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Title	Dynamic Resource Management In Mec Powered By Edge Intelligence For Smart City Internet Of Things
ID_Doc	21374
Authors	Wan X.
Year	2024
Published	Journal of Grid Computing, 22, 1
DOI	http://dx.doi.org/10.1007/s10723-024-09749-3
Abstract	The Internet of Things (IoT) has become an infrastructure that makes smart cities possible. is both accurate and efficient. The intelligent production industry 4.0 period has made mobile edge computing (MEC) essential. Computationally demanding tasks can be delegated from the MEC server to the central cloud servers for processing in a smart city. This paper develops the integrated optimization framework for offloading tasks and dynamic resource allocation to reduce the power usage of all Internet of Things (IoT) gadgets subjected to delay limits and resource limitations. A Federated Learning FL-DDPG algorithm based on the Deep Deterministic Policy Gradient (DDPG) architecture is suggested for dynamic resource management in MEC networks. This research addresses the optimization issues for the CPU frequencies, transmit power, and IoT device offloading decisions for a multi-mobile edge computing (MEC) server and multi-IoT cellular networks. A weighted average of the processing load on the central MEC server (PMS), the system’s overall energy use, and the task-dropping expense is calculated as an optimization issue. The Lyapunov optimization theory formulates a random optimization strategy to reduce the energy use of IoT devices in MEC networks and reduce bandwidth assignment and transmitting power distribution. Additionally, the modeling studies demonstrate that, compared to other benchmark approaches, the suggested algorithm efficiently enhances system performance while consuming less energy. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Author Keywords	Deep reinforcement learning; Internet of Things; Lyapunov optimization theory; Mobile edge computing; Smart buildings

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