Lightweight Proof of Game (LPoG): A Proof of Work (PoW)’s Extended Lightweight Consensus Algorithm for Wearable Kidneys

In healthcare, interoperability is widely adopted in the case of cross-departmental or specialization cases. As the human body demands multiple specialized and cross-disciplined medical experiments, interoperability of business entities like different departments, different specializations, the involvement of legal and government monitoring issues etc. are not sufficient to reduce the active medical cases. A patient-centric system with high capability to collect, retrieve, store or exchange data is the demand for present and future times. Such data-centric health processes would bring automated patient medication, or patient self-driven trusted and high satisfaction capabilities. However, data-centric processes are having a huge set of challenges such as security, technology, governance, adoption, deployment, integration etc. This work has explored the feasibility to integrate resource-constrained devices-based wearable kidney systems in the Industry 4.0 network and facilitates data collection, liquidity, storage, retrieval and exchange systems. Thereafter, a Healthcare 4.0 processes-based wearable kidney system is proposed that is having the blockchain technology advantages. Further, game theory-based consensus algorithms are proposed for resource-constrained devices in the kidney system. The overall system design would bring an example for the transition from the specialization or departmental-centric approach to data and patient-centric approach that would bring more transparency, trust and healthy practices in the healthcare sector. Results show a variation of 0.10 million GH/s to 0.18 million GH/s hash rate for the proposed approach. The chances of a majority attack in the proposed scheme are statistically proved to be minimum. Further Average Packet Delivery Rate (ADPR) lies between 95% to 97%, approximately, without the presence of outliers. In the presence of outliers, network performance decreases below 80% APDR (to a minimum of 41.3%) and this indicates that there are outliers present in the network. Simulation results show that the Average Throughput (AT) value lies between 120 Kbps to 250 Kbps.

[1]  Yilong Yang,et al.  MedChain: Efficient Healthcare Data Sharing via Blockchain , 2019, Applied Sciences.

[2]  Peng Zhang,et al.  Design of Blockchain-Based Apps Using Familiar Software Patterns to Address Interoperability Challenges in Healthcare , 2017 .

[3]  Peter B. Nichol,et al.  Co-Creation of Trust for Healthcare: The Cryptocitizen Framework for Interoperability with Blockchain , 2016 .

[4]  H. Krumholz,et al.  Blockchain Technology: Applications in Health Care , 2017, Circulation. Cardiovascular quality and outcomes.

[5]  Jesse M. Ehrenfeld,et al.  Blockchain for Healthcare: The Next Generation of Medical Records? , 2018, Journal of Medical Systems.

[6]  Qi Xia,et al.  BBDS: Blockchain-Based Data Sharing for Electronic Medical Records in Cloud Environments , 2017, Inf..

[7]  Pijush Kanti Dutta Pramanik,et al.  WBAN: Driving e-healthcare Beyond Telemedicine to Remote Health Monitoring , 2019, Telemedicine Technologies.

[8]  Alok Aggarwal,et al.  Design and Analysis of Lightweight Trust Mechanism for Accessing Data in MANETs , 2014, KSII Trans. Internet Inf. Syst..

[9]  Sheng Liu,et al.  Blockchain-Based Data Preservation System for Medical Data , 2018, Journal of Medical Systems.

[10]  Bhaskar Krishnamachari,et al.  A Survey of Blockchain-Based Strategies for Healthcare , 2020, ACM Comput. Surv..

[11]  Ben A. Amaba,et al.  Blockchain technology innovations , 2017, 2017 IEEE Technology & Engineering Management Conference (TEMSCON).

[12]  Said Jai-Andaloussi,et al.  Blockchain and IoT for Security and Privacy: A Platform for Diabetes Self-management , 2018, 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech).

[13]  Alok Aggarwal,et al.  Novel Trusted Hierarchy Construction for RFID Sensor–Based MANETs Using ECCs , 2015 .

[14]  Uttam Ghosh,et al.  Blockchain and Fog Based Architecture for Internet of Everything in Smart Cities , 2020, Future Internet.

[15]  Ravikiran Vatrapu,et al.  Blockchain-based Personal Health Data Sharing System Using Cloud Storage , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[16]  Christian Catalini,et al.  Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability , 2018, Computational and structural biotechnology journal.

[17]  Khaled Shuaib,et al.  Introducing blockchains for healthcare , 2017, 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA).

[18]  Diego Reforgiato Recupero,et al.  Internet of Entities (IoE): A Blockchain-based Distributed Paradigm for Data Exchange between Wireless-based Devices , 2019, SENSORNETS.

[19]  Marko Hölbl,et al.  A Systematic Review of the Use of Blockchain in Healthcare , 2018, Symmetry.

[20]  Kevin J. Peterson,et al.  A Blockchain-Based Approach to Health Information Exchange Networks , 2016 .

[21]  G. Nahler Glomerular filtration rate (GFR) , 2020, Definitions.

[22]  Zhang Zhe,et al.  A review on consensus algorithm of blockchain , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[23]  Douglas C. Schmidt,et al.  Applying Software Patterns to Address Interoperability in Blockchain-based Healthcare Apps , 2017, ArXiv.

[24]  M. A. Engelhardt,et al.  Hitching Healthcare to the Chain: An Introduction to Blockchain Technology in the Healthcare Sector , 2017 .

[25]  Gautam Srivastava,et al.  A Decentralized Privacy-Preserving Healthcare Blockchain for IoT , 2019, Sensors.

[26]  Hyeon-Eui Kim,et al.  Blockchain distributed ledger technologies for biomedical and health care applications , 2017, J. Am. Medical Informatics Assoc..

[27]  Douglas C. Schmidt,et al.  Metrics for assessing blockchain-based healthcare decentralized apps , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[28]  Xin Huang,et al.  A Secure System For Pervasive Social Network-Based Healthcare , 2016, IEEE Access.

[29]  Anand Nayyar,et al.  Healthcare models and algorithms for privacy and security in healthcare records , 2019, Security and Privacy of Electronic Healthcare Records: Concepts, paradigms and solutions.

[30]  P. Sanjeevikumar,et al.  Enhancement of Security and Handling the Inconspicuousness in IoT Using a Simple Size Extensible Blockchain , 2020 .

[31]  Antonio Puliafito,et al.  Blockchain and IoT Integration: A Systematic Survey , 2018, Sensors.

[32]  Yury Yanovich,et al.  Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare , 2015, Oncotarget.

[33]  Alok Aggarwal,et al.  Design and Analysis of Lightweight Trust Mechanism for Secret Data using Lightweight Cryptographic Primitives in MANETs , 2016, Int. J. Netw. Secur..

[34]  Adarsh Kumar,et al.  Proof of Game (PoG): A Game Theory Based Consensus Model , 2019, Sustainable Communication Networks and Application.

[35]  Mads Nibe Stausholm,et al.  How to Use Blockchain for Diabetes Health Care Data and Access Management: An Operational Concept , 2018, Journal of diabetes science and technology.

[36]  Thaier Hayajneh,et al.  Healthcare Blockchain System Using Smart Contracts for Secure Automated Remote Patient Monitoring , 2018, Journal of Medical Systems.

[37]  J. Riddell,et al.  Blockchain Technology: A Data Framework to Improve Validity, Trust, and Accountability of Information Exchange in Health Professions Education. , 2018, Academic medicine : journal of the Association of American Medical Colleges.