Um Simulador para o Cálculo e Otimização da Age of Information (AoI) em Sistemas Ciberfísicos

  • Paulo César Prandel UnB
  • Priscila Solis Barreto UnB

Abstract


The Age of Information (AoI) emerges as a new concept and set of metrics that represent the degree of freshness that a monitor has in relation to a remote entity or process that sends updates in a periodically way. These metrics can be applied in the evaluation of Cyber-Physical Systems (CPS), such as a monitoring system composed of several sensors, a network and a monitor. Most of the works in the literature use an analytical approach, obtaining exact expressions for AoI. This approach, however, is limited by the difficulty encountered in modeling as the CPS under study becomes more complex, motivating the use of other evaluation methods, such as the use of simulations. Thus, this work proposes a simulation tool based on a computational model for the evaluation of AoI in CPS. Such a tool is capable of evaluating any kind of system, also using several package management techniques present in the literature for the optimization of AoI. Finally, the tool also returns a statistical analysis of the simulation accuracy.

References

Bedewy, A. M., Sun, Y., and Shroff, N. B. (2017). Age-optimal information updates in multihop networks. 2017 IEEE International Symposium on Information Theory (ISIT), pages 576–580.

Bedewy, A. M., Sun, Y., and Shroff, N. B. (2019). Minimizing the age of information through queues. IEEE Transactions on Information Theory, 65(8):5215–5232.

Bhattacharyya, S. S. and Wolf, M. C. (2020). Research challenges for heterogeneous cyberphysical system design. Computer, 53(7):71–75.

Inoue, Y., Masuyama, H., Takine, T., and Tanaka, T. (2019). A general formula for the stationary distribution of the age of information and its application to single-server queues. IEEE Transactions on Information Theory, 65(12):8305–8324.

Jordon, D. (2016). Queueing-tool: A network simulator. https://queueing-tool.readthedocs.io/en/latest/index.html.

Kaul, S., Yates, R., and Gruteser, M. (2012). Real-time status: How often should one update? In 2012 Proceedings IEEE INFOCOM, pages 2731–2735.

Moltafet, M., Leinonen, M., and Codreanu, M. (2020). On the age of information in multi-source queueing models. IEEE Transactions on Communications, 68(8):5003–5017.

Pappas, N., Gunnarsson, J., Kratz, L., Kountouris, M., and Angelakis, V. (2015). Age of information of multiple sources with queue management. In 2015 IEEE International Conference on Communications (ICC), pages 5935–5940.

Prandel, P. C. and Barreto, P. S. (2021). Computational modeling of age of information for cyber-physical systems. In 2021 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.

Sun, Y., Uysal-Biyikoglu, E., and Kompella, S. (2018). Age-optimal updates of multiple information flows.

Yates, R. D. (2018). Status updates through networks of parallel servers. In 2018 IEEE International Symposium on Information Theory (ISIT), pages 2281–2285.

Yates, R. D. and Kaul, S. K. (2019). The age of information: Real-time status updating by multiple sources. IEEE Transactions on Information Theory, 65(3):1807–1827.

Yates, R. D., Sun, Y., Brown, D. R., Kaul, S. K., Modiano, E., and Ulukus, S. (2021). Age of information: An introduction and survey. IEEE Journal on Selected Areas in Communications, 39(5):1183–1210.
Published
2022-05-23
PRANDEL, Paulo César; BARRETO, Priscila Solis. Um Simulador para o Cálculo e Otimização da Age of Information (AoI) em Sistemas Ciberfísicos. In: DEMO SESSION - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 40. , 2022, Fortaleza/CE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 89-96. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc_estendido.2022.223491.