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Title	Uav-Assisted Intelligent Iot Service Provisioning In Infrastructure-Less Environments
ID_Doc	59281
Authors	Zheng Q.; Shen Z.; Jin J.; Lei Z.; Cheung T.; Xiang W.
Year	2024
Published	Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
DOI	http://dx.doi.org/10.1109/AIoT63253.2024.00043
Abstract	With the exponential advancement of beyond fifth/sixth generation (B5G/6G) mobile systems, coupled with the powerful capabilities of the Internet of Things (IoT) driven by artificial intelligence (AI), intelligent information services have become ubiquitous in various environments including the modern smart city. However, providing these intelligent services in infrastructure-less environments, such as remote or rural areas, still presents substantial challenges due to geographical and economic constraints. To address these challenges, we develop an AI-empowered IoT platform for infrastructure-less environments. The platform's reach is extended through non-terrestrial networks (NTNs), which incorporate unmanned aerial vehicles (UAVs) and satellites, thereby ensuring seamless and efficient communication coverage. In order to enhance the efficiency of task allocation, a dual-deep Q-learning (D- DQN) scheme is proposed for task sorting and task allocation, which simplifies decision-making complexity. We establish an experimental environment that simulates the IoT network services in infrastructure-less environments. Extensive experimental results demonstrate that our proposed system delivers superior performance against other baseline methods: it exhibits enhanced energy efficiency, with improvements seen in both task processing ratio and accuracy. © 2024 IEEE.
Author Keywords	Internet of Things; reinforcement learning; service provisioning; Unmanned aerial vehicles

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