NB-IoT Application on Decision Support System of Building Information Management

Lin, Hsin‐Piao; Jung, Chun-Yao; Huang, Teng‐Yi; Hendrick, Hendrick; Wang, Zhi-Hao

doi:10.1007/s11277-020-07389-w

Cited by 14 publications

(2 citation statements)

References 22 publications

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“…It also supports a tremendous number of devices within a single cell. Using the smart environment concept is not only for monitoring but also to control the situation from worsening based on sensor measurements (Lin et al, 2020 ). NB-IoT has been utilized in mobile computing, disaster management, clinical health, and remote monitoring to name a few (Chen et al, 2019 ; Xie et al, 2019 ; Lin et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Situation aware intelligent reasoning during disaster situation in smart cities

et al. 2022

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Investigating prior methodologies, it has come to our knowledge that in smart cities, a disaster management system needs an autonomous reasoning mechanism to efficiently enhance the situation awareness of disaster sites and reduce its after-effects. Disasters are unavoidable events that occur at anytime and anywhere. Timely response to hazardous situations can save countless lives. Therefore, this paper introduces a multi-agent system (MAS) with a situation-awareness method utilizing NB-IoT, cyan industrial Internet of things (IIOT), and edge intelligence to have efficient energy, optimistic planning, range flexibility, and handle the situation promptly. We introduce the belief-desire-intention (BDI) reasoning mechanism in a MAS to enhance the ability to have disaster information when an event occurs and perform an intelligent reasoning mechanism to act efficiently in a dynamic environment. Moreover, we illustrate the framework using a case study to determine the working of the proposed system. We develop ontology and a prototype model to demonstrate the scalability of our proposed system.

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Section: Introductionmentioning

confidence: 99%

Situation aware intelligent reasoning during disaster situation in smart cities

et al. 2022

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“…Machine Learning methods has been used in many areas such as in smart building [7], biomedical [8], and industries [9]. The machine learning method, Principal Component Analysis (PCA), is also implemented in e-Nose such as fruit classification through fruit odors.…”

Section: Introductionmentioning

confidence: 99%

Odor Classification in Cattle Ranch based on Electronic Nose

Humaira

Hidayat

Wang

et al. 2021

Int. J. Data. Science.

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Unpleasant smell and pollution are the side effects during the cattle ranch activities. That is the reason why the cattle ranch is placed far from the housing residents. The cattle ranch areas are usually not covered by the internet network, but it is also important to monitoring the pollutant in the cattle ranch. The pollutant gases are also produced during the cattle ranch activities such as hydrogen, oxygen, methane, and carbon dioxide. To classify the odor or unpleasant smell in the air, the electronic Nose (e-Nose) become an effective system to monitor and classify the odor in real time. This research, we proposed an e-Nose system that able to classify the odor in cattle ranch. The Backpropagation method is selected to create the e-Nose model. This e-Nose system is able to transmit data to server without the internet network. The Lora Network has been applied by using point to point method. The web application is also made to display the real time data monitoring and prediction of the odor. Based on our test, the e-Nose accuracy is 99% in real time prediction.

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Design of intelligent manufacturing IoT sensing system for polymer process monitoring

Wang,

Li,

2023

Int J Adv Manuf Technol

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NB-IoT Application on Decision Support System of Building Information Management

Cited by 14 publications

References 22 publications

Situation aware intelligent reasoning during disaster situation in smart cities

Situation aware intelligent reasoning during disaster situation in smart cities

Odor Classification in Cattle Ranch based on Electronic Nose

Design of intelligent manufacturing IoT sensing system for polymer process monitoring

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