On Mining IoT Data for Evaluating the Operation of Public Educational Buildings

Zhu, Na; Anagnostopoulos, Aris; Chatzigiannakis, Ioannis

doi:10.1109/percomw.2018.8480226

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The aim stated in [23] is to identify the influence of thermal conditions in classrooms on learning, to facilitate specific measures to improve these conditions. The research is motivated by the fact that the expense of ensuring energy efficiency in schools represents a very large amount of money allocated from the budget of educational institutions.…”

Section:  Identification Of Potentially Useful Studies;mentioning

confidence: 99%

Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

Simionescu,

Danubianu,

Gradinaru

et al. 2024

IJACSA

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The quality of education is one of the pillars of sustainable development, as set out in "The 2030 Agenda for Sustainable Development", adopted by all United Nations Member States in 2015. Recent social and technological developments, as well as events such as the COVID-19 pandemic or conflicts in many parts of the world, have led to essential changes in the way education processes are carried out. In addition, they have made it possible to generate, collect and store large amounts of data related to these processes, data that can hide useful information for decisions that, in the medium or long term, can lead to a significant increase in the quality of education. Uncovering this information is the subject of Educational Data Mining. To understand the state-of-the-art reflected by recent developments, trends, theories, methodologies, and applications in this field, in the European Union, we considered it appropriate to conduct a systematic and critical literature review. Our paper aims to identify, analyze, and synthesize relevant information from these articles, both to build a foundation for further studies and to identify gaps or unexplored issues that can be addressed in future research. The analysis is based on research identified in three international databases recognized for content quality: Scopus, Science direct, and IEEEXplore.

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Section:  Identification Of Potentially Useful Studies;mentioning

confidence: 99%

Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

Simionescu,

Danubianu,

Gradinaru

et al. 2024

IJACSA

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“…Contemporary application requirements for decreased latency and semi-autonomous operation: The constantly increasing numbers of interconnected nodes introduce severe challenges for cloud deployments aiming to provide uninterrupted services since connectivity irregularities will statistically become more often and much more difficult to predict [54,55]. The anticipated advent of 5G [56,57] will resolve some of the most common causes of compromised networking, but since low latency and guaranteed robustness are prerequisites for existing infrastructure as well, one should examine fog computing as a method for addressing these issues.…”

Section: Fog Computingmentioning

confidence: 99%

A Smart Water Metering Deployment Based on the Fog Computing Paradigm

Amaxilatis¹,

Chatzigiannakis

Tselios

et al. 2020

Applied Sciences

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In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system’s design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system.

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Internet of Everything

Chatzigiannakis¹,

Tselios²

2021

Intelligent Computing for Interactive System Design

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On Mining IoT Data for Evaluating the Operation of Public Educational Buildings

Cited by 4 publications

References 18 publications

Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

A Smart Water Metering Deployment Based on the Fog Computing Paradigm

Internet of Everything

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