Modelagem Computacional para Avaliação da Age of Information (AoI) em Sistemas Ciberfísicos
Abstract
Age of information (AoI) is a concept that defines a metric related to the degree of update that a monitor has in relation to one or more information sources, which send sequential update packets. The present study proposes the use of AoI to evaluate and optimize a Cyber Physical System characterized as a monitoring and decision-making system in the context of military command and control (C2) activity. In this type of system, agents periodically send their georeferenced positions to an operations center and common scenarios may have different degrees of criticality. To achieve this goal, this work developed a computational model to calculate the AoI, which was used as a basis for the implementation of a simulation tool. In addition to the aforementioned tool, this work contributed to the improvement of current package management techniques for the optimization of AoI, by proposing two new techniques: Last Generated - First Served - Conditional (LGFS-C ), for single-source, single-server models, and Max Age First - Last Generated - First Served - Multiple preemption in Waiting (MAF-LGFS-MW), for multiple-source, multiple-server models. Both techniques, when evaluated, showed results equal or better than current state of the art techniques. As a last contribution, this research applies an evaluation methodology, using the developed tool in different usage scenarios.
References
Bedewy, A., Sun, Y., and Shroff, N. (2019a). The age of information in multihop networks. IEEE/ACM Transactions on Networking, 27:1248–1257.
Bedewy, A. M., Sun, Y., and Shroff, N. B. (2017). Optimizing data freshness, throughput, and delay in multi-server information-update systems.
Bedewy, A. M., Sun, Y., and Shroff, N. B. (2019b). 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.
Brasil (2015). Comando e controle Manual de campanha.
Costa, M., Codreanu, M., and Ephremides, A. (2016). On the age of information in status update systems with packet management. IEEE Transactions on Information Theory, 62(4):1897–1910.
Kaul, S., Yates, R., and Gruteser, M. (2012). Real-time status: How often should one update? In 2012 Proceedings IEEE INFOCOM, pages 2731–2735.
Kaul, S. K. and Yates, R. D. (2018). Age of information: Updates with priority. In 2018 IEEE International Symposium on Information Theory (ISIT), pages 2644–2648.
Kosta, A., Pappas, N., Ephremides, A., and Angelakis, V. (2019). Age of information performance of multiaccess strategies with packet management. Journal of Communications and Networks, 21(3):244–255.
Prandel, P. (2022). Uma ferramenta de simulação para a age of information. https://github.com/pprandel/aoi-simulator.
Prandel, P. and Barreto, P. (2022a). Preempção condicional de pacotes baseada na vida média residual para otimização da age of information em sistemas ciberfísicos. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 461–474, Porto Alegre, RS, Brasil. SBC.
Prandel, P. and Barreto, P. (2022b). Um simulador para o cálculo e otimização da age of information (aoi) em sistemas ciberfísicos. In Anais Estendidos do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 89–96, Porto Alegre, RS, Brasil. SBC.
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., 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.
