Advertisement

Group Decision and Negotiation

, Volume 28, Issue 1, pp 175–196 | Cite as

Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

  • Ivan Marsa-MaestreEmail author
  • Enrique de la Hoz
  • Jose Manuel Gimenez-Guzman
  • David Orden
  • Mark Klein
Article
  • 29 Downloads

Abstract

At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climbing mediated negotiation and a simulated annealing mediated negotiation. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Moreover, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer. Finally, we show how the different strategic behavior of the participants affects the results.

Notes

Acknowledgements

This work has been supported by the Spanish Ministry of Economy and Competitiveness Grants TIN2016-80622-P (AEI/FEDER, UE), TIN2014-61627-EXP, MTM2017-83750-P.

References

  1. A Bazzi (2011) On uncoordinated multi user multi RAT combining. In: Vehicular technology conference (VTC Fall), 2011 IEEE, pp 1–6.  https://doi.org/10.1109/VETECF.2011.6093056
  2. Aardal KI, Van Hoesel SP, Koster AM, Mannino C, Sassano A (2007) Models and solution techniques for frequency assignment problems. Ann Oper Res 153(1):79–129CrossRefGoogle Scholar
  3. Abusubaih M, Gross J, Wolisz A (2007) An inter-access point coordination protocol for dynamic channel selection in IEEE802. 11 wireless LANs. In: 1st IEEE workshop on autonomic communications and network management 2007 (ACNM 2007)Google Scholar
  4. Baid A, Raychaudhuri D (2015) Understanding channel selection dynamics in dense wi-fi networks. IEEE Commun Mag 53(1):110–117CrossRefGoogle Scholar
  5. Banchs A, Ortin J, Garcia-Saavedra A, Leith DJ, Serrano P (2016) Thwarting selfish behavior in 802.11 WLANS. IEEE/ACM Trans Netw 24(1):492–505CrossRefGoogle Scholar
  6. Bernini R, Bondavalli A, Lollini P, Montecchi L (2016) Combining san and p-graphs for the analysis and optimization of industrial processes. In: Dependable computing conference (EDCC), 2016 12th European, IEEE, pp 197–207Google Scholar
  7. Bodlaender HL, Kloks T, Tan RB, van Leeuwen J (2000) \(\lambda \)-coloring of graphs. In: Annual symposium on theoretical aspects of computer science, Springer, pp 395–406Google Scholar
  8. Chieochan S, Hossain E, Diamond J (2010) Channel assignment schemes for infrastructure-based 802.11 WLANs: a survey. IEEE Commun Surv Tutor 12(1):124–136CrossRefGoogle Scholar
  9. Cisco (2007) Radio resource management under unified wireless networks, cisco system technical noteGoogle Scholar
  10. De Jonge D, Sierra C (2015) NB\(^3\): a multilateral negotiation algorithm for large, non-linear agreement spaces with limited time. Auton Agents Multi-Agent Syst 29(5):896–942CrossRefGoogle Scholar
  11. de la Hoz E, Gimenez-Guzman JM, Marsa-Maestre I, Orden D (2015) Automated negotiation for resource assignment in wireless surveillance sensor networks. Sensors 15(11):29547–29568CrossRefGoogle Scholar
  12. Tragos EZ, Zeadally S, Fragkiadakis AG, Siris VA (2013) Spectrum assignment in cognitive radio networks: a comprehensive survey. IEEE Commun Surv Tutor 15(3):1108–1135.  https://doi.org/10.1109/SURV.2012.121112.00047 CrossRefGoogle Scholar
  13. FAP (2017) Fap web—a website about frequency assignment problems. http://fap.zib.de/
  14. Fatima S, Kraus S, Wooldridge M (2014) Principles of automated negotiation. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  15. Fatima SS, Wooldridge M, Jennings NR (2001) Optimal negotiation strategies for agents with incomplete information. In: International workshop on agent theories, architectures, and languages. Springer, pp 377–392Google Scholar
  16. Fornito A (2016) Graph theoretic analysis of human brain networks. fMRI Techniques and Protocols pp 283–314Google Scholar
  17. Fujita K, Bai Q, Ito T, Zhang M, Ren F, Aydoğan R, Hadfi R (2017) Modern approaches to agent-based complex automated negotiation. Springer, SwitzerlandGoogle Scholar
  18. Geier J (2017) How to: define minimum SNR values for signal coverage. http://www.wireless-nets.com/resources/tutorials/define_SNR_values.html
  19. Ghavidelsyooki M, Awasthi A, Allouche M, Berger J, Mitrovic Minic S (2017) Partitioning of transportation networks under disruption. Int J Model Simul 37:1–9CrossRefGoogle Scholar
  20. Gimenez-Guzman JM, Marsa-Maestre I, Orden D, de la Hoz E, Ito T (2018) On the goodness of using orthogonal channels in WLAN IEEE 802.11 in realistic scenarios. Wireless Communications and Mobile Computing, vol. 2018, 11p.  https://doi.org/10.1155/2018/5742712
  21. Green DB, Obaidat AS (2002) An accurate line of sight propagation performance model for ad-hoc 802.11 wireless LAN (WLAN) devices. In: IEEE international conference on communications, 2002. ICC 2002, vol 5, pp 3424–3428.  https://doi.org/10.1109/ICC.2002.997466
  22. Griggs JR et al (2009) Graph labellings with variable weights, a survey. Discret Appl Math 157(12):2646–2658CrossRefGoogle Scholar
  23. Hattori H, Klein M, Ito T (2007) Using Iterative Narrowing to Enable Multi-party Negotiations with Multiple Interdependent Issues. In: Proceedings of the 6th international joint conference on autonomous agents and multiagent systems, ACM, New York, NY, USA, AAMAS ’07, pp 247:1–247:3.  https://doi.org/10.1145/1329125.1329424
  24. Jansen P, Perez R (2011) Constrained structural design optimization via a parallel augmented Lagrangian particle swarm optimization approach. Comput Struct 89(13–14):1352–1366.  https://doi.org/10.1016/j.compstruc.2011.03.011 CrossRefGoogle Scholar
  25. Jensen TR, Toft B (2011) Graph coloring problems, vol 39. Wiley, HobokenGoogle Scholar
  26. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw.  https://doi.org/10.1093/comnet/cnu016
  27. Klein M, Faratin P, Sayama H, Bar-Yam Y (2003) Negotiating complex contracts. Group Decis Negotiat 12(2):111–125.  https://doi.org/10.1023/A:1023068821218 CrossRefGoogle Scholar
  28. Koschützki D, Lehmann KA, Peeters L, Richter S, Tenfelde-Podehl D, Zlotowski O (2005) Centrality indices. In: Brandes U, Erlebach T (eds) Network analysis: methodological foundations. Springer, Berlin, pp 16–61CrossRefGoogle Scholar
  29. Koutsoupias E, Papadimitriou C (1999) Worst-case equilibria. Stacs, Springer 99:404–413Google Scholar
  30. Kumar S, Dutta K, Sharma G (2016) A detailed survey on selfish node detection techniques for mobile ad hoc networks. In: Fourth international conference on parallel, distributed and grid computing (PDGC), IEEE, pp 122–127Google Scholar
  31. de La Hoz E, Marsa-Maestre I, Gimenez-Guzman JM, Orden D, Klein M (2017) Multi-agent nonlinear negotiation for wi-fi channel assignment. In: Proceedings of the 16th conference on autonomous agents and multiagent systems, international foundation for autonomous agents and multiagent systems, pp 1035–1043Google Scholar
  32. Lang F, Fink A (2015) Learning from the metaheuristics: protocols for automated negotiations. Group Decis Negotiat 24(2):299–332.  https://doi.org/10.1007/s10726-014-9390-x CrossRefGoogle Scholar
  33. Lopez-Carmona MA, Marsa-Maestre I, Klein M, Ito T (2012) Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces. Auton Agents Multi-Agent Syst 24(3):485–535CrossRefGoogle Scholar
  34. Malaguti E, Toth P (2010) A survey on vertex coloring problems. Int Trans Oper Res 17(1):1–34CrossRefGoogle Scholar
