete topology of such a network in real time, the proposed mobility models work online, are fully distributed, and rely on neighborhood information. Our simulations demonstrate that both proposed models achieve efficient area coverage and desired node degree and BS connectivity, improving significantly over existing schemes.

翻译：由低SWaP（尺寸、重量与功耗）固定翼无人机组成的无人机网络广泛应用于区域监测、搜索救援、监视与追踪等任务。高效执行这些操作需要具备高网络连通性的可扩展、分散式自主无人机网络架构。快速区域覆盖对于及时感知环境至关重要，而强节点度与基站（BS）连接性则用于无人机控制协调及实时传输感知信息至基站。然而，区域覆盖与连接性存在根本性权衡：维持连接性会限制无人机的探索能力。本文首先提出一种节点度与基站连接感知的分布式信息素（BS-CAP）移动模型，用于在分散式无人机网络中自主协调无人机运动。该模型在实现快速区域覆盖的同时，维持一跳邻居节点间及与基站的期望连接性。随后，我们提出基于深度Q学习策略的BS-CAP模型（BS-CAP-DQN），进一步调优覆盖与连接性的权衡。由于实时获取此类网络的完整拓扑并不实际，所提移动模型在线运行、完全分布式且仅依赖邻域信息。仿真结果表明，两种模型均能实现高效区域覆盖、期望节点度与基站连接性，显著优于现有方案。