Automated Decision Support IoT Framework

Lunardi, Willian T.; Amaral, Leonardo Albernaz; Marczak, Sabrina; Hessel, Fabiano; Voos, Holger

doi:10.1109/etfa.2016.7733572

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Several mathematical aspects of IoT devices signals processing are presented in [20,21]. The incorporation of IoT manages to improve efficiency in energy consumption we can see in the [3,6,7,17].…”

Section: Literature Reviewmentioning

confidence: 99%

Automation Factors Influencing the Operation of IoT in Health Institutions: A Decision Support Methodology

Oddershede¹,

Macuada

Quezada

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Health institutions are adopting new technologies for their processes through automation by means of the concept of "Internet of Things" (IoT). Hence, offering innovative tools, applications and technology for the collection of key data and information, which is then integrated and consolidated, covering the different systems and their collaborators. The necessity of receiving quality medical services is essential in the public Policy of any country. The increasing demand for having an adequate number of medical specialists, pharmacies and medications stock, dental and mental health coverage and other, together with the minimization of the waiting list and patient care time have been a crucial concern. Under this context, it is valuable to redesign the processes planning and its coordination through the use of Information & Communications Technology (ICT) and IoT that unifies the systems. Based on previous research, the general purpose is to generate a system model to examine healthcare quality of service and corroborate its effectiveness in a real environment. The aim of this paper focus on the development of a decision support model to define key areas where the inclusion of IoT would sustain the efficiency in health care service. The research methodology is based on case study, integrating planning processes, data analysis, scoring method that interacts with multicriteria approach. A pilot case study is pursued in health institutions in Chile, determining critical factors and the current automation level system appraisal to generate actions of improvement in processes that show poor service quality. The results give rise to the development of an investment plan that can be converted into action plans for a health institution.

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Section: Literature Reviewmentioning

confidence: 99%

Automation Factors Influencing the Operation of IoT in Health Institutions: A Decision Support Methodology

Oddershede¹,

Macuada

Quezada

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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show abstract

“…The proposed inventory management system was simulated with inventory management scenarios using IoT technology. Lunardi et al [8] presented a decision support IoT framework with existing rule-based decision management systems (i.e., COMPaaS [39] and COBASEN [40]); the framework was used to support the discovery and selection of IoT devices for IoT applications. This paper does not directly mention self-adaptation.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In this context, self-adaptive software can be applied to dynamic IoT environments because it alters its behavior or structure in dynamic environments at runtime [5,6]. In addition, there have been several studies on the application of self-adaptive software in various IoT environments such as inventory management systems [7], smart rooms [8], healthcare [9,10], middleware [11], emergency handling systems [12], smart city [13,14], and IoT networks [15,16,17]. However, each study has its own aspects, distinct characteristics, and target domains.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Framework Based on MAPE Loop for Internet of Things

Lee

Seo

Kim

2019

Sensors

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The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.

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“…Lunardi, Amaral, Marczak, Hessel, and Voos [16] presented a decision support IoT framework. By analyzing others IoT middleware systems and frameworks the researchers found that they do not provide methods to allow automated decision making based on device data stream.…”

Section: Related Workmentioning

confidence: 99%

The Use of Fuzzy Logic for Online Monitoring of Manufacturing Machine: An Intelligent System

Novilla¹,

Balute²,

Gonzales³

2017

CCS Archive

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Internet of Things (IoT) has positioned itself at the front of rapid technological advancements in order to lessen human labor. The concept of making the world smart arises from the basis that “things” can be connected using the Internet. In this paper, the researchers will be describing an effective implementation of an intelligent monitoring system for manufacturing machine unit which is used in industry environment. The propose system design can be installed in manufacturing machines in order to solve management problems, maintenance, shortens the mean time to repair and predict mean time to fail. The researchers have designed a system based on internet of thing for monitoring using the fuzzy logic approach. It consists of monitoring the normal activities of the manufacturing machines in order to build a reference of its condition; then a real-time monitoring and analysis of gathered data from the sensors is accomplished. The status is carried out using a Fuzzy Logic based network. It will give the users the current activity of the manufacturing machine and will also provide information about health status. This system had been incorporated through internet using host, network, Ethernet module, embedded system gateway, sensors, microcontroller unit (MCU) and other components. This paper discusses how monitoring system can be implemented and how the use of cloud computing technology along with IoT devices can be used so that the data collected by these devices can be safely stored, monitored and analyzed.

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Automated Decision Support IoT Framework

Cited by 5 publications

References 32 publications

Automation Factors Influencing the Operation of IoT in Health Institutions: A Decision Support Methodology

Automation Factors Influencing the Operation of IoT in Health Institutions: A Decision Support Methodology

Self-Adaptive Framework Based on MAPE Loop for Internet of Things

The Use of Fuzzy Logic for Online Monitoring of Manufacturing Machine: An Intelligent System

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