  35. Marsa-Maestre I, Lopez-Carmona MA, Velasco JR, Ito T, Klein M, Fujita K (2009) Balancing utility and deal probability for auction-based negotiations in highly nonlinear utility spaces. In: Proceedings of the 21st international jont conference on artifical intelligence IJCAI’09. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 214–219Google Scholar
  36. Marsa-Maestre I, de la Hoz E, Gimenez-Guzman JM, Orden D, Klein M (2016) Nonlinear negotiation approaches for complex-network optimization: a study inspired by wi-fi channel assignment. In: International workshop on conflict resolution in decision making, Springer, pp 51–65Google Scholar
  37. McDiarmid C, Reed B (2000) Channel assignment and weighted coloring. Networks 36(2):114–117CrossRefGoogle Scholar
  38. Mishra A, Banerjee S, Arbaugh W (2005) Weighted coloring based channel assignment for WLANs. ACM SIGMOBILE Mobile Comput Commun Rev 9(3):19–31CrossRefGoogle Scholar
  39. Mishra A, Brik V, Banerjee S, Srinivasan A, Arbaugh WA (2006) A client-driven approach for channel management in wireless LANs. In: INFOCOMGoogle Scholar
  40. Narayanan L (2002) Channel assignment and graph multicoloring. Handbook Wirel Netw Mobile Comput 8:71–94CrossRefGoogle Scholar
  41. Newman M (2010) Networks: an introduction. Oxford University Press, OxfordCrossRefGoogle Scholar
  42. Ng SWK, Szymanski TH (2012) Interference measurements in an 802.11n wireless mesh network testbed. In: 25th IEEE Canadian conference on electrical computer engineering (CCECE), 2012 , pp 1–6.  https://doi.org/10.1109/CCECE.2012.6334846
  43. Orden D, Gimenez-Guzman JM, Marsa-Maestre I, de la Hoz E (2018) Spectrum graph coloring and applications to Wi-Fi channel assignment. Symmetry 10(3):65CrossRefGoogle Scholar
  44. Orden D, Marsa-Maestre I, Gimenez-Guzman JM, de la Hoz E, Alvarez-Suarez A (2018) Spectrum graph coloring to improve Wi-Fi channel assignment in a real-world scenario via edge contraction. Discret Appl Math (in press)Google Scholar
  45. Ren F, Zhang M, Sim KM (2009) Adaptive conceding strategies for automated trading agents in dynamic, open markets. Wirel Healthc 46(3):704–716.  https://doi.org/10.1016/j.dss.2008.11.005 Google Scholar
  46. Rubinstein A (1982) Perfect equilibrium in a bargaining model. Econometrica 50(1):97–109.  https://doi.org/10.2307/1912531 CrossRefGoogle Scholar
  47. Seyedebrahimi M, Bouhafs F, Raschellà A, Mackay M, Shi Q (2016) Sdn-based channel assignment algorithm for interference management in dense wi-fi networks. In: 2016 European conference on networks and communications (EuCNC), pp 128–132Google Scholar
  48. Sharp A (2007) Distance coloring. In: European symposium on algorithms, Springer, pp 510–521Google Scholar
  49. Tuza Z, Gutin G, Plurnmer M, Tucker A, Burke E, Werra D, Kingston J (2003) Colorings and related topics. Handbook of graph theory. Discrete mathematics and its applications. CRC Press, Boca Raton, pp 340–483Google Scholar
  50. Valori L, Giannuzzi GL, Facchini A, Squartini T, Garlaschelli D, Basosi R (2016) A generation-attraction model for renewable energy flows in italy: a complex network approach. Eur Phys J Spec Top 225(10):1913–1927CrossRefGoogle Scholar
  51. Wiener H (1947) Structural determination of paraffin boiling points. J Am Chem Soc 69(1):17–20.  https://doi.org/10.1021/ja01193a005 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Computer Engineering DepartmentUniversity of AlcaláAlcalá de HenaresSpain
  2. 2.Department of Physics and MathematicsUniversity of AlcaláAlcalá de HenaresSpain
  3. 3.Center for Collective Intelligence, MITCambridgeUSA

Personalised recommendations