1. Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things
- Author
-
Muhammad Saddam Khokhar, Lei Wang, Abdullah Lakhan, Ali Hassan Sodhro, Sandeep Pirbhulal, Mazhar Ali Dootio, and Tor Morten Groenli
- Subjects
Schedule ,IoMT ,Computer science ,workflow ,Internet of Things ,Data security ,Medieteknik ,Scheduling (computing) ,Blockchain ,Computer Systems ,serverless ,Server ,Teknik och teknologier ,iomt ,QA1-939 ,Humans ,Media and Communication Technology ,Computer Security ,Cost efficiency ,business.industry ,Applied Mathematics ,Quality of service ,General Medicine ,Computational Mathematics ,Datorsystem ,Workflow ,service selection ,Modeling and Simulation ,cost-efficient scheduling ,Engineering and Technology ,The Internet ,General Agricultural and Biological Sciences ,business ,Delivery of Health Care ,TP248.13-248.65 ,Mathematics ,Computer network ,Biotechnology - Abstract
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., Funding information: This work is financially supported by the Research grant of PIFI 2020 (2020VBC0002), China.
- Published
- 2021