Markovian with Federated Deep Recurrent Neural Network for Edge-IoMT to Improve Healthcare in Smart Cities

The architectural design of smart cities should prioritize the provision of critical medical services. This involves establishing improved connectivity and leveraging supercomputing capabilities to enhance the quality of services (QoS) offered to residents. Edge computing is vital in healthcare applications by enabling low network latencies necessary for real-time data processing. By implementing edge computing, smart cities can benefit from reduced latency, increased bandwidth, and improved power consumption efficiency. In the context of Mobile Edge Computing (MEC), the study proposes a novel approach called the Markovian Decision Process with Federated Deep Recurrent Neural Network (MDP-FDRNN) as the primary algorithm for managing resource allocation. MEC focuses on utilizing edge computing capabilities to process data and perform computations at the network's edges. The conducted tests demonstrate that the MDP-FDRNN algorithm is superior and well-suited for effectively resolving high-processing traffic at the network's edges. It significantly reduces processing time, particularly crucial for healthcare operations related to patients' health problems. By employing the MDP-FDRNN algorithm in resource allocation management, smart cities can efficiently utilize their edge computing infrastructure to handle complex processing tasks. The superior performance of this algorithm in reducing processing time showcases its potential to support critical healthcare operations within smart cities, thereby enhancing the overall quality of healthcare services provided to residents. This article underscores the significance of implementing appropriate technology, including edge computing and the IoM, in developing prosperous smart cities. It also highlights the effectiveness of the MDP-FDRNN algorithm in managing resource allocation and addressing processing challenges at the network's edges, particularly in healthcare operations.