Abstract
Along with the development of realtime applications, the freshness of information becomes significant because the overdue information is worthless and useless and even harmful to the right judgement of system. Therefore, The Age of Information (AoI) used for marking the freshness of information is proposed. In Internet of Medical Things (IoMT), which is derived from the requirement of Internet of Thins (IoT) in medicine, high freshness of medical information should be guaranteed. In this paper, we introduce the AoI of medical information when allocating channels for users in IoMT. Due to the advantages of Deep Q‐learning Network (DQN) applied in resource management in wireless network, we propose a novel DQN‐based Channel Allocation (DQCA) algorithm to provide the strategy for channel allocation under the optimization of the system cost considering the AoI and energy consumption of coordinator nodes. Unlike the traditional centralized channel allocation methods, the DQCA algorithm is distributed as each user performs the DQN process separately. The simulation results show that our proposed DQCA algorithm is superior to Greedy algorithm and Q‐learning algorithm in terms of the average AoI, average energy consumption and system cost.
Highlights
Corona Virus Disease 2019 (COVID-19) has caused more than 2.32 million deaths worldwide by February 8th, 2021 [1]
To measure the cost that the system pays for the lack of new information on gateway, we propose a system cost function based on the Age of Information (AoI) and the current energy consumption rate of the nodes
(iii) For the problems raised, we propose a Deep QLearning Network (DQN) based channel allocation algorithm, named DQN-based Channel Allocation (DQCA), which provides channel allocation scheme to minimize the cost on the basis of meeting the requirements of node SNR and residual energy
Summary
Corona Virus Disease 2019 (COVID-19) has caused more than 2.32 million deaths worldwide by February 8th, 2021 [1]. People are forced to stay at home, reduce the trip proportion, and avoid to go to crowded places In this case, both the government, medical staff, or the general public hope to monitor virus infections like COVID-19 and isolate them in time to avoid the spread of the virus on a large scale. This paper only studies the channel allocation problems in the monitoring and transmitting human physiological data. This paper adopts a deep learning method to solve the proposed optimization problem. (iii) For the problems raised, we propose a Deep QLearning Network (DQN) based channel allocation algorithm, named DQCA, which provides channel allocation scheme to minimize the cost on the basis of meeting the requirements of node SNR and residual energy.
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