  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.
