Advertisement

Soft Computing

, Volume 23, Issue 23, pp 12621–12640 | Cite as

A Coral Reefs Optimization algorithm with substrate layer for robust Wi-Fi channel assignment

  • Carlos Camacho-Gómez
  • Ivan Marsa-Maestre
  • Jose Manuel Gimenez-GuzmanEmail author
  • Sancho Salcedo-Sanz
Methodologies and Application

Abstract

In this paper, we tackle a problem of frequency assignment in Wi-Fi networks with a novel evolutionary-type algorithm. In this version of the problem, we consider the interferences originated by the access points, and also by the clients and all the 11 available channels in the 2.4 GHz Wi-Fi frequency band. The proposed evolutionary-type algorithm is the Coral Reefs Optimization approach with substrate layer (CRO-SL). It is a recently proposed algorithm, which simulates the processes which occur in real coral reefs, including the reproduction and fight for the space of living corals. This version of the algorithm includes a layer of “substrates” which allows using different search patterns jointly in the algorithm. This way, the CRO-SL is able to apply search patterns such as harmony search, differential evolution, Gaussian-based mutations and other traditional and novel search procedures, including local search algorithms, within a single population of solutions. We show the good performance of the proposed approach in a real case study of Wi-Fi frequency assignment, in the Polytechnic School building of the Universidad de Alcalá (Spain), where different realistic scenarios of the problem have been simulated and successfully solved with the CRO-SL algorithm.

Keywords

Wi-Fi channel assignment Graph coloring Coral Reefs Optimization algorithm Meta-heuristics 

