AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks
نویسندگان
چکیده
As the number of smart connected devices increases day by day, a massive amount tasks are generated various types Internet Things (IoT) devices. Intelligent edge computing is promising enabler in next-generation wireless networks to execute these on proximate servers instead Additionally, regarding execution servers, could provide low-latency environment end users. Within this paper, an artificial intelligence (AI)-empowered fast task method heterogeneous IoT applications proposed reduce decision latency taking into account different system parameters such as deadline task, battery level devices, channel conditions between mobile and server capacity. In scenarios, requests, resource constraints mobility energy consumption main performance considerations. AI-empowered solve multi-device problem formulating it multi-class classification problem. The extensive simulation results demonstrate that framework extremely precise decision-making for offloading computation compared conventional Lyapunov optimization-based algorithm ensuring guaranteed quality experience.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3232073