1,112 research outputs found
Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks
Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets), which play
a crucial role in creating new opportunities, are experiencing significant
growth in civil applications worldwide. UASNets improve disaster management
through timely surveillance and advance precision agriculture with detailed
crop monitoring, thereby significantly transforming the commercial economy.
UASNets revolutionize the commercial sector by offering greater efficiency,
safety, and cost-effectiveness, highlighting their transformative impact. A
fundamental aspect of these new capabilities and changes is the collection of
data from rugged and remote areas. Due to their excellent mobility and
maneuverability, UAVs are employed to collect data from ground sensors in harsh
environments, such as natural disaster monitoring, border surveillance, and
emergency response monitoring. One major challenge in these scenarios is that
the movements of UAVs affect channel conditions and result in packet loss. Fast
movements of UAVs lead to poor channel conditions and rapid signal degradation,
resulting in packet loss. On the other hand, slow mobility of a UAV can cause
buffer overflows of the ground sensors, as newly arrived data is not promptly
collected by the UAV.
Our proposal to address this challenge is to minimize packet loss by jointly
optimizing the velocity controls and data collection schedules of multiple
UAVs.Furthermore, in UASNets, swift movements of UAVs result in poor channel
conditions and fast signal attenuation, leading to an extended age of
information (AoI). In contrast, slow movements of UAVs prolong flight time,
thereby extending the AoI of ground sensors.To address this challenge, we
propose a new mean-field flight resource allocation optimization to minimize
the AoI of sensory data
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