Development of Game Theory Strategy for Estimating Mobility Variety in Approaching Wireless Networks

Year : 2021 | Volume : | Issue : 3 | Page : 17-32


  1. Vijayaprabhu A.

  2. Gopalakrishnan S

  1. Research Scholar, Dr. M.G.R. Educational and Research institute, , India
  2. Assistant Professor, , Sri Venkateswara College of engineering and technology, , India
  3. Professor, Siddhartha Institute of Technology and Sciences, , India


Game theory designed with a set of structured tools with an evaluation tool for the tedious interaction among logical players. This theory approaches for analyzing of communication networks, which functions with autonomous structured networks and the designed network devices can take rational decisions according to network congestion. The proposed structure consists of mixture frame for channel allocation for random probability for accessing channel. The main objective of this work is to reduce the cost of wireless access while satisfying the quality-of-service requirements thereby developing a game theoretic model and also to estimate both the stable coalitional structure and the optimal channel access policy from the game model. Then the average cost of optimal and stable coalitional structure is compared with the dominant coalitional structures which achieves the lowest average cost.

Keywords: Social preparedness, Game theory, channel allocation, Quality of Service, Network congestion, Wireless access.

[This article belongs to Recent Trends in Electronics Communication Systems(rtecs)]

How to cite this article: Ancy.M, Vijayaprabhu A., Gopalakrishnan S Development of Game Theory Strategy for Estimating Mobility Variety in Approaching Wireless Networks rtecs 2021; 8:17-32
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1. K. Y. Islam, I. Ahmad, D. Habibi, and A. Waqar, “A survey on energy efficiency in underwater wireless communications,” J. Netw. Comput. Appl., vol. 198, p. 103295, Feb. 2022, doi: 10.1016/J.JNCA.2021.103295.
2. J. Fahad A. Rida, “Development of a remote health care wireless sensor network based on wireless spread spectrum communication networks,” Mater. Today Proc., Mar. 2021, doi: 10.1016/J.MATPR.2021.02.534.
3. M. M. Ahmed, A. Ganguly, A. Vashist, and S. M. Pudukotai Dinakarrao, “AWARe-Wi: A jamming-aware reconfigurable wireless interconnection using adversarial learning for multichip systems,” Sustain. Comput. Informatics Syst., vol. 29, p. 100470, Mar. 2021, doi: 10.1016/j.suscom.2020.100470.
4. A. Ometov et al., “A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges,” Comput. Networks, vol. 193, p. 108074, Jul. 2021, doi: 10.1016/J.COMNET.2021.108074.
5. M. Abbasi, A. Shahraki, M. Jalil Piran, and A. Taherkordi, “Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey,” Eng. Appl. Artif. Intell., vol. 102, p. 104234, Jun. 2021, doi: 10.1016/J.ENGAPPAI.2021.104234.
6. S. K. Memon, N. I. Sarkar, A. Al-Anbuky, and M. A. Hossain, “Preemptive admission control mechanism for strict QoS guarantee to life-saving emergency traffic in wireless LANs,” J. Netw.Comput. Appl., vol. 199, 2022, doi:10.1016/j.jnca.2021.103318.
7. B. Sahin, D. Yazir, A. Soylu, and T. L. Yip, “Improved fuzzy AHP based game-theoretic model for shipyard selection,” Ocean Eng., vol. 233, 2021, doi: 10.1016/j.oceaneng.2021.109060.
8. K. Abid, H. Lakhlef, and A. Bouabdallah, “A survey on recent contention-free MAC protocols for static and mobile wireless decentralized networks in IoT,” Computer Networks, vol. 201. Elsevier,p. 108583, Dec. 24, 2021, doi:10.1016/j.comnet.2021.108583.
