A systematic literature review on semantic models for IoT-enabled smart campus

2020 ◽  
pp. 1-27
Author(s):  
Soulakshmee D. Nagowah ◽  
Hatem Ben Sta ◽  
Baby Gobin-Rahimbux

Smart communities have recently gained much attention. Researchers have been trying to tackle a number of challenges faced by smart communities. Interoperability is one key challenge that occurs due to different systems using different knowledge representations. To solve interoperability problems, ontologies are seen as a promising solution as they provide a commonly agreed vocabulary for representing data that are understandable by stakeholders of smart communities. Smart communities make use of Internet of Things (IoT) and ubiquitous networks to support communication among objects and devices in such environments. Smart campuses are examples of smart communities. Recently, many articles related to ontologies focusing on smart communities and smart campuses in IoT environments, have been published. This paper presents a Systematic Literature Review that has been conducted using Google Scholar. 18 ontologies for smart communities/smart campuses have been identified and analyzed out of 341 articles from year 2010 to 2019. The review classifies the ontologies in terms of domain, ontologies being reused, availability online, limitations, language adopted and coverage. It additionally discusses on the standards, the level of expressiveness, the ontology development approaches and methodologies adopted by the identified ontologies. Our analysis shows that the identified ontologies have been developed based on different ontological commitments. None of them have come up with a core semantic model that models different collaborating domains in a smart campus such as smart learning, smart management, smart governance, smart room, smart health, smart library and smart parking among others and that enhances cross-domain interoperability in a such an environment. Further details on our findings are presented and discussed in the paper.

Author(s):  
Evaristus Didik Madyatmadja ◽  
Natasha Anugrah Roito Noverya ◽  
Asprina Br Surbakti

2019 ◽  
Vol 26 (4) ◽  
pp. 3-27 ◽  
Author(s):  
Zsuzsanna Tomor ◽  
Albert Meijer ◽  
Ank Michels ◽  
Stan Geertman

Author(s):  
Bernardo Tabuenca ◽  
Sergio Serrano-Iglesias ◽  
Adrian Carruana-Martin ◽  
Cristina Villa-Torrano ◽  
Yannis A. Dimitriadis ◽  
...  

2019 ◽  
Vol 2 (3) ◽  
pp. 194-202
Author(s):  
Umar Al Faruqi

Development of digital transformation towards the information age have made significant change in various sectors and industries, including education. This transformation also needs to consider social problem solving with human centered design approach. One of the learning methods enabled by the technology development of this transformation is smart learning, that aims to improve the quality of learning utilizing intelligent technology in accordance with the learning context. This paper explains systematic literature review conducted as initial study in the research of smart learning for executives. The journals used in this review are various international journals obtained from some reputable journal databases. After going through the filtering process using several inclusion and exclusion criteria, 15 journals were analyzed based on the research questions. From the review, it can be concluded that smart learning can improve the learning process in terms of motivation, engagement, and learning performance. Various information technology is used to enhance the efficiency and effectiveness of smart learning, which provides the learning needs for executive education.


Author(s):  
Gabriel Souto Fischer ◽  
Rodrigo da Rosa Righi ◽  
Vinicius Facco Rodrigues ◽  
Cristiano André da Costa

Internet of Things (IoT) is a constantly growing paradigm that promises to revolutionize healthcare applications and could be associated with several other techniques. Data prediction is another widely used paradigm, where data captured over time is analyzed in order to identify and predict problematic situations that may happen in the future. After research, no surveys that address IoT combined with data prediction in healthcare area exist in the literature. In this context, this work presents a systematic literature review on Internet of Things applied to healthcare area with a focus on data prediction, presenting twenty-three papers about this theme as results, as well as a comparative analysis between them. The main contribution for literature is a taxonomy for IoT systems with data prediction applied to healthcare. Finally, this article presents the possibilities and challenges of exploration in the study area, showing the existing gaps for future approaches.


2020 ◽  
Vol 22 (1) ◽  
pp. 27-40
Author(s):  
Yunita Arafah ◽  
Haryo Winarso

Konsep smart city telah digunakan secara meluas diberbagai bidang, namun secara umum masih mendominasi bidang-bidang yang berhubungan dengan teknologi informasi dan komunikasi, serta komputer dan keteknikan. Padahal tidak hanya itu, pembahasan smart city dalam bidang sosial juga merupakan sebuah prioritas. Hal ini juga dapat dilihat dari kecenderungan evolusi perkembangan konsep smart city dari tahun ke tahun yang terus mengarah dan fokus kepada aspek manusia dan masyarakat di dalamnya, salah satunya yaitu bagaimana manusia sebagai pengguna smart city dapat ikut berpartisipasi dalam menyelesaikan permasalahan kota dan ikut berpartisipasi dalam pembangunan. Tulisan ini bertujuan untuk melihat sejauh mana tingkat partisipasi masyarakat dalam konsep smart city yang telah berjalan, khususnya pada kasus kota-kota di Indonesia, selanjutnya melihat faktor-faktor apa saja yang menjadi prioritas dalam usaha peningkatan dan penguatan partisipasi masyarakat dalam konteks smart city. Studi ini akan fokus pada teori smart city, partisipasi, smart community, smart people, dan smart governance sebagai salah satu karakter pembentuk smart city. Analisa dan pembahasan dilakukan berdasarkan studi literatur, dan menggunakan metodologi systematic literature review melalui rangkuman hasil penelitian-penelitian sebelumnya dengan teknik deskriptif kualitatif.


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