Planning a secure and reliable IoT-enabled FOG-assisted computing infrastructure for healthcare

Outlet Title

Cluster Computing

Document Type

Article

Publication Date

2022

Abstract

Transmitting electronic medical records (EMR) and other communication in modern Internet of Things (IoT) healthcare ecosystem is both delay and integrity-sensitive. Transmitting and computing volumes of EMR data on traditional clouds away from healthcare facilities is a main source of trust-deficit using IoT-enabled applications. Reliable IoT-enabled healthcare (IoTH) applications demand careful deployment of computing and communication infrastructure (CnCI). This paper presents a FOG-assisted CnCI model for reliable healthcare facilities. Planning a secure and reliable CnCI for IoTH networks is a challenging optimization task. We proposed a novel mathematical model (i.e., integer programming) to plan FOG-assisted CnCI for IoTH networks. It considers wireless link interfacing gateways as a virtual machine (VM). An IoTH network contains three wirelessly communicating nodes: VMs, reduced computing power gateways (RCPG), and full computing power gateways (FCPG). The objective is to minimize the weighted sum of infrastructure and operational costs of the IoTH network planning. Swarm intelligence-based evolutionary approach is used to solve IoTH networks planning for superior quality solutions in a reasonable time. The discrete fireworks algorithm with three local search methods (DFWA-3-LSM) outperformed other experimented algorithms in terms of average planning cost for all experimented problem instances. The DFWA-3-LSM lowered the average planning cost by 17.31%, 17.23%, and 18.28% when compared against discrete artificial bee colony with 3 LSM (DABC-3-LSM), low-complexity biogeography-based optimization (LC-BBO), and genetic algorithm, respectively. Statistical analysis demonstrates that the performance of DFWA-3-LSM is better than other experimented algorithms. The proposed mathematical model is envisioned for secure, reliable and cost-effective EMR data manipulation and other communication in healthcare.

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