research-article

The role of autonomous aggregators in IoT multi-core systems

Authors:

Basil Nikolopoulos,

Alexandros C. Dimopoulos,

Mara Nikolaidou,

George Dimitrakopoulos,

Dimosthenis AnagnostopoulosAuthors Info & Claims

IoT '17: Proceedings of the Seventh International Conference on the Internet of Things

Article No.: 17, Pages 1 - 8

https://doi.org/10.1145/3131542.3131548

Published: 22 October 2017 Publication History

Abstract

The Internet of Things constitutes a prominent field, integrating smart devices and people into complex systems that may vary in scale. To ensure the constant availability and performance of provided services, alternative distributed architectures should be explored, promoting system scalability. To this end, alternative architectures for the IoT are proposed. Commonly an intermediate layer consisting of aggregators, controlling sensors and actuators and providing a service interface to IoT applications, is incorporated in such architectures. To promote scalability of IoT systems, aggrerators should to operate as autonomous entities. For an aggregator to become autonomous, self-management policies should be enforced. In the paper, we discuss autonomous aggregator software, running on multi-core IoT systems to efficiently implement such policies. A demonstrator for smart buildings, developed as a proof of concept for the proposed concepts, is also presented.

References

[1]

A. Ahmed and E. Ahmed. 2016. A survey on mobile edge computing. In 2016 10th International Conference on Intelligent Systems and Control (ISCO). 1--8.

[2]

Unai Alegre, Juan Carlos Augusto, and Tony Clark. 2016. Engineering context-aware systems and applications: A survey. Journal of Systems and Software 117 (2016), 55--83.

Digital Library

[3]

Satoshi Asano, Takeshi Yashiro, and Ken Sakamura. 2016. Device collaboration framework in IoT-aggregator for realizing smart environment. In TRON Symposium (TRONSHOW), 2016. IEEE, 1--9.

[4]

Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010a. The Internet of Things: A survey. Computer Networks 54, 15 (oct 2010), 2787--2805.

Digital Library

[5]

Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010b. The internet of things: A survey. Computer networks 54, 15 (2010), 2787--2805.

Digital Library

[6]

Sanjoy Baruah, Vincenzo Bonifaci, Gianlorenzo D'Angelo, Haohan Li, Alberto Marchetti-Spaccamela, Nicole Megow, and Leen Stougie. 2012. Scheduling real-time mixed-criticality jobs. IEEE Trans. Comput. 61, 8 (2012), 1140--1152.

Digital Library

[7]

Claudio Bettini, Oliver Brdiczka, Karen Henricksen, Jadwiga Indulska, Daniela Nicklas, Anand Ranganathan, and Daniele Riboni. 2010. A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing 6, 2 (2010), 161--180.

Digital Library

[8]

George Bravos and et.al. 2015. An autonomic management framework for multi-criticality smart building applications. In 13th INDIN Conference. IEEE, 1378--1385.

[9]

Alan Burns and Robert Davis. 2013. Mixed criticality systems-a review - 8th edition. Department of Computer Science, University of York, Tech. Rep (2013).

[10]

Guanling Chen, Ming Li, and David Kotz. 2008. Data-centric middleware for context-aware pervasive computing. Pervasive and mobile computing 4, 2 (2008), 216--253.

Digital Library

[11]

M. Chiang and T. Zhang. 2016. Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal 3, 6 (Dec 2016), 854--864.

[12]

Marco Crasso, Alejandro Zunino, and Marcelo Campo. 2008. Easy web service discovery: A query-by-example approach. Science of Computer Programming 71, 2 (2008), 144--164.

Digital Library

[13]

Anind K Dey, Gregory D Abowd, and Daniel Salber. 2001. A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-computer interaction 16, 2 (2001), 97--166.

Digital Library

[14]

Alexandros C Dimopoulos and et.al. 2016. A multi-core context-aware management architecture for mixed-criticality smart building applications. In 11th SOSE Conference. IEEE, 1--6.

[15]

Schahram Dustdar and Wolfgang Schreiner. 2005. A survey on web services composition. International journal of web and grid services 1, 1 (2005), 1--30.

Digital Library

[16]

Debanjan Ghosh, Raj Sharman, H. Raghav Rao, and Shambhu Upadhyaya. 2007. Self-healing systems --- survey and synthesis. Decision Support Systems 42, 4 (jan 2007), 2164--2185.

Digital Library

[17]

Tao Gu, HC Qian, Jian Kang Yao, and Hung Keng Pung. 2003. An architecture for flexible service discovery in OCTOPUS. In Computer Communications and Networks, 2003. ICCCN 2003. Proceedings. The 12th International Conference on. IEEE, 291--296.

[18]

Dominique Guinard, Vlad Trifa, Stamatis Karnouskos, Patrik Spiess, and Domnic Savio. 2010. Interacting with the soa-based internet of things: Discovery, query, selection, and on-demand provisioning of web services. IEEE transactions on Services Computing 3, 3 (2010), 223--235.

Digital Library

[19]

Karen Henricksen, Jadwiga Indulska, and Andry Rakotonirainy. 2002. Modeling context information in pervasive computing systems. In International Conference on Pervasive Computing. Springer, 167--180.

