Open Access

ARTICLE

Intelligent Microservice Based on Blockchain for Healthcare Applications

Faisal Jamil¹, Faiza Qayyum¹, Soha Alhelaly², Farjeel Javed³, Ammar Muthanna^4,5,*

1 Department of Computer Engineering, Jeju National University, Jeju-si, 63243, Korea
2 College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
3 SAP Pakistan Liaison Office, Islamabad, 44010, Pakistan
4 Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint Petersburg, 193232, Russia
5 Peoples’ Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya St, Moscow, Moscow, 117198, Russian Federation

* Corresponding Author: Ammar Muthanna. Email:

(This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)

Computers, Materials & Continua 2021, 69(2), 2513-2530. https://doi.org/10.32604/cmc.2021.018809

Received 21 March 2021; Accepted 22 April 2021; Issue published 21 July 2021

Abstract

Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size, and monolithic service designing techniques. This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment. The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT. To demonstrate the proposed model effectiveness, we have used the IoT fitness application as a case study. Based on the designed predictive analytic model, a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness. Moreover, the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan. Finally, the proposed model is evaluated using Hyperledger Caliper in terms of latency, throughput, and resource utilization with varying peers and orderer nodes. The experimental result shows that the proposed model is applicable for diverse resource-constrained blockchain-enabled IoT applications and extensible for several IoT scenarios.

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