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dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)spa
dc.contributor.authorEscorcia-Gutierrez, José
dc.contributor.authorMansour, Romany F.
dc.contributor.authorLeal, Esmeide
dc.contributor.authorVillanueva, Jair
dc.contributor.authorJiménez-Cabas, Javier
dc.contributor.authorSoto, Carlos
dc.contributor.authorSoto-Diaz, Roosvel
dc.identifier.citationEscorcia-Gutierrez, J., Mansour, R.F., Leal, E. et al. Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems. Mobile Netw Appl (2023).
dc.description.abstractDue to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods.eng
dc.format.extent1 páginaspa
dc.publisherSpringer Netherlandsspa
dc.rights© 2023 Springer Nature Switzerland AG. Part of Springer Nature.
dc.titlePrivacy preserving blockchain with energy aware clustering scheme for iot healthcare systemseng
dc.typeArtículo de revistaspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.relation.ispartofjournalMobile Networks and Applicationsspa
dc.relation.referencesAlqaralleh BA, Vaiyapuri T, Parvathy VS, Gupta D, Khanna A, Shankar K (2021) Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things environment. Personal and Ubiquitous Computing, pp 1–11spa
dc.relation.referencesDwivedi AD, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):326spa
dc.relation.referencesAbdolkhani R, Gray K, Borda A, DeSouza R (2019) Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open (ooz036).
dc.relation.referencesVeeramakali T, Siva R, Sivakumar B, Mahesh PS, Krishnaraj N (2021) An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. The Journal of Supercomputing 77:9576–9596spa
dc.relation.referencesUddin MA, Stranieri A, Gondal I, Balasubramanian V (2018) Continuous patient monitoring with a patient centric agent: a block architecture. IEEE Access 6:32700–32726spa
dc.relation.referencesUddin MA, Stranieri A, Gondal I, Balasubramanian (2018) A patient agent to manage blockchains for remote patient monitoring. Stud Health Technol Inform 254:105–115spa
dc.relation.referencesTuli S, Mahmud R, Tuli S, Buyya R (2018) Fogbus: a blockchain-based lightweight framework for edge and fog computing, arXiv: 1811.11978spa
dc.relation.referencesLi Z-t, Chen Q, Zhu G-m, Choi Y-j, Sekiya H (2015) A low latency, energy efficient MAC protocol for wireless sensor networks. Int J Distrib Sens Netw 11(8):1–9spa
dc.relation.referencesWang W, Garofalakis M, Ramchandran K (2007) Distributed sparse random projection for refinable approximation, IEEE 6th International Symposium on Information Processing in Sensor Networks, pp 331–339spa
dc.relation.referencesUllah F, Ullah I, Khan A, Uddin MI, Alyami H, Alosaimi W (2020) Enabling clustering for privacy-aware data dissemination based on Medical Healthcare-IoTs (MH-IoTs) for wireless body area network. Journal of Healthcare Engineering 2020:1–10spa
dc.relation.referencesShukla S, Thakur S, Hussain S, Breslin JG, Jameel SM (2021) Identification and authentication in healthcare Internet-of-Things using integrated fog computing based blockchain model. Internet of Things 15:100422spa
dc.relation.referencesDwivedi AD, Malina L, Dzurenda P, Srivastava G (2019) Optimized blockchain model for internet of things based healthcare applications. In: 2019 42nd international conference on telecommunications and signal processing (TSP). IEEE, pp 135–139spa
dc.relation.referencesShynu PG, Menon VG, Kumar RL, Kadry S, Nam Y (2021) Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing. IEEE Access 9:45706–45720spa
dc.relation.referencesGuo X, Lin H, Wu Y, Peng M (2020) A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Futur Gener Comput Syst 113:407–417spa
dc.relation.referencesHonar Pajooh H, Rashid M, Alam F, Demidenko S (2021) Multi-layer blockchain-based security architecture for internet of things. Sensors 21(3):772spa
dc.relation.referencesKumar R, Tripathi R (2021) Towards design and implementation of security and privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs technology. The Journal of Supercomputing 77:7916–7955spa
dc.relation.referencesHossein KM, Esmaeili ME, Dargahi T (2019) Blockchain-based privacy-preserving healthcare architecture. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, pp 1–4spa
dc.relation.referencesBhattacharya P, Mehta P, Tanwar S, Obaidat MS, Hsiao KF (2020) HeaL: A blockchain-envisioned signcryption scheme for healthcare IoT ecosystems. In: 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE, pp 1–6spa
dc.relation.referencesAlzubi JA (2021) Blockchain-based Lamport Merkle Digital signature: authentication tool in IoT healthcare. Comput Commun 170:200–208spa
dc.relation.referencesRay PP, Chowhan B, Kumar N, Almogren A (2021) BIoTHR: Electronic health record servicing scheme in IoT-blockchain ecosystem. IEEE Internet of Things Journal 8(13):10857–10872spa
dc.relation.referencesFrikha T, Chaari A, Chaabane F, Cheikhrouhou O, Zaguia A (2021) Healthcare and fitness data management using the IoT-based blockchain platform. Journal of Healthcare Engineering 2021:1–12spa
dc.relation.referencesZaabar B, Cheikhrouhou O, Jamil F, Ammi M, Abid M (2021) HealthBlock: A secure blockchain-based healthcare data management system. Comput Netw 200:108500spa
dc.relation.referencesMansour RF (2022) Blockchain assisted clustering with Intrusion Detection System for Industrial Internet of Things environment. Expert Systems with Applications 207:117995spa
dc.relation.referencesYang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249spa
dc.relation.referencesGomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626spa
dc.relation.referencesNguyen TT (2021) Enhanced sunflower optimization for placement distributed generation in distribution system. Int J Electr Comput Eng 11(1):107spa
dc.relation.referencesSuh S, Lee H, Lukowicz P, Lee YO (2021) CEGAN: classification enhancement generative adversarial networks for unraveling data imbalance problems. Neural Netw 133:69–86spa
dc.relation.referencesNguyen GN, Le Viet NH, Devaraj AFS, Gobi R, Shankar K (2020) Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks. Sustain Comput Inform Syst 28:100464spa
dc.relation.referencesDua D, and Graff C (2019) UCI Machine Learning Repository []. Irvine, CA: University of California, School of Information and Computer Sciencespa
dc.relation.referencesBhuvaneeswari R, Sudhakar P, Prabakaran G (2019) Heart disease prediction model based on gradient boosting tree (GBT) classification algorithm. Int J Recent Technol Eng 8(2):41–51spa
dc.subject.proposalArtificial intelligenceeng
dc.subject.proposalHealthcare systemeng
dc.subject.proposalInternet of thingsfra
dc.subject.proposalEnergy efficiencyeng

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Atribución 4.0 Internacional (CC BY 4.0)
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional (CC BY 4.0)