Application of Multiagent Systems to Simulate User Behavior in Telecommunications Networks
Abstract
The growth of data flow and the change in user behavior, which started to consume more and more bandwidth, has a direct impact on telecommunications operators, which has a demand growth of around 100% per year. Therefore, the increase in the data flow in telecommunications networks, together with the increasing competition among the operators, demand studies on the influence of the collective behavior. This behavior can significantly alter the financial results of the operators and the competition between them. This paper will analyze the behavior of a duopoly that allows the comparison and regulation of existing models in telecommunications systems, through a simulation using multi-agent systems. This paper aims to present the simulation results of a duopoly and validate its results with the analytical model of a sinlgle link studied in the literature. The validation of simulation results with analytical results will allow the expansion of studies on the influence of users on telecommunications systems.
References
Asmin, Bagabo Nantale; Semwanga, Agnes Rwashana; NGOMA, Muhammed. Agent-based modelling of response time to an advert: behaviour of different market segments in mobile telecommunication in Uganda. Information Technologist (The), v. 16, n. 2, p. 71-84, 2019.
Elreedy, Dina et al. A framework for an agent-based dynamic pricing for broadband wireless price rate plans. Journal of Simulation, v. 13, n. 2, p. 96-110, 2019.
Kumar, Anurag; Manjunath, D.; Kuri, Joy. Communication networking: an analytical approach. Elsevier, 2004.
Laffont, Jean-Jacques; Tirole, Jean. Competition in telecommunications. MIT press, 2001.
Ormerod, Paul; Rosewell, Bridget. Validation and verification of agent-based models in the social sciences. Epistemological aspects of computer simulation in the social sciences, p. 130-140, 2009.
Pereira, Marcelo de Carvalho; Dequech, David. A history-friendly model of the internet access market: the case of Brazil. In: The evolution of economic and innovation systems. Springer, Cham, 2015. p. 579-610.
Pindyck, Robert S.; Rubinfeld, Daniel L. Microeconomics (Global ed., The Pearson series in economics). Harlow: Pearson, 2018.
Railsback, Steven F.; Grimm, Volker. Agent-based and individual-based modeling: a practical introduction. Princeton university press, 2019.
Soares, M.A. & Madeira, Edmundo. A multi-agent architecture for autonomic management of virtual networks. 2012 Ieee Network Operations And Management Symposium, [s.l.], p.1183-1186, abr. 2012.
Takahashi, Hiroki, Nariaki Nishino, and Takeshi Takenaka. "Multi-agent Simulation for the Manufacturer's Decision Making in Sharing Markets." Procedia CIRP 67 (2018): 546-551.
Vega, Diego A., et al. "An Adaptive Trust Model for Achieving Emergent Cooperation in Ad Hoc Networks." Current Trends in Semantic Web Technologies: Theory and Practice. Springer, Cham, 2019. 85-100.
Von Zuben, Fernando J. Computação evolutiva: uma abordagem pragmática. Tutorial: Notas de Aula da disciplina IA707, Faculdade de Engenharia Elétrica e de Computação-Universidade Estadual de Campinas, 2000.
Waldman, Helio; Bortoletto, Rodrigo C.; Pavani, Gustavo S. Um framework para dimensionamento de redes Oticas em ambientes competitivos”. Proceedings 28◦ Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2010.
Watson, Joel. Strategy: an introduction to game theory. New York: WW Norton, 2002.
Wilensky, Uri; Rand, William. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press, 2015.
