Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.

College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq

Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq

Department of Computer System Engineering and Technology Dawood University of Engineering and Technology Karachi City 74800 Sindh Pakistan

Department of Cybernetics and Biomedical Engineering VSB Technical University of Ostrava 70800 Ostrava Czech Republic

Department of Cybersecurity and Computer Science Dawood University of Engineering and Technology Karachi City 74800 Sindh Pakistan

Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Department of Industrial Engineering Turkish Naval Academy National Defence University 34942 Tuzla Istanbul Turkey

Department of Telecommunications VSB Technical University of Ostrava 70800 Ostrava Czech Republic

Information and Communication Technology Research Group Scientific Research Center Al Ayen University Thi Qar Iraq

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