Towards the oneM2M standards for building IoT ecosystem: Analysis, implementation and lessons

Kim, Jae-Ho; Choi, Sung Chan; Yun, Jaeseok; Lee, Jang-Won

doi:10.1007/s12083-016-0505-9

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…Due to limited resources and rapid changes in the business environment, the efficiency of innovation has a major impact on the company's survival. Therefore, Kim et al [38] believe that establishing various "standards" is very important. If there is no design, operation, production, procurement, decision-making, and the support of other standards, the process of implementing TI will be complicated.…”

Section: Scholars (Dominguez and Mayrhofermentioning

confidence: 99%

A new decision-marking model to improve the performance of technological innovation project

Chien

Liu

Chang

2022

Preprint

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The promotion of technical and management capabilities is one of the important strategies in government policy to enhance the competitiveness of small and medium enterprises (SMEs). A serial of efficient and accurate decision-making models is then required to achieve this strategic goal. The construction machinery industry belongs to SMEs. It is also essential for infrastructure construction in Taiwan. This study aims to show the ways to improve the performance of SMEs in an uncertain environment. To deal with this issue, the Fuzzy Delphi method and fuzzy analytic hierarchy process (FAHP) are used to find key success factors (KSFs) of SMEs. TOPSIS method is used to find the best project leader for their projects. Also, the MCGP-U method is used to find the optimal resource allocations. In addition, the “plan-do-check-action cycle” is used to emphasize the importance of sustainable improvement. The contributions of the proposed model are as follows: (1) it considers multi-faceted of SMEs to avoid wrong decisions; (2) it can easily obtain the SEM’s KSFs to improve the performance of SMEs; and (3) the results of the study can be used to improve SEMs' managerial fitness and effectiveness.

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Section: Scholars (Dominguez and Mayrhofermentioning

confidence: 99%

A new decision-marking model to improve the performance of technological innovation project

Chien

Liu

Chang

2022

Preprint

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“…It has ports for connecting sensors and a computer system. This hardware can be used in the implementation of IoT, smart homes, patient control, and robotics (Jaeho Kim et al, 2018).…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Machine Learning in Apache Spark Environment for Diagnosis of Diabetes

Saravi¹,

Moghanian²,

Javidi³

et al. 2021

Preprint

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Disease-related data and information collected by physicians, patients, and researchers seem insignificant at first glance. Still, the same unorganized data contain valuable information that is often hidden. The task of data mining techniques is to extract patterns to classify the data accurately. One of the various Data mining and its methods have been used often to diagnose various diseases. In this study, a machine learning (ML) technique based on distributed computing in the Apache Spark computing space is used to diagnose diabetics or hidden pattern of the illness to detect the disease using a large dataset in real-time. Implementation results of three ML techniques of Decision Tree (DT) technique or Random Forest (RF) or Support Vector Machine (SVM) in the Apache Spark computing environment using the Scala programming language and WEKA show that RF is more efficient and faster to diagnose diabetes in big data.

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“…If one or more matches are found, the message is sent to the corresponding subscriber and if no matches are found the message is discarded. The MQTT is intended for constrained systems [10][11].…”

Section: A) Data Oriented Communication Protocolmentioning

confidence: 99%

Considerations on the Deployment of Heterogeneous IoT Devices For Smart Water Networks

Shahra

Romano

2019

2019 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

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Water distribution systems are seeing the increased deployment of new technologies that use Internet of Things (IoT) to gather, analyse and extract useful information from data; further enabling Smart Water Networks (SWNs). IoT type technologies have a huge potential to enable more efficient water resources management. Heterogeneous IoT sensors/devices/technologies from different vendors are starting to be employed in SWNs. The deployment of IoT sensors is a critical issue that significantly affects a wireless sensors network's real-time monitoring performance. The aim of the deployment of IoT sensors (i.e. nodes) for wireless networks is to find the most efficient number, type and locations of sensors that enable satisfying a SWN's hydraulic and water quality requirements (among the others) while considering issues such as energy consumption of the nodes and coverage of the target monitoring area, to mention just a few. This paper provides an overview of the SWN architecture and its components and scrutinises emerging sensor and communication technologies for developing SWNs focussing on the sensing and control, and collection and communication layers of the SWN architecture. Additionally, it presents a selective review of recent literature on issues related to the intelligent deployment of sensors in wireless sensors networks and on sensors deployment for water quality and leak detection/localisation applications in SWNs. The main aim of the literature review carried out and presented here has been to highlight the main challenges of developing IoT-enabled SWNs, so that interesting future research directions for achieving a context-aware IoT framework for SWNs can be identified.

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Towards the oneM2M standards for building IoT ecosystem: Analysis, implementation and lessons

Cited by 19 publications

References 18 publications

A new decision-marking model to improve the performance of technological innovation project

A new decision-marking model to improve the performance of technological innovation project

Machine Learning in Apache Spark Environment for Diagnosis of Diabetes

Considerations on the Deployment of Heterogeneous IoT Devices For Smart Water Networks

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