Notes

Acknowledgements

This work has been partially supported by the project TIN2014-54583-C2-2-R of the Spanish Ministerial Commission of Science and Technology (MICYT), by the Spanish Ministry of Economy and Competitiveness grants TIN2016-80622-P (AEI/FEDER, UE) and TIN2014-61627-EXP and by the Comunidad Autónoma de Madrid, under project number S2013ICE-2933_02.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abusubaih M (2016) Using partially overlapping channels in home 802.11g WLANs. Wirel Pers Commun 88(2):295–303CrossRefGoogle Scholar
  2. Achanta M (2006) Method and apparatus for least congested channel scan for wireless access points. US Patent number: US20060072602 A1Google Scholar
  3. Bazzi A (2011) On uncoordinated multi-user multi-RAT combining. In: Proceedings of the IEEE vehicular technology conference, VTC Fall, pp 1–6Google Scholar
  4. Bermejo E, Chica M, Damas S, Salcedo-Sanz S, Cordón O (2018) Coral Reef Optimization with substrate layers for medical image registration. Swarm Evolut Comput 42:138–159CrossRefGoogle Scholar
  5. Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52MathSciNetzbMATHCrossRefGoogle Scholar
  6. Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evolut Comput 1:1–23CrossRefGoogle Scholar
  7. Chen JK, De Veciana G, Rappaport TS (2007) “Improved measurement-based frequency allocation algorithms for wireless networks. In: Proceedings of the IEEE global telecommunications conference, GLOBECOM’07, pp 4790–4795Google 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. Cortés P, García JM, Onieva L (2008) Viral systems: a new bio-inspired optimisation approach. Comput Oper Res 35(9):2840–2860zbMATHCrossRefGoogle Scholar
  10. 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
  11. 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
  12. Del Ser J, Matinmikko M, Gil-López S, Mustonen M (2012) Centralized and distributed spectrum channel assignment in cognitive wireless networks: a Harmony search approach. Appl Soft Comput 12:921–930CrossRefGoogle Scholar
  13. Dorigo M, Maziezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating ants. IEEE Trans Syst Man Cybern B 26(1):29–41CrossRefGoogle Scholar
  14. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer-Verlag, Natural Computing Series 1st editionGoogle Scholar
  15. Elwekeil M, Alghoniemy M, El-Khamy M, Furukawa H, Muta O (2012). Optimal channel assignment for IEEE 802.11 multi-cell WLANs. In: Proceedings of the 20th IEEE European signal processing conference (EUSIPCO), pp 694–698Google Scholar
  16. Ficco M, Esposito C, Palmieri F, Castiglione A (2018) A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation. Future Gener Comput Syst 78:343–352CrossRefGoogle Scholar
  17. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: Harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  18. Geier J How to define minimum SNR values for signal coverage. http://www.wireless-nets.com/resources/tutorials/define_SNR_values.html
  19. Grassberger P, Procaccia I (1983) Characterization of strange attractors. Phys Rev Lett 50(5):346–349MathSciNetzbMATHCrossRefGoogle Scholar
  20. Green DB, Obaidat AS (2002) An accurate line of sight propagation performance model for ad-hoc 802.11 wireless LAN (WLAN) devices. In: Proceedings of the IEEE international conference on communications, ICC 2002, vol 5, pp 3424–3428Google Scholar
  21. Haidar M, Akl R, Al-Rizzo H, Chan Y (2007) Channel assignment and load distribution in a power-managed WLAN. In: Proceedings of the 15th IEEE international symposium on personal, indoor and mobile radio communications, PIMRC’07, pp 1–5Google Scholar
  22. Jensen TR, Toft B (2011) Graph coloring problems, vol 39. Wiley, HobokenzbMATHGoogle Scholar
  23. Karaboga D, Basturk B (2008) On the performance of the artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697CrossRefGoogle Scholar
  24. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84CrossRefGoogle Scholar
  25. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294CrossRefGoogle Scholar
  26. Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27CrossRefGoogle Scholar
  27. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 4th IEEE international conference on neural networks, pp 1942–1948Google Scholar
  28. Kephart JO (1994) A biologically inspired immune system for computers. In: Proceedings of the artificial life IV: the fourth international workshop on the synthesis and simulation of living systems, MIT Press, pp 130–139Google Scholar
  29. Kirpatrick D, Gerlatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680MathSciNetCrossRefGoogle Scholar
  30. Klein M, Faratin P, Sayama H, Bar-Yam Y (2003) Negotiating complex contracts. Group Decis Negotiat 12(2):111–125zbMATHCrossRefGoogle Scholar
  31. Lang F, Fink A (2015) Learning from the metaheuristics: protocols for automated negotiations. Group Decis Negotiat 24:299–332CrossRefGoogle Scholar
  32. Lee Y, Kim K, Choi Y (2002) Optimization of AP placement and channel assignment in wireless LANs. In: Proceedings of the 27th annual IEEE conference on local computer networks, pp 831–836Google Scholar
  33. Li M, Miao C, Leung C (2015) A Coral Reef Algorithm based on learning automata for the coverage control problem of heterogeneous directional sensor networks. Sensors 15:3061730635Google Scholar
  34. Mahonen P, Riihijarvi J, Petrova M (2004) Automatic channel allocation for small wireless local area networks using graph colouring algorithm approach. In: Proceedings of the 15th IEEE international symposium on personal, indoor and mobile radio communications, PIMRC’04, vol 1, pp 536–539Google Scholar
  35. Marsa-Maestre I, López-Carmona MA, Velasco JR, de la Hoz E (2010) Avoiding the prisoner’s dilemma in auction-based negotiations for highly rugged utility spaces. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems, vol 1, pp 425–432Google Scholar
  36. Medeiros IG, Xavier-Júnior JC, Canuto AM (2015) Applying the Coral Reefs Optimization algorithm to clustering problems. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 1–8Google Scholar
  37. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1:355–366CrossRefGoogle Scholar
  38. Michaloliakos A, Rogalin R, Zhang Y, Psounis K, Caire G (2016) Performance modeling of next-generation WiFi networks. Comput Netw 105:150–165CrossRefGoogle Scholar
  39. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  40. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRefGoogle Scholar
  41. Mishra A, Brik V, Banerjee S, Srinivasan A, Arbaugh WA (2006) A client-driven approach for channel management in wireless LANs. In: Proceedings of the INFOCOM conferenceGoogle Scholar
  42. Müller S, Airaghi S, Marchetto J (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evolut Comput 6(1):16–29CrossRefGoogle Scholar
  43. Ng SWK, Szymanski TH (2012) Interference measurements in an 802.11n wireless mesh network testbed. In: Proceedings of the 25th IEEE Canadian conference on electrical computer engineering (CCECE), pp 1–6Google Scholar
  44. Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60(7):2087–2098zbMATHCrossRefGoogle Scholar
  45. 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):65zbMATHCrossRefGoogle Scholar
  46. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67CrossRefGoogle Scholar
  47. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248zbMATHCrossRefGoogle Scholar
  48. Riggio R, Rasheed T, Testi S, Granelli F, Chlamtac I (2011) Interference and traffic aware channel assignment in WiFi-based wireless mesh networks. Ad Hoc Netw 9:864–875CrossRefGoogle Scholar
  49. Salcedo-Sanz S (2016) Modern meta-heuristics based on nonlinear physics processes: a review of models and design procedures. Phys Rep 655:1–70MathSciNetCrossRefGoogle Scholar
  50. Salcedo-Sanz S (2017) A review on the coral reefs optimization algorithm: new development lines and current applications. Prog Artif Intell 6:1–15CrossRefGoogle Scholar
  51. Salcedo-Sanz S, Del Ser J, Landa-Torres I, Gil-López S, Portilla-Figueras JA (2014a) The Coral Reefs Optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, vol 2014, Article ID: 739768Google Scholar
  52. Salcedo-Sanz S, Gallo-Marazuela D, Pastor-Sánchez A, Carro-Calvo L, Portilla-Figueras A, Prieto L (2014b) Offshore wind farm design with the Coral Reefs Optimization algorithm. Renew Energy 63:109–115CrossRefGoogle Scholar
  53. Salcedo-Sanz S, Pastor-Sánchez A, Prieto L, Blanco-Aguilera A, García-Herrera R (2014c) Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization Extreme learning machine approach. Energy Convers Manag 87:10–18CrossRefGoogle Scholar
  54. Salcedo-Sanz S, Casanova-Mateo C, Pastor-Sánchez A, Sánchez-Girón M (2014d) Daily Global solar radiation prediction based on a hybrid coral reefs optimization—extreme learning machine approach. Solar Energy 105:91–98CrossRefGoogle Scholar
  55. Salcedo-Sanz S, Sánchez-García JE, Portilla-Figueras JA, Jiménez-Fernández S, Ahmadzadeh AM (2014e) A Coral-Reefs Optimization algorithm for the optimal service distribution problem in mobile radio access networks. Trans Emerg Telecommun Technol 25(11):1057–1069CrossRefGoogle Scholar
  56. Salcedo-Sanz S, García-Díaz P, Portilla-Figueras JA, Del Ser J, Gil-López S (2014f) A Coral Reefs Optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion. Appl Soft Comput 24:239–248CrossRefGoogle Scholar
  57. Salcedo-Sanz S, García-Díaz P, Del Ser J, Bilbao MN, Portilla-Figueras JA (2016a) A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteria. Expert Syst Appl 55:388–2402CrossRefGoogle Scholar
  58. Salcedo-Sanