Research article Special Issues

Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things


  • Received: 18 June 2021 Accepted: 19 August 2021 Published: 30 August 2021
  • These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.

    Citation: Abdullah Lakhan, Mazhar Ali Dootio, Ali Hassan Sodhro, Sandeep Pirbhulal, Tor Morten Groenli, Muhammad Saddam Khokhar, Lei Wang. Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7344-7362. doi: 10.3934/mbe.2021363

    Related Papers:

  • These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.



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    [1] L. A. Mastoi, Q. U. Ain, M. Elhoseny, M. S. Memon, M. A. Mohammed, Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using iot assisted mobile fog cloud, Enterp. Inf. Syst., (2021), 1–23.
    [2] T. Huang, L. Lan, X. Fang, P. An, J. Min, F. Wang, Promises and challenges of big data computing in health sciences, Big Data Res., 2 (2015), 2–11.
    [3] A. Lakhan, M. Ahmad, M. Bilal, A. Jolfaei, R. M. Mehmood, Mobility aware blockchain enabled offloading and scheduling in vehicular fog cloud computing, IEEE Trans. Intell. Transp. Syst., 2021.
    [4] T. Lynn, P. Rosati, A. Lejeune, V. Emeakaroha, A preliminary review of enterprise serverless cloud computing (function-as-a-service) platforms, in 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), (2017), 162–169.
    [5] A. Lakhan, M. S. Memon, M. Elhoseny, M. A. Mohammed, M. Qabulio, M. Abdel-Basset, et al., Cost-efficient mobility offloading and task scheduling for microservices iovt applications in container-based fog cloud network, Cluster Comput., (2021), 1–23.
    [6] A. Lakhan, M. A. Mohammed, A. N. Rashid, S. Kadry, T. Panityakul, K. H. Abdulkareem, et al., Smart-contract aware ethereum and client-fog-cloud healthcare system, Sensors, 21 (2021), 4093. doi: 10.3390/s21124093
    [7] A. Lakhan, M. A. Dootio, T. M. Groenli, A. H. Sodhro, M. S. Khokhar, Multi-layer latency aware workload assignment of e-transport iot applications in mobile sensors cloudlet cloud networks, Electronics, 10 (2021), 1719. doi: 10.3390/electronics10141719
    [8] M. Hussain, L. F. Wei, A. Lakhan, S. Wali, S. Ali, A. Hussain, Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing, Sustainable Comput.: Inf. Syst,, 30 (2021), 100517.
    [9] A. Lakhan, X. Li, Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks, Computing, 102 (2020), 105–139. doi: 10.1007/s00607-019-00733-4
    [10] A. Lakhan, L. Xiaoping, Energy aware dynamic workflow application partitioning and task scheduling in heterogeneous mobile cloud network, in 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB), 2018 (2018), 1–8.
    [11] A. Lakhan, X. Li, Content aware task scheduling framework for mobile workflow applications in heterogeneous mobile-edge-cloud paradigms: Catsa framework, in 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), (2019), 242–249.
    [12] A. Lakhan1, X. Li, Mobility and fault aware adaptive task offloading in heterogeneous mobile cloud environments, EAI Endorsed Trans. Mobile Commun. Appl., 5 (2019), 16.
    [13] J. Yun, Y. Goh, J. M. Chung, Dqn based optimization framework for secure sharded blockchain systems, IEEE Int.Things J., 2020.
    [14] F. Zhang, M. M. Wang, Stochastic congestion game for load balancing in mobile edge computing, IEEE Int. Things J., 2020.
    [15] A. Lakhan, Q. U. A. Mastoi, M. A. Dootio, F. Alqahtani, I. R. Alzahrani, F. Baothman, et al., Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network, Electronics, 10 (2021), 1974. doi: 10.3390/electronics10161974
    [16] F. H. Khoso, A. Lakhan, A. A. Arain, M. A. Soomro, S. Z. Nizamani, A microservice-based system for industrial internet of things in fog-cloud assisted network, Eng. Technol. Appl. Sci. Res., 11 (2021), 7029–7032. doi: 10.48084/etasr.4077
    [17] F. H. Khoso, A. A. Arain, A. Lakhan, A. Kehar, S. Z. Nizamani, Proposing a novel iot framework by identifying security and privacy issues in fog cloud services network, Int. J., 9 (2021), 592–596.
    [18] A. Lakhan, R. Singh, Implementation of etl tool for data warehousing for non-hodgkin lymphoma (nhl) cancer in public sector, pakistan, Int. J., 9 (2021), 7. doi: 10.22201/ceiich.24485705e.2021.24.78946
    [19] A. Lakhan, F. H. Khoso, A. A. Arain, K. Kanwar, Serverless based functions aware framework for healthcare application, Int. J., 9 (2021), 4.
    [20] M. Waseem, A. Lakhan, I. A. Jamali, Data security of mobile cloud computing on cloud server, Open Access Libr. J., 3 (2016), 1–11.
    [21] I. A. Jamali, A. Lakhan, D. Kumar, A. R. Mahessar, Energy efficient task assignment algorithm framework in mo-bile cloud computing, GSJ, 6 (2018), 171.
    [22] A. L. Mujeeb-ur Rehman, Z. Hussain, F. H. Khoso, A. A. Arain, Cyber security intelligence and ethereum blockchain technology for e-commerce, Int. J., 9 (2021), 7. doi: 10.22201/ceiich.24485705e.2021.24.78946
    [23] A. Lakhan, D. K. Sajnani, M. Tahir, M. Aamir, R. Lodhi, Delay sensitive application partitioning and task scheduling in mobile edge cloud prototyping, in International Conference on 5G for Ubiquitous Connectivity, (2018), 59–80.
    [24] D. K. Sajnani, A. R. Mahesar, A. Lakhan, I. A. Jamali, R. Lodhi, M. Aamir, Latency aware optimal workload assignment in mobile edge cloud offloading network, in 2018 IEEE 4th International Conference on Computer and Communications (ICCC), (2018), 658–662.
    [25] D. K. Sajnani, A. R. Mahesar, A. Lakhan, I. A. Jamali, Latency aware and service delay with task scheduling in mobile edge computing, Commun. Network, 10 (2018), 127. doi: 10.4236/cn.2018.104011
    [26] A. H. Sodhro, Z. Luo, A. K. Sangaiah, S. W. Baik, Mobile edge computing based qos optimization in medical healthcare applications, Int. J. Inf. Manage., 45 (2019), 308–318. doi: 10.1016/j.ijinfomgt.2018.08.004
    [27] A. H. Sodhro, S. Pirbhulal, V. H. C. De Albuquerque, Artificial intelligence-driven mechanism for edge computing-based industrial applications, IEEE Trans. Ind. Inf., 15 (2019), 4235–4243. doi: 10.1109/TII.2018.2889692
    [28] M. Muzammal, R. Talat, A. H. Sodhro, S. Pirbhulal, A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks, Inf. Fusion, 53 (2020), 155–164. doi: 10.1016/j.inffus.2019.06.021
    [29] H. Magsi, A. H. Sodhro, F. A. Chachar, S. A. K. Abro, G. H. Sodhro, S. Pirbhulal, Evolution of 5g in internet of medical things, in 2018 international conference on computing, mathematics and engineering technologies (iCoMET), (2018), 1–7.
    [30] T. Zhang, A. H. Sodhro, Z. Luo, N. Zahid, M. W. Nawaz, S. Pirbhulal, et al., A joint deep learning and internet of medical things driven framework for elderly patients, IEEE Access, 8 (2020), 822–832.
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