In disasters such as earthquakes, fires, or floods, the rapid, effective, and reliable communication and emergency response could greatly alleviate economic losses and save lives. However, the communication network infrastructures tend to be collapsed or limited functioning in such circumstances. Unmanned Aerial Vehicles (UAVs), with high mobility and agility, can assist as flying base stations or mobile relays to serve a disaster area out of the reach of the cellular network.

Most existing UAV-aided communication network systems for disaster management support only a single UAV feature with limited capacity or naively operate multiple UAVs manually by pilots, experiencing the issues of non-cooperation, long delay bottleneck, energy consuming and limited adaptability to a dynamic disaster environment. There are important unaddressed technological issues in the literature regarding the UAV-user interaction for path planning, UAV-UAV cooperation for sustainable service provision, and onboard energy allocation for balancing both hovering time and service capacity according to energy consumption and dynamic user demands. Aware of such limitations, a future UAV network should be intelligent enough to provide fast and reliable communication services to rescuers and victims in case of cellular coverage collapse.

In this Special Issue, researchers from academia and practitioners from the industry are invited to submit their cutting-edge original research and review articles on UAV-aided network intelligence techniques in disasters. This Special Issue aims to address advances in Artificial Intelligence (AI) techniques for novel and improved capabilities of emergency communications in disasters, which enable a more energy-efficient, responsive, and intelligent network infrastructure.

Potential topics include but are not limited to the following:

• UAVs’ trajectories optimization
• UAV swarm cooperation for data collection
• Joint disaster sensing communication UAV Network
• Game theory for UAVs’ placement
• Path-planning of UAV swarm in uncertain environments
• Machine learning techniques for UAV network design
• UAV-aided edge computing
• UAV relaying networks
• Recharging schemes in sustainable UAV networks
• Security and privacy issues in UAV networks
• UAV sensing, data processing and mining
• Computational intelligence in UAV-related applications
• Other UAV-assisted disaster management applications