9. S. H. Al-Sharaeh, “Dynamic rate-based borrowing scheme for QoS provisioning in high speed multimedia wireless cellular networks,” Appl. Math. Comput., vol. 179, no. 2, pp. 714–724, 2006, doi: 10.1016/j.amc.2005.11.119.
10. P. Ghasemi, F. Goodarzian, J. Muñuzuri, and A. Abraham, “A cooperative game theory approach for location-routing-inventory decisions in humanitarian relief chain incorporating stochastic planning,” Appl. Math. Model., vol. 104, pp. 750–781, Apr. 2022, doi: 10.1016/j.apm.2021.12.023.
11. M. Alizadeh Bidgoli and A. Ahmadian, “Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach,” Energy, vol. 239, p.122036, Jan. 2022, doi:10.1016/J.ENERGY.2021.122036.
12. B. Pourpeighambar, M. Dehghan, and M. Sabaei, “Non-cooperative reinforcement learning based routing in cognitive radio networks,” Comput. Commun., vol. 106, pp. 11–23, Jul. 2017, doi: 10.1016/J.COMCOM.2017.02.013.
13. H. Wang, C. Zhang, K. Li, and X. Ma, “Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage,” Energy, vol. 221, p. 119777, Apr. 2021, doi:10.1016/J.ENERGY.2021.119777.
14. M. Noura and R. Nordin, “A survey on interference management for Device-to-Device (D2D) communication and its challenges in 5G networks,” J. Netw. Comput. Appl., vol. 71, pp. 130–150, Aug. 2016, doi: 10.1016/j.jnca.2016.04.021.
15. F. Basso, L. J. Basso, M. Rönnqvist, and A. Weintraub, “Coalition formation in collaborative production and transportation with competing firms,” Eur. J. Oper. Res., vol. 289, no. 2, pp. 569– 581, Mar. 2021, doi:10.1016/J.EJOR.2020.07.039.
16. W. Tushar et al., “A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid,” Appl. Energy, vol. 243, pp. 10–20, Jun. 2019, doi: 10.1016/J.APENERGY.2019.03.111.
17. J. Delaram, M. Houshamand, F. Ashtiani, and O. Fatahi Valilai, “A utility-based matching mechanism for stable and optimal resource allocation in cloud manufacturing platforms using deferred acceptance algorithm,” J. Manuf. Syst., vol. 60, pp. 569–584, Jul. 2021, doi: 10.1016/J.JMSY.2021.07.012.
18. Y. Ishida and S. Ikeno, “Asymmetry of Strategies in Proposal: Gale-Shapley Algorithm on Diagrams,” Procedia Comput. Sci., vol. 96, pp. 1730–1739, Jan. 2016, doi: 10.1016/J.PROCS.2016.08.221.
19. V. Varagapriya, V. V. Singh, and A. Lisser, “Constrained Markov decision processes with uncertain costs,” Oper. Res. Lett., Feb. 2022, doi: 10.1016/J.ORL.2022.02.001.
20. D. A. Melo Moreira, K. Valdivia Delgado, L. Nunes de Barros, and D. Deratani Mauá, “Efficient algorithms for Risk-Sensitive Markov Decision Processes with limited budget,” Int. J. Approx. Reason., vol. 139, pp. 143–165, Dec. 2021, doi: 10.1016/J.IJAR.2021.09.003.
21. M. Iftikhar, B. Landfeldt, S. Zeadally, and A. Zomaya, “Service level agreements (SLAs) parameter negotiation between heterogeneous 4G wireless network operators,” Pervasive Mob. Comput., vol. 7, no. 5, pp. 525–544, Oct. 2011, doi: 10.1016/J.PMCJ.2011.02.008.
22. D. Sikeridis, E. E. Tsiropoulou, M. Devetsikiotis, and S. Papavassiliou, “Wireless powered Public Safety IoT: A UAV-assisted adaptive-learning approach towards energy efficiency,” J. Netw. Comput. Appl., vol. 123, pp. 69–79, Dec. 2018, doi: 10.1016/J.JNCA.2018.09.003
23. A. Roy and N. Sarma, “A synchronous duty-cycled reservation based MAC protocol for underwater wireless sensor networks,” Digit. Commun. Networks, vol. 7, no. 3, pp. 385–398, Aug. 2021, doi: 10.1016/J.DCAN.2020.09.002.
24. D. Pianini, R. Casadei, M. Viroli, and A. Natali, “Partitioned integration and coordination via the self-organising coordination regions pattern,” Futur. Gener. Comput. Syst., vol. 114, pp. 44–68, Jan. 2021, doi:10.1016/J.FUTURE.2020.07.032.

Regular Issue Open Access Article
Volume 8
Issue 3
Received January 23, 2022
Accepted February 15, 2022
Published February 21, 2021