Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

Abdullah Lakhan,Mazhar Ali Dootio,Qammer Hussain Abbasi,Ibrahim R Alzahrani,Syed Aziz Shah,Fatmah Baothman,Syed Yaseen Shah,Muhammad Saddam Khokhar,Qurat-Ul-Ain Mastoi,Fehaid Alqahtani,Nadeem Anjum

doi:10.3390/electronics10161974

Abstract

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

Highlights

Nowadays, the usage of medical devices based on the Internet of Medical Things (IoMT) network to deal with healthcare issues has been growing progressively [1]
This paper proposes a novel scheduling system for mixing fine-grained and workflow IoMT tasks in distributed and virtual machinebased mobile edge cloud networks to cope with the issues mentioned earlier
Denial of Service (DoS)=1; Waiting for offloading; 21 End Main; In this paper, we suggest implementing a system for performing operations on encrypted data without decrypting them, which will produce the same results after calculations as if we were directly operating on the raw data

Summary

Introduction

The usage of medical devices based on the Internet of Medical Things (IoMT) network to deal with healthcare issues has been growing progressively [1]. The combination of IoMT and healthcare devices can improve the quality of human life and provide better care services and create a more cost-effective system [3]. Many IoMT-based applications have been developed by different shareholders to deal with other diseases, such as E-healthcare sensor-based applications and E-real-time-healthcare applications [4]. These healthcare applications have different classes, such as workflow or fine-grained classes, while providing services to the users [5]. Fine-grained-based applications have only one independent task process. A heartbeat task or ECG monitoring task [6]

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