OPTIMIZING ENERGY CONSUMPTION AND LATENCY IN IOT THROUGH EDGE COMPUTING IN AIR–GROUND INTEGRATED NETWORK WITH DEEP REINFORCEMENT LEARNING

Optimizing Energy Consumption and Latency in IoT Through Edge Computing in Air–Ground Integrated Network With Deep Reinforcement Learning

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges.In this paper, we propose a framework that facilitates local computing at IoT d

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The influences of seismic load on dynamic deformation properties of rock material under different confining pressures

Long-term geological storage of carbon dioxide in underground engineering is the most economically viable option for reducing emissions of this greenhouse gas to the atmosphere.Underground engineering projects are often subjected to earthquakes during their lives, thus it is essential to investigate the deformation characteristics of surrounding ro

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