Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows
promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned …

Deep reinforcement learning (DRL) has emerged as a promising technique for optimizing
the deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor …

Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from
ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms …

Real-time status updates require more frequent updates with fresh information. This study
investigates the applications and research potential of unmanned aerial vehicles (UAV) for …

In many massive IoT communication scenarios, the IoT devices require coverage from
dynamic units that can move close to the IoT devices and reduce the uplink energy …

Reinforcement learning (RL) has been widely adopted for controlling and optimizing
complex engineering systems such as next-generation wireless networks. An important …

The gathering of data produced by ground Internet of Things (IoT) devices can be facilitated
with the assistance from Unmanned Aerial Vehicles (UAVs) especially in hard-to-reach …

This paper explores the significance of the timeliness of information in the context of fifth
generation (5G) non-terrestrial networks (NTN). As 5G technology continues to evolve, its …

The Age of Information (AoI) is used to measure the freshness of the data. In IoT networks,
the traditional resource management schemes rely on a message exchange between the …

This paper studies the age of information (AoI) and energy tradeoff (AET) problem in an
aerial-ground collaborative mobile edge computing system, where a high-altitude platform …