Como os objetos da Internet das Coisas se comportam? Uma análise através de métricas quantitativas
Abstract
Everyday objects have been connected promoting the Internet of Things (IoT). In this cybernetic environment, mobility is a factor that IoT's solutions must be aware. Also, IoT devices eventually will do social ties emerging the Social Internet of Things (SIoT). However, there is a lack of empiric data or even synthetic ones that allow the study of mobility patterns and interactions of IoT's entities. In this way, this paper aims to adapt an existing mobility model to mimic mobile IoT's devices and then apply metrics to quantify their interactions. Also, we run different routing protocols solutions for delayed tolerant communication over this model. The results highlights the importance of mobile objects in the IoT context and the need for deep research in this field. All data employed in this work is available online.
References
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H. Z. Asl, A. Iera, L. Atzori, and G. Morabito. How often social objects meet each other? analysis of the properties of a social network of iot devices based on real data. In 2013 IEEE Global Communications Conference (GLOBECOM), pages 2804–2809, 2013. doi: 10.1109/GLOCOM.2013.6831499.
L. Atzori, A. Iera, G. Morabito, and M. Nitti. The social internet of things (siot)–when social networks meet the internet of things: Concept, architecture and network characterization. Computer networks, 56(16):3594–3608, 2012.
L. Atzori, C. Campolo, B. Da, R. Girau, A. Iera, G. Morabito, and S. Quattropani. Smart devices in the social loops: Criteria and algorithms for the creation of the social links. Future Generation Computer Systems, 97:327–339, 2019.
G. Bigwood, D. Rehunathan, M. Bateman, T. Henderson, and S. Bhatti. Exploiting selfreported social networks for routing in ubiquitous computing environments. In 2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, pages 484–489. IEEE, 2008.
BonnMotion. BonnMotion a mobility scenario generation and analysis too. Downloaded from http://sys.cs.uos.de/bonnmotion/index.shtml, abr 2016.
M. Castro, A. J. Jara, and A. F. Skarmeta. An analysis of m2m platforms: Challenges In 2012 Sixth International Conference and opportunities for the internet of things. on Innovative Mobile and Internet Services in Ubiquitous Computing, pages 757–762, 2012. doi: 10.1109/IMIS.2012.184.
S. Chang, E. Pierson, P. W. Koh, J. Gerardin, B. Redbird, D. Grusky, and J. Leskovec. Mobility network models of covid-19 explain inequities and inform reopening. Nature, 589(7840):82–87, 2021.
F. R. de Souza, A. C. Domingues, P. O. Vaz de Melo, and A. A. Loureiro. Mocha: A tool for mobility characterization. In Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 281–288, 2018.
N. Eagle, A. S. Pentland, and D. Lazer. Inferring friendship network structure by using mobile phone data. Proceedings of the national academy of sciences, 106(36):15274– 15278, 2009.
D. Hristova, M. J. Williams, M. Musolesi, P. Panzarasa, and C. Mascolo. Measuring urban social diversity using interconnected geo-social networks. 25th International World Wide Web Conference, WWW 2016, pages 21–30, 2016.
R. L. Inocêncio and F. N. Ribeiro. Inferring Cultural Similarity among Brazilian States Based on Data from Social Media Advertising Platforms, page 261–268. Association for Computing Machinery, New York, NY, USA, 2020. ISBN 9781450381963.
W. Kassab and K. A. Darabkh. A–z survey of internet of things: Architectures, protocols, applications, recent advances, future directions and recommendations. Journal of Network and Computer Applications, 163:102663, 2020.
A. Keränen, J. Ott, and T. Kärkkäinen. The one simulator for dtn protocol evaluation. In Proceedings of the 2nd international conference on simulation tools and techniques, pages 1–10, 2009.
V. Kostakos and J. Venkatanathan. Making friends in life and online: Equivalence, microcorrelation and value in spatial and transpatial social networks. In 2010 IEEE Second International Conference on Social Computing, pages 587–594. IEEE, 2010.
A. Lindgren, A. Doria, and O. Schelén. Probabilistic routing in intermittently connected networks. ACM SIGMOBILE mobile computing and communications review, 7(3):19– 20, 2003.
C. Marche, L. Atzori, and M. Nitti. A dataset for performance analysis of the social internet of things. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pages 1–5. IEEE, 2018.
C. Marche, L. Atzori, V. Pilloni, and M. Nitti. How to exploit the social internet of things: Query generation model and device profiles’ dataset. Computer Networks, 174:107248, 2020.
A. Mei and J. Stefa. Swim: A simple model to generate small mobile worlds. In IEEE INFOCOM 2009, pages 2106–2113. IEEE, 2009.
P. Meroni, S. Gaito, E. Pagani, and G. P. Rossi. CRAWDAD dataset the unimi/pmtr. Downloaded from http://www.crawdad.org/unimi/pmtr, sep 2010.
A. M. Meslin, N. d. L. R. Rodriguez, and M. Endler. Supporting multiple smart-city applications based on musanet, a common iomt middleware. In Anais do XVIII Workshop em Clouds e Aplicações, pages 13–26. SBC, 2020.
B. P. Santos, L. A. Silva, C. Celes, J. B. Borges, B. S. P. Neto, M. A. M. Vieira, L. F. M. Vieira, O. N. Goussevskaia, and A. Loureiro. Internet das coisas: da teoria à prática. Minicursos SBRC-Simpósio Brasileiro de Redes de Computadores e Sistemas Distribudos, 31, 2016.
B. P. Santos, L. F. M. Vieira, and A. A. F. Loureiro. Routing and mobility management in the internet of things. In Anais Estendidos do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 161–168. SBC, 2020.
J. Surowiecki. The Wisdom of Crowds. Anchor, 2005. ISBN 0385721706. J. M. Tjensvold. Comparison of the ieee 802.11, 802.15. 1, 802.15. 4 and 802.15. 6 wireless standards. In IEEE: September, volume 18, 2007.
D. Zinoviev. Complex network analysis in Python: Recognize-construct-visualizeanalyze-interpret. Pragmatic Bookshelf, 2018. ISBN 9781680502695.
Published
2021-08-16
How to Cite
J. JÚNIOR, Jamisson; FIGUEIREDO, Thiago S.; TORRES, Luiz C. B.; SANTOS, Bruno P..
Como os objetos da Internet das Coisas se comportam? Uma análise através de métricas quantitativas. In: URBAN COMPUTING WORKSHOP (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 56-69.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17104.
