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

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 155 ◽  
Author(s):  
Tuan Anh Nguyen ◽  
Dugki Min ◽  
Eunmi Choi
Keyword(s):  
Virtual Machine ◽  
Hierarchical Model ◽  
Hierarchical Modeling ◽  
Smart Factory ◽  
Analysis Framework ◽  
Security Measures ◽  
Modeling Framework ◽  
System Architectures ◽  
Modeling And Analysis

Modeling a complete Internet of Things (IoT) infrastructure is crucial to assess its availability and security characteristics. However, modern IoT infrastructures often consist of a complex and heterogeneous architecture and thus taking into account both architecture and operative details of the IoT infrastructure in a monolithic model is a challenge for system practitioners and developers. In that regard, we propose a hierarchical modeling framework for the availability and security quantification of IoT infrastructures in this paper. The modeling methodology is based on a hierarchical model of three levels including (i) reliability block diagram (RBD) at the top level to capture the overall architecture of the IoT infrastructure, (ii) fault tree (FT) at the middle level to elaborate system architectures of the member systems in the IoT infrastructure, and (iii) continuous time Markov chain (CTMC) at the bottom level to capture detailed operative states and transitions of the bottom subsystems in the IoT infrastructure. We consider a specific case-study of IoT smart factory infrastructure to demonstrate the feasibility of the modeling framework. The IoT smart factory infrastructure is composed of integrated cloud, fog, and edge computing paradigms. A complete hierarchical model of RBD, FT, and CTMC is developed. A variety of availability and security measures are computed and analyzed. The investigation of the case-study’s analysis results shows that more frequent failures in cloud cause more severe decreases of overall availability, while faster recovery of edge enhances the availability of the IoT smart factory infrastructure. On the other hand, the analysis results of the case-study also reveal that cloud servers’ virtual machine monitor (VMM) and virtual machine (VM), and fog server’s operating system (OS) are the most vulnerable components to cyber-security attack intensity. The proposed modeling and analysis framework coupled with further investigation on the analysis results in this study help develop and operate the IoT infrastructure in order to gain the highest values of availability and security measures and to provide development guidelines in decision-making processes in practice.

Why Does Construct Validity Matter in Measuring Implementation Fidelity? A Methodological Case Study

2021 ◽  
pp. 153450842199877
Author(s):  
Wilhelmina van Dijk ◽  
A. Corinne Huggins-Manley ◽  
Nicholas A. Gage ◽  
Holly B. Lane ◽  
Michael Coyne
Keyword(s):  
Construct Validity ◽  
Reading Instruction ◽  
Model Building ◽  
Implementation Fidelity ◽  
Modeling Framework ◽  
Relative Rank ◽  
Rank Ordering ◽  
Adherence Data ◽  
Funded Project

In reading intervention research, implementation fidelity is assumed to be positively related to student outcomes, but the methods used to measure fidelity are often treated as an afterthought. Fidelity has been conceptualized and measured in many different ways, suggesting a lack of construct validity. One aspect of construct validity is the fidelity index of a measure. This methodological case study examined how different decisions in fidelity indices influence relative rank ordering of individuals on the construct of interest and influence our perception of the relation between the construct and intervention outcomes. Data for this study came from a large State-funded project to implement multi-tiered systems of support for early reading instruction. Analyses were conducted to determine whether the different fidelity indices are stable in relative rank ordering participants and if fidelity indices of dosage and adherence data influence researcher decisions on model building within a multilevel modeling framework. Results indicated that the fidelity indices resulted in different relations to outcomes with the most commonly used fidelity indices for both dosage and adherence being the worst performing. The choice of index to use should receive considerable thought during the design phase of an intervention study.

scholarly journals Agent-Based Modeling Framework for Electric Vehicle Adoption Transition in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Dita Novizayanti ◽  
Eko Agus Prasetio ◽  
Manahan Siallagan ◽  
Sigit Puji Santosa
Keyword(s):  
Electric Vehicles ◽  
Data Analytics ◽  
Agent Based Modeling ◽  
Analysis Framework ◽  
Modeling Framework ◽  
Agent Based ◽  
Twitter Data ◽  
Decision Making Processes ◽  
Electric Vehicle Adoption ◽  
Technical Features

Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

scholarly journals School learning climate in the lens of parental involvement and school leadership: lessons for inclusiveness among public schools

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jason Alinsunurin
Keyword(s):  
Public Schools ◽  
School Improvement ◽  
Parental Involvement ◽  
School Leadership ◽  
Hierarchical Modeling ◽  
Substantial Reduction ◽  
Learning Climate ◽  
Modeling Framework ◽  
Research Attention ◽  
School Learning

Abstract Prior literature has shown that school learning climate is critical in helping individual learners meet their educational objectives. In this paper, the role of parental involvement in shaping the school learning climate is explored within a multilevel and hierarchical modeling framework using data from the 2015 PISA round. As the schools’ social and relational character, we find that reducing learning barriers is a critical challenge for school leadership. A welcoming environment for parents, as well as the effective design of effective forms of two-way communications, are positively associated with a substantial reduction in the barriers to improving teacher management’s learning climate. We also find that public schools facing social and educational inclusiveness challenges can dramatically enhance their learning environment by activating specific parental involvement mechanisms. Similarly, principal’s leadership in framing and communicating goals and curricular development to the school is also found to be significant for inclusiveness. However, parental involvement is also found to have potential tensions with school management. The worsening of the learning climate may arise due to pressures brought about by laws requiring parental involvement in schools. Because the learning climate is composed of a wide variety of relationships between and within schools, this work demonstrates that parental involvement is an integral part of school leadership and the school improvement process. Further research attention is encouraged to understand the tensions between teacher roles, principal leadership, and parental involvement through employing other quantitative or qualitative research designs.

Optimal Flow Control and Single Split Architecture Exploration for Fluid-Based Thermal Management

2018 ◽  
Author(s):  
Satya R. T. Peddada ◽  
Daniel R. Herber ◽  
Herschel C. Pangborn ◽  
Andrew G. Alleyne ◽  
James T. Allison
Keyword(s):  
Flow Control ◽  
Thermal Management ◽  
High Performance ◽  
Dynamic Models ◽  
Cooling System ◽  
Rooted Tree ◽  
Modeling Framework ◽  
System Architectures ◽  
Tree Graphs ◽  
Heat Loads

High-performance cooling is often necessary for thermal management of high power density systems. Both human intuition and vast experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This paper presents a design framework supporting comprehensive exploration of a class of single phase fluid-based cooling architectures. The candidate cooling system architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow control problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components having variable sets of heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4,051 architectures. In addition, cooling system architectures capable of steady-state operation under a given loading are identified.

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