article

Taxonomy on Ambient Computing: : A Research Methodology Perspective

Author:

Diganta SenguptaAuthors Info & Claims

International Journal of Ambient Computing and Intelligence (IJACI), Volume 11, Issue 1

Pages 1 - 33

https://doi.org/10.4018/IJACI.2020010101

Published: 01 January 2020 Publication History

Abstract

The rise of Internet-of-Things, Cloud infrastructure, and intelligent home solutions has resulted in the growth of Ambient Computing over the past decade. This article is an attempt to cluster the multi-dimensional research in ambient computing into relevant nodes. The author proposes a taxonomy in ambient computing, creating a logical arrangement of such nodes. An algorithm based on the cosine similarity function for calculating the distance between two ‘author keywords' has been used to spawn the inter-nodal relationships. The vocabulary for the research has been generated from IEEE Xplore taking into consideration the relevant journal publications. The taxonomy will guide researchers to the relevant published literature about a certain area of interest instead of exhaustive search into a plethora of published research in ambient computing. The taxonomy exhibits exponential growth of research in ambient computing over the last three years.

References

[1]

Bhatt, C., Dey, N., & Ashour, A. S. (2017). Internet of Things and Big Data Technologies for Next Generation Healthcare. Berlin: Springer.

[2]

Blagoderov, V., Brake, I., Georgiev, T., Penev, L., Roberts, D., Ryrcroft, S., & Smith, V. et al. (2010). Streamlining taxonomic publication: A working example with Scratchpads and ZooKeys. ZooKeys, 50, 17–28. 21594114.

[3]

Boukharrou, R. R., Ilié, J.-M., & Saidouni, D. E. (2017). Time and Space Reasoning for Ambient Systems. International Journal of Ambient Computing and Intelligence, 8(3), 38–57.

[4]

Camina, S. L. (2010). A Comparison of Taxonomy Generation Techniques Using Bibliometric [EECS Thesis]. Massachusetts Institute of technology.

[5]

Cilibrasi, R. L., & Vitanyi, P. M. (2007). The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370–383.

Digital Library

[6]

Dey, N., & Ashour, A. S. (2017). Ambient Intelligence in Healthcare: A State-of-the-Art. Global Journal of Computer Science and Technology, 18-28.

[7]

Dey, N., Hassanien, A. E., Bhatt, C., Ashour, A. S., & Satapathy, S. C. (2018). Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Berlin: Springer.

[8]

Elhayatmy, G., Dey, N., & Ashour, A. S. (2018). Internet of Things based wireless body area network in healthcare. In Internet of things and big data analytics toward next-generation intelligence (pp. 3–20). Springer.

[9]

Eng, K., Douglas, R. J., & Verschure, P. (2005). An interactive space that learns to influence human behavior. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 35(1), 66–77.

Digital Library

[10]

Gaoyong, L., & Huiling, W. (2008). A Method for Evaluating the Service Quality of E-commerce Websites Based on Ontology. Proceedings of the IEEE International Conference on Information Management, Innovation Management and Industrial Engineering. IEEE Press. 10.1109/ICIII.2008.134

[11]

George, K. K., Kumar, C. S., Ramachandran, K. I., & Panda, A. (2015). Cosine distance features for improved speaker verification. Electronics Letters, 51(12), 939–941.

[12]

Gutierrez, C. E., Alsharif, M. R., Cuiwei, H., Khosravy, M., Villa, R., Yamashita, K., & Miyagi, H. (2013). Uncover news dynamic by principal component analysis. ICIC Express Letters, 7(4), 1245–1250.

[13]

Gutierrez, C. E., Alsharif, M. R., Khosravy, M., Yamashita, K., Miyagi, H., & Villa, R. (2014). Main Large Data Set Features Detection by a Linear Predictor Model. Proceedings of the 10th international conference of computational methods in sciences and engineering (pp. 733-737). AIP Conference Publications.

[14]

Gutierrez, C. E., Alsharif, M. R., Yamashita, K., & Khosravy, M. (2014). A tweets mining approach to detection of critical events characteristics using random forest. International Journal of Next-Generation Computing, 5(2), 167–176.

[15]

Hernandez, A. F., & Garcia, N. Y. (2016). Distributed processing using cosine similarity for mapping Big Data in Hadoop. IEEE Latin America Transactions, 14(6), 2857–2861.

[16]

Khosravy, M., Gupta, N., Marina, N., Sethi, I. K., & Asharif, M. R. (2017). Morphological filters: An inspiration from natural geometrical erosion and dilation. In Nature-Inspired Computing and Optimization (pp. 349–379). Springer.

[17]

Mondal, A. S., Sanyal, M., Chattapadhyay, S., & Mondal, K. C. (2019). Performance Analysis of Structured, Un-Structured, and Cloud Storage Systems. International Journal of Ambient Computing and Intelligence, 10(1), 1–29.

[18]

Pal, A. K., Banerjee, S., Dey, N., & Sengupta, D. (2018). IoT Based Home Automation. Proceedings of the IEEE 3rd International Conference for Convergence in Technology (I2CT) (pp. 1-6). IEEE. 10.1109/I2CT.2018.8529487

[19]

Raven, P. H., Berlin, B., & Breedlove, D. E. (1971). The Origins of Taxonomy. Science, 174(4015), 1210–1213. 17806924.

[20]

Sedaaghi, M. H., & Khosravy, M. (2003). Morphological ECG signal preprocessing with more efficient baseline drift removal. Proceedings of the IASTED 2003. Academic Press.

[21]

Sengupta, D. (2019, July 19). Bibliography on Ambient Computing for generation of Taxonomy. Retrieved from https://sites.google.com/site/dstaxambcomp/

[22]

SenguptaD.SultanaM. (2018). Taxonomy of Decimal Multiplier Research. Conference on Algorithms and Applications (ALAP 2018), Springer, (pp. 3-21). Kolkata. 10.1007/978-981-10-8102-6_1

[23]

Shamsi, J. A., & Khojaye, M. A. (2018, June). Understanding Privacy Violations in Big Data Systems. IEEE IT Professional, 20(3), 73–81.

[24]

Thomson, S. A., Pyle, R. A., Ahyong, S. T., Alonso-Zarazaga, M., Ammirati, J., Araya, J. F., & Zhou, H.-Z. et al. (2018). Taxonomy based on science is necessary for global conservation. PLoS Biology, 16(3), 1–12. 29538381.

[25]

Wang, D., Lu, H., & Bo, C. (2015). Visual Tracking via Weighted Local Cosine Similarity. IEEE Transactions on Cybernetics, 45(9), 1838–1850. 25423661.

[26]

Yamin, M., & Sen, A. A. (2018). Improving Privacy and Security of User Data in Location Based Services. International Journal of Ambient Computing and Intelligence, 9(1), 19–42.

Digital Library

[27]

Zaharakis, I., & Komninos, A. (2012). Ubiquitous computing multidisciplinary endeavour. IEEE Latin America Transactions, 10(3), 1850–1852.

Cited By

Majumder SBiswas D(2023)A Bibliometric and Co-Occurrence Analysis of Work-Life BalanceInternational Journal of Information Systems and Supply Chain Management10.4018/IJISSCM.31618216:1(1-11)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.4018/IJISSCM.316182
McKenna H(2021)The Importance of Theory for Understanding Smart Cities: Making a Case for Ambient TheoryDistributed, Ambient and Pervasive Interactions10.1007/978-3-030-77015-0_4(41-54)Online publication date: 24-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-77015-0_4

Index Terms

Taxonomy on Ambient Computing: A Research Methodology Perspective

Index terms have been assigned to the content through auto-classification.

Recommendations

A taxonomy of ubiquitous computing applications

Ubiquitous computing is emerging as a new paradigm in next-generation information technology. This new paradigm has been embodied into tremendous business models and applications through lots of ubiquity-related technologies. In this study, a new ...
Context Aware Computing for Ambient Assisted Living
ICOST 2016: Proceedings of the 14th International Conference on Inclusive Smart Cities and Digital Health - Volume 9677

With the prevalence of wireless technologies, cloud computing and the rapid growth of deployed smart sensors in the past few years, we live in an increasingly interconnected world. These technologies have fostered the dissemination of the Internet of ...
A taxonomy of measures for smart cities
ICEGOV '20: Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance

As the idea of a smart city has developed over the past few decades and become commonplace, so the urge to decide which cities are smarter or smartest, and the need to measure progress of cities towards increasing smartness have emerged. Measuring the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image International Journal of Ambient Computing and Intelligence

International Journal of Ambient Computing and Intelligence Volume 11, Issue 1

Jan 2020

135 pages

ISSN:1941-6237

EISSN:1941-6245

Issue’s Table of Contents

Copyright © 2020.

Publisher

IGI Global

United States

Publication History

Published: 01 January 2020

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Majumder SBiswas D(2023)A Bibliometric and Co-Occurrence Analysis of Work-Life BalanceInternational Journal of Information Systems and Supply Chain Management10.4018/IJISSCM.31618216:1(1-11)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.4018/IJISSCM.316182
McKenna H(2021)The Importance of Theory for Understanding Smart Cities: Making a Case for Ambient TheoryDistributed, Ambient and Pervasive Interactions10.1007/978-3-030-77015-0_4(41-54)Online publication date: 24-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-77015-0_4

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents