short-paper

The effects of relative importance of user constraints in cloud of things resource discovery: a case study

Authors:

Júlio C. Estrella,

Alexandre N. Delbem,

Charith Perera,

Stephan Reiff-MarganiecAuthors Info & Claims

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

Pages 245 - 250

https://doi.org/10.1145/2996890.3007867

Published: 06 December 2016 Publication History

Abstract

Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.

References

[1]

L. Abdullah and C. R. Adawiyah. Simple additive weighting methods of multi criteria decision making and applications: A decade review. International Journal of Information Processing and Management, 5(1):39, 2014.

[2]

A. Abraham and L. Jain. Evolutionary multiobjective optimization. Springer, 2005.

Digital Library

[3]

M. Behzadian, S. K. Otaghsara, M. Yazdani, and J. Ignatius. A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17):13051--13069, dec 2012.

Digital Library

[4]

G. Bovet and J. Hennebert. Distributed semantic discovery for web-of-things enabled smart buildings. In New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on, pages 1--5, March 2014.

[5]

M. Caramia and P. Dell'Olmo. Multi-objective Management in Freight Logistics. Springer Science Business Media, 2008.

[6]

D. Carlson and A. Schrader. Ambient ocean: A web search engine for context-aware smart resource discovery. In Internet of Things (iThings), 2014 IEEE International Conference on, and Green Computing and Communications (GreenCom), IEEE and Cyber, Physical and Social Computing(CPSCom), IEEE, pages 177--184, Sept 2014.

Digital Library

[7]

Y. Collette and P. Siarry. Multiobjective Optimization. Springer Berlin Heidelberg, 2004.

[8]

K. Deb. Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, Chichester New York, 2001.

Digital Library

[9]

J. Diaz-Montes, M. AbdelBaky, M. Zou, and M. Parashar. Cometcloud: Enabling software-defined federations for end-to-end application workflows. Internet Computing, IEEE, 19(1):69--73, 2015.

Digital Library

[10]

J. Dodgson, M. Spackman, A. Pearman, and L. Phillips. Multi-criteria analysis: a manual. Department for Communities and Local Government: London, 2009.

[11]

C. Doukas and F. Antonelli. Developing and deploying end-to-end interoperable amp; discoverable iot applications. In Communications (ICC), 2015 IEEE International Conference on, pages 673--678, June 2015.

[12]

F. Gao, M. I. Ali, and A. Mileo. Semantic discovery and integration of urban data streams. In CEUR Workshop Proceedings, volume 1280, pages 15--30, 2014.

Digital Library

[13]

Gartner. Gartner says 6.4 billion connected "things" will be in use in 2016, up 30 percent from 2015. Avaliable in http://www.gartner.com/newsroom/id/3165317, November 2015. Last access: 23/08/2015.

[14]

J.-J. Huang, G.-H. Tzeng, and H.-H. Liu. A revised VIKOR model for multiple criteria decision making - the perspective of regret theory. In Communications in Computer and Information Science, pages 761--768. Springer Science Business Media, 2009.

[15]

A. L. Jaimes, S. Z. Martinez, and C. A. C. Coello. An introduction to multiobjective optimization techniques. 2009.

[16]

P. P. Jayaraman, K. Mitra, S. Saguna, T. Shah, D. Georgakopoulos, and R. Ranjan. Orchestrating quality of service in the cloud of things ecosystem. In 2015 IEEE International Symposium on Nanoelectronic and Information Systems, pages 185--190, Dec 2015.

Digital Library

[17]

A. Kamilaris, K. Papakonstantinou, and A. Pitsillides. Exploring the use of dns as a search engine for the web of things. In Internet of Things (WF-IoT), 2014 IEEE World Forum on, pages 100--105, March 2014.

[18]

F. Khodadadi, A. V. Dastjerdi, and R. Buyya. Simurgh: A framework for effective discovery, programming, and integration of services exposed in IoT. In 2015 International Conference on Recent Advances in Internet of Things (RIoT). Institute of Electrical & Electronics Engineers (IEEE), apr 2015.

[19]

J. Kiljander, A. D'Elia, F. Morandi, P. Hyttinen, J. Takalo-Mattila, A. Ylisaukko-Oja, J.-P. Soininen, and T. Cinotti. Semantic interoperability architecture for pervasive computing and internet of things. Access, IEEE, 2:856--873, 2014.

[20]

A. Kothari, V. Boddula, L. Ramaswamy, and N. Abolhassani. Dqs-cloud: A data quality-aware autonomic cloud for sensor services. In Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2014 International Conference on, pages 295--303, Oct 2014.

[21]

K. Miettinen. Nonlinear multiobjective optimization, volume 12. Springer Science & Business Media, 2012.

[22]

L. H. Nunes, J. C. Estrella, L. H. V. Nakamura, R. M. D. O. Libardi, C. H. G. Ferreira, L. Jorge, C. Perera, and S. Reiff-Marganiec. A distributed sensor data search platform for internet of things environments. International Journal of Services Computing (IJSC), 4(1):1--12, 2016.

[23]

L. H. Nunes, J. C. Estrella, C. Perera, S. Reiff-Marganiec, and A. N. Delbem. Multi-criteria iot resource discovery: A comparative analysis. Software: Practice and Experience, -(-):--, 2016. In print.

[24]

C. Perera, A. Zaslavsky, C. Liu, M. Compton, P. Christen, and D. Georgakopoulos. Sensor search techniques for sensing as a service architecture for the internet of things. IEEE Sensors Journal, 14(2):406--420, 2014.

[25]

C. Perera, A. B. Zaslavsky, P. Christen, and D. Georgakopoulos. Context aware computing for the internet of things: A survey. Communications Surveys & Tutorials, vol.16:414--454, 2014.

[26]

F. Ramezani, A. Memariani, and J. Lu. A dynamic fuzzy multi-criteria group decision support system for manager selection. In Y. Wang and T. Li, editors, Practical Applications of Intelligent Systems, volume 124 of Advances in Intelligent and Soft Computing, pages 265--274. Springer Berlin Heidelberg, 2012.

[27]

K. Romer, B. Ostermaier, F. Mattern, M. Fahrmair, and W. Kellerer. Real-time search for real-world entities: A survey. Proceedings of the IEEE, 98(11):1887--1902, nov 2010.

[28]

S. Sowe, T. Kimata, M. Dong, and K. Zettsu. Managing heterogeneous sensor data on a big data platform: Iot services for data-intensive science. In Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International, pages 295--300, July 2014.

Digital Library

[29]

W.-H. Tsai, W. Hsu, and W.-C. Chou. A gap analysis model for improving airport service quality. Total Quality Management & Business Excellence, 22(10):1025--1040, 2011.

[30]

G. Tzeng and J. Huang. Multiple Attribute Decision Making: Methods and Applications. A Chapman & Hall book. Taylor & Francis, 2011.

[31]

L. Xu and J.-B. Yang. Introduction to multi-criteria decision making and the evidential reasoning approach. 2001.

Cited By

Goudarzi PRahmani AMosleh M(2022)Resource discovery approaches in cloudIoT: a systematic reviewThe Journal of Supercomputing10.1007/s11227-022-04541-078:15(17202-17230)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11227-022-04541-0
Dai HWong RWang HZheng ZVasilakos A(2019)Big Data Analytics for Large-scale Wireless NetworksACM Computing Surveys10.1145/333706552:5(1-36)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3337065
Nunes LEstrella JPerera CReiff-Marganiec SDelbem A(2018)The elimination-selection based algorithm for efficient resource discovery in Internet of Things environments2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC.2018.8319280(1-7)Online publication date: Jan-2018
https://doi.org/10.1109/CCNC.2018.8319280
Show More Cited By

Recommendations

Multi-criteria IoT resource discovery: a comparative analysis

The growth of real-world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between ...
Resource discovery in the Internet of Things integrated with fog computing using Markov learning model
Abstract
Due to high and unpredictable connection delays, privacy gaps, and traffic load of networks connecting cloud computing to end users in many of the Internet of Things (IoT)-based services, some challenges have been created in cloud computing ...
Cloud of Things Modeling for Efficient and Coordinated Resources Provisioning
On the Move to Meaningful Internet Systems. OTM 2017 Conferences
Abstract
The shift towards the Cloud of Things (CoT) requires a seamless integration of Cloud Computing and the Internet of Things (IoT). Such transition promotes a holistic approach for managing and orchestrating cloud and IoT infrastructures. However, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

December 2016

549 pages

ISBN:9781450346160

DOI:10.1145/2996890

General Chairs:
Changjun Jiang
Tongji University, China
,
Omer Rana
Cardiff University, UK
,
Nick Antonopoulos
University of Derby, UK

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

UCC '16

UCC '16: 9th International Conference on Utility and Cloud Computing

December 6 - 9, 2016

Shanghai, China

Acceptance Rates

Overall Acceptance Rate 38 of 125 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
86
Total Downloads

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

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Goudarzi PRahmani AMosleh M(2022)Resource discovery approaches in cloudIoT: a systematic reviewThe Journal of Supercomputing10.1007/s11227-022-04541-078:15(17202-17230)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11227-022-04541-0
Dai HWong RWang HZheng ZVasilakos A(2019)Big Data Analytics for Large-scale Wireless NetworksACM Computing Surveys10.1145/333706552:5(1-36)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3337065
Nunes LEstrella JPerera CReiff-Marganiec SDelbem A(2018)The elimination-selection based algorithm for efficient resource discovery in Internet of Things environments2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC.2018.8319280(1-7)Online publication date: Jan-2018
https://doi.org/10.1109/CCNC.2018.8319280
Bangui HGe MBuhnova BRakrak SRaghay SPitner T(2017)Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading ParadigmJournal of Sensor and Actuator Networks10.3390/jsan60400256:4(25)Online publication date: 13-Nov-2017
https://doi.org/10.3390/jsan6040025

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents