A Hierarchical Modeling and Analysis Framework for Availability and Security Quantification of IoT Infrastructures

Nguyen, Tuan Anh; Dai, Min; Choi, Eunmi

doi:10.3390/electronics9010155

Cited by 22 publications

(12 citation statements)

References 65 publications

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“…• IoHT Infrastructure -[i1]: Pure performance of data transactions between IoT sensors and internal/external clients (e.g., medical professionals, medical physicians, family members) is the main focus of the modeling, therefore the involvement of physical components and their operational availability are minimized. And that, failure and recovery behaviors of components are not considered in the modeling for performance evaluation [33]. -[i2]: Our main focus is (i) to explore the bottleneck in the considered IoHT architecture in realtime medical data transmission, (ii) to explore the impact of configuration alteration in fog and cloud layers on the performance metrics and (iii) to realize the performance-related trade-offs between internal clients at local places and distant clients.…”

Section: C: Assumptions and Discussionmentioning

confidence: 99%

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Silva

Nguyen

Fé³

et al. 2021

IEEE Access

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Due to the non-stop and rapid spreading of virus pandemics all over the world, traditional healthcare monitoring capabilities of hospitals and/or medical centers are under a severe over-load. Modern computing infrastructures with the harmony of various layers of computing paradigms (e.g., cloud/fog/edge computing) for healthcare monitoring are apparently the essential computing backbone that help access and process instantly the medical data of every single patient at the very edge of the healthcare system to combat with global or regional virus contagion. Previous studies proposed different computing system architectures for healthcare monitoring but few works considered the evaluation of pure performance of medical data transmission in a comprehensive manner. In this paper, we proposed an M/M/c/K queuing network model for the performance evaluation of an Internet of Healthcare Things (IoHT) infrastructure in association with a three layer cloud/fog/edge computing continuum. The model considers a life cycle of medical data from body-attached IoT sensors in edge layer all the way to local clients (e.g., local medical doctors, physicians) through fog layer and to remote clients (e.g., medical professionals, patient's family members) through cloud layer. Furthermore, we also explore the impact of the alteration in system configuration and computing capability of computing layers in two scenarios on various performance metrics. Critical performance metrics related to quality of service are evaluated in a comprehensive manner, such as (i) mean response time of medical data transmission to fog (local) clients and to cloud (remote) clients, (ii) utilization of cloud/fog/edge computing layers, (iii) service throughput, (iv) number of medical messages in a period of time, and (v) drop rate. The simulation results pinpoint bottle-neck parameters and configurations of the IoHT infrastructure's system architecture in relation to the frequency of medical data collection for health check of patients. Thus, the findings of this study can help improve medical administration in hospitals and healthcare centers and help design computing infrastructures in accordance for medical monitoring in the severe circumstances of virus pandemics.

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Section: C: Assumptions and Discussionmentioning

confidence: 99%

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Silva

Nguyen

Fé³

et al. 2021

IEEE Access

Self Cite

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“…Moreover, the increase in energy efficiency is associated with the need to apply the developed methods and tools with a high level of intellectualization based on the machine learning, artificial neural networks, etc. [36][37][38][39][40], and the synthesis of a multilevel [41] energy efficiency management system at an enterprise should be implemented with the use of system approach.…”

Section: Related Workmentioning

confidence: 99%

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

Teslyuk¹,

Tsmots²,

ml.³

et al. 2022

Computers, Materials &Amp; Continua

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To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption. One of the possible ways is proposed in this research. A multi-level hierarchical system for energy efficiency management of the enterprise is designed, it is based on the modular principle providing rapid modernization. The novelty of the work is the development of new and improvement of the existing methods and models, in particular: 1) models for dynamic analysis of IT tools for data acquisition and processing (DAAP) in multilevel energy management systems, which are based on Petri nets; 2) method of synthesis of DAAP tools in energy efficiency management information systems (EEMIS) of the enterprise which provides a reduction in hardware and time costs from 10% to 40%; 3) method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges, it reduces the cost and energy consumption; 4) method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times. The proposed methods, models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse, as a result, energy efficiency has increased by 12%-25% (depending on season and peculiarities of growing plants).

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“…The third criterion is the type of model chosen to evaluate the system. Most papers chose Petri nets [25] and/or with hierarchical models [26] to evaluate their systems. The variation of types observed for the used Petri nets is considerable, ranging from colored Petri nets to Hierarchical Context-Aware Aspect-Oriented Petri nets.…”

Section: Related Workmentioning

confidence: 99%

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

et al. 2021

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Applications in the Internet of Things (IoT) context continuously generate large amounts of data. The data must be processed and monitored to allow rapid decision making. However, the wireless connection that links such devices to remote servers can lead to data loss. Thus, new forms of a connection must be explored to ensure the system’s availability and reliability as a whole. Unmanned aerial vehicles (UAVs) are becoming increasingly empowered in terms of processing power and autonomy. UAVs can be used as a bridge between IoT devices and remote servers, such as edge or cloud computing. UAVs can collect data from mobile devices and process them, if possible. If there is no processing power in the UAV, the data are sent and processed on servers at the edge or in the cloud. Data offloading throughout UAVs is a reality today, but one with many challenges, mainly due to unavailability constraints. This work proposes stochastic Petri net (SPN) models and reliability block diagrams (RBDs) to evaluate a distributed architecture, with UAVs focusing on the system’s availability and reliability. Among the various existing methodologies, stochastic Petri nets (SPN) provide models that represent complex systems with different characteristics. UAVs are used to route data from IoT devices to the edge or the cloud through a base station. The base station receives data from UAVs and retransmits them to the cloud. The data are processed in the cloud, and the responses are returned to the IoT devices. A sensitivity analysis through Design of Experiments (DoE) showed key points of improvement for the base model, which was enhanced. A numerical analysis indicated the components with the most significant impact on availability. For example, the cloud proved to be a very relevant component for the availability of the architecture. The final results could prove the effectiveness of improving the base model. The present work can help system architects develop distributed architectures with more optimized UAVs and low evaluation costs.

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A Hierarchical Modeling and Analysis Framework for Availability and Security Quantification of IoT Infrastructures

Cited by 22 publications

References 65 publications

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

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