Digital Library

[20]

Tim Kindberg and John Barton. 2001. A web-based nomadic computing system. Computer Networks 35, 4 (2001), 443--456.

Digital Library

[21]

Jeff Kramer and Jeff Magee. 2007. Self-managed systems: an architectural challenge. In 2007 Future of Software Engineering. IEEE Computer Society, 259--268.

Digital Library

[22]

Chi Harold Liu, Bo Yang, and Tiancheng Liu. 2014. Efficient naming, addressing and profile services in Internet-of-Things sensory environments. Ad Hoc Networks 18 (2014), 85--101.

[23]

David Luckham. 2015. Event Processing for Business: Organizing the Real-Time Enterprise. Wiley Publishers.

[24]

Basil Nikolopoulos and et.al. 2016. Embedded intelligence in smart cities through multi-core smart building architectures: Research achievements and challenges. In 10th RCIS Conference. IEEE, 1--2.

[25]

Ismael Peña-López and others. 2005. ITU Internet report 2005: the internet of things. (2005).

[26]

C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos. 2014. Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys Tutorials 16, 1 (First 2014), 414--454.

[27]

Mazeiar Salehie and Ladan Tahvildari. 2009. Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS) 4, 2 (2009), 14.

Digital Library

[28]

Michael F. Schwartz, Alan Emtage, Brewster Kahle, and B. Clifford Neuman. 1992. A comparison of internet resource discovery approaches. Computing Systems 5, 4 (1992), 461--493.

[29]

Huseyin Ugur Yildiz, Kemal Bicakci, Bulent Tavli, Hakan Gultekin, and Davut Incebacak. 2016. Maximizing WSN lifetime by communication/ computation energy optimization of non-repudiation security service: Node level versus network level strategies. Ad Hoc Networks 37 (2016), 301--323.

Digital Library

[30]

YIN Yuehong, Yan Zeng, Xing Chen, and Yuanjie Fan. 2016. The Internet of Things in Healthcare: An Overview. Journal of Industrial Information Integration 1 (2016), 3--13.

Cited By

Nikolopoulos VNikolaidou MVoreakou MAnagnostopoulos D(2022)Context Diffusion in Fog Colonies: Exploring Autonomous Fog Node Operation Using ECTORASIoT10.3390/iot30100053:1(91-108)Online publication date: 18-Jan-2022
https://doi.org/10.3390/iot3010005
Nikolopoulos BVoreakou MNikolaidou MAnagnostopoulos D(2019)Enhancing Context-Awareness in Autonomous Fog Nodes for IoT Systems2019 IEEE International Conference on Edge Computing (EDGE)10.1109/EDGE.2019.00034(113-115)Online publication date: Jul-2019
https://doi.org/10.1109/EDGE.2019.00034
Saheb TIzadi L(2019)Paradigm of IoT Big Data Analytics in Healthcare Industry: A Review of Scientific literature and Mapping of Research TrendsTelematics and Informatics10.1016/j.tele.2019.03.005Online publication date: Mar-2019
https://doi.org/10.1016/j.tele.2019.03.005

Recommendations

Intrusion detection systems for IoT-based smart environments: a survey

One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are ...
A Survey on IoT Privacy Issues and Mitigation Techniques
ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

Internet of Things (IoT) has emerged as a global network, which intelligently connects different devices or systems which are having self-configuring capabilities. The key idea is to bind or to connect miscellaneous devices or objects via wireless or ...
Internet of things (IoT): a survey on architecture, enabling technologies, applications and challenges
IML '17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning

Convergence of the two technologies- Internet and sensing networks creates a paradigm, Internet of Things (IoT) which allows direct machine-to-machine(M2M) communication. The IoT has transformed human-to-human and human-to-machine communication to ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IoT '17: Proceedings of the Seventh International Conference on the Internet of Things

October 2017

211 pages

ISBN:9781450353182

DOI:10.1145/3131542

Copyright © 2017 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: 22 October 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

ARTEMIS Joint Undertaking

Conference

IoT '17

IoT '17: 7th International Conference on the Internet of Things

October 22 - 25, 2017

Linz, Austria

Acceptance Rates

Overall Acceptance Rate 28 of 84 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
94
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Nikolopoulos VNikolaidou MVoreakou MAnagnostopoulos D(2022)Context Diffusion in Fog Colonies: Exploring Autonomous Fog Node Operation Using ECTORASIoT10.3390/iot30100053:1(91-108)Online publication date: 18-Jan-2022
https://doi.org/10.3390/iot3010005
Nikolopoulos BVoreakou MNikolaidou MAnagnostopoulos D(2019)Enhancing Context-Awareness in Autonomous Fog Nodes for IoT Systems2019 IEEE International Conference on Edge Computing (EDGE)10.1109/EDGE.2019.00034(113-115)Online publication date: Jul-2019
https://doi.org/10.1109/EDGE.2019.00034
Saheb TIzadi L(2019)Paradigm of IoT Big Data Analytics in Healthcare Industry: A Review of Scientific literature and Mapping of Research TrendsTelematics and Informatics10.1016/j.tele.2019.03.005Online publication date: Mar-2019
https://doi.org/10.1016/j.tele.2019.03.005

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