Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.

College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq

Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala Punjab India

Department of Computer Science and Information Engineering Asia University Taichung Taiwan

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

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

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

School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India

School of Information Technology Halmstad University Halmstad Sweden

See more in PubMed

Heuillet A, Couthouis F, Díaz-Rodríguez N. Explainability in deep reinforcement learning. Knowl. Based Syst. 2021;214:106685. doi: 10.1016/j.knosys.2020.106685. DOI

Dai, Y., Wang, G., Muhammad, K. & Liu, S. A closed-loop healthcare processing approach based on deep reinforcement learning. Multimedia Tools and Applications 81, 3107–3129 (2022).

Chen, H., Chen, Z., Lin, F. & Zhuang, P. Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. IEEE Access9, 36008–36018 (2021).

Xiaoding, W. et al. Enabling secure authentication in industrial iot with transfer learning empowered blockchain.IEEE Trans. Ind. Inform.17, 7725–7733 (2021).

Gazori P, Rahbari D, Nickray M. Saving time and cost on the scheduling of fog-based iot applications using deep reinforcement learning approach. Futur. Gener. Comput. Syst. 2020;110:1098–1115. doi: 10.1016/j.future.2019.09.060. DOI

Lakhan, A. et al. Blockchain-enabled cybersecurity efficient iioht cyber-physical system for medical applications. IEEE Trans. Netw. Sci. Eng. 1–14, (2022).

Farahbakhsh F, Shahidinejad A, Ghobaei-Arani M. Multiuser context-aware computation offloading in mobile edge computing based on Bayesian learning automata. Trans. Emerg. Telecommun. Technol. 2021;32:e4127.

Sodhro AH, Sennersten C, Ahmad A. Towards cognitive authentication for smart healthcare applications. Sensors. 2022;22:2101. doi: 10.3390/s22062101. PubMed DOI PMC

Qurat, Khan, F. A., Abbasi, Q. H. et al. Dynamic content and failure aware task offloading in heterogeneous mobile cloud networks. In 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), 1–6 (IEEE, 2020).

Tiwari P, Zhu H, Pandey HM. Dapath: Distance-aware knowledge graph reasoning based on deep reinforcement learning. Neural Netw. 2021;135:1–12. doi: 10.1016/j.neunet.2020.11.012. PubMed DOI

Alatoun K, et al. A novel low-latency and energy-efficient task scheduling framework for internet of medical things in an edge fog cloud system. Sensors. 2022;22:5327. doi: 10.3390/s22145327. PubMed DOI PMC

Weng, J. et al. Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Trans. Dependable Secur. Comput.18(5), 2438–2455 (2019).

Ferrag MA, Maglaras L. Deepcoin: A novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans. Eng. Manag. 2019;67:1285–1297. doi: 10.1109/TEM.2019.2922936. DOI

Singh M, Aujla GS, Singh A, Kumar N, Garg S. Deep-learning-based blockchain framework for secure software-defined industrial networks. IEEE Trans. Ind. Inform. 2020;17:606–616. doi: 10.1109/TII.2020.2968946. DOI

Li, X. Mobility and fault aware adaptive task offloading in heterogeneous mobile cloud environments. EAI Endorsed Trans. Mobile Commun. Appl.5, -16 (2019).

Sajnani, D. K., Tahir, M., Aamir, M. & Lodhi, R. Delay sensitive application partitioning and task scheduling in mobile edge cloud prototyping. In International Conference on 5G for Ubiquitous Connectivity, 59–80 (Springer, 2018).

Waseem M. Data security of mobile cloud computing on cloud server. Open Access Libr. J. 2016;3:1–11.

Khoso, F. H., Arain, A. A., Lakhan, A., Kehar, A. & Nizamani, S. Z. Proposing a novel iot framework by identifying security and privacy issues in fog cloud services network. Int. J.9(5), 592–596 (2021).

Khoso FH, et al. A microservice-based system for industrial internet of things in fog-cloud assisted network. Eng. Technol. Appl. Sci. Res. 2021;11:7029–7032. doi: 10.48084/etasr.4077. DOI

Vahdat Pour, M., Li, Z., Ma, L. & Hemmati, H. A search-based testing framework for deep neural networks of source code embedding. arXiv e-prints arXiv–2101 (2021).

Tapas, N., Merlino, G. & Longo, F. Blockchain-based iot-cloud authorization and delegation. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP), 411–416 (IEEE, 2018).

Uddin MA, Stranieri A, Gondal I, Balasubramanian V. A survey on the adoption of blockchain in iot: Challenges and solutions. Blockchain Res. Appl. 2021;2:100006. doi: 10.1016/j.bcra.2021.100006. DOI

Nartey, C. et al. On blockchain and iot integration platforms: current implementation challenges and future perspectives. Wirel. Commun. Mob. Comput.2021 (2021).

Qiu, C., Yao, H., Jiang, C., Guo, S. & Xu, F. Cloud computing assisted blockchain-enabled internet of things. IEEE Trans. Cloud Comput.10(1), 247–257 (2020).

Wu H, et al. Eedto: an energy-efficient dynamic task offloading algorithm for blockchain-enabled iot-edge-cloud orchestrated computing. IEEE Internet Things J. 2020;8:2163–2176. doi: 10.1109/JIOT.2020.3033521. DOI

Haque, R. et al. Blockchain-based information security of electronic medical records (emr) in a healthcare communication system. In Intelligent Computing and Innovation on Data Science, 641–650 (Springer, 2020).

Chelladurai, U. & Pandian, S. A novel blockchain based electronic health record automation system for healthcare. J. Ambient Intell. Humaniz. Comput. 1–11 (2022).

Sathio, A. A. et al. Pervasive futuristic healthcare and blockchain enabled digital identities-challenges and future intensions. In 2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE), 30–35 (IEEE, 2021).

Lakhan A, et al. Smart-contract aware ethereum and client-fog-cloud healthcare system. Sensors. 2021;21:4093. doi: 10.3390/s21124093. PubMed DOI PMC

Lakhan, A., Mohammed, M. A., Kozlov, S. & Rodrigues, J. J. Mobile-fog-cloud assisted deep reinforcement learning and blockchain-enable iomt system for healthcare workflows.Trans. Emerg. Telecommun. Technol. e43–63 (2021).

Singh AP, et al. A novel patient-centric architectural framework for blockchain-enabled healthcare applications. IEEE Trans. Ind. Inform. 2020;17:5779–5789. doi: 10.1109/TII.2020.3037889. DOI

Oh SH, Lee SJ, Park J. Effective data-driven precision medicine by cluster-applied deep reinforcement learning. Knowl. Based Syst. 2022;256:109877. doi: 10.1016/j.knosys.2022.109877. DOI

Wang L, Xi S, Qian Y, Huang C. A context-aware sensing strategy with deep reinforcement learning for smart healthcare. Pervasive Mob. Comput. 2022;83:101588. doi: 10.1016/j.pmcj.2022.101588. DOI

Rjoub, G., Wahab, O. A., Bentahar, J., Cohen, R. & Bataineh, A. S. Trust-augmented deep reinforcement learning for federated learning client selection.Inf. Syst. Front. 1–18 (2022). PubMed PMC

Talaat, F. M. Effective deep q-networks (edqn) strategy for resource allocation based on optimized reinforcement learning algorithm. Multimed. Tools Appl.81(28), 39945–39961 (2022).

Ali A, et al. An industrial iot-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network. Sensors. 2022;22:572. doi: 10.3390/s22020572. PubMed DOI PMC

Almaiah MA, Hajjej F, Ali A, Pasha MF, Almomani O. A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare iot based cps. Sensors. 2022;22:1448. doi: 10.3390/s22041448. PubMed DOI PMC

Ali A, et al. Security, privacy, and reliability in digital healthcare systems using blockchain. Electronics. 2021;10:2034. doi: 10.3390/electronics10162034. DOI

Ali A, et al. Deep learning based homomorphic secure search-able encryption for keyword search in blockchain healthcare system: A novel approach to cryptography. Sensors. 2022;22:528. doi: 10.3390/s22020528. PubMed DOI PMC

Ali A, et al. A novel secure blockchain framework for accessing electronic health records using multiple certificate authority. Appl. Sci. 2021;11:9999. doi: 10.3390/app11219999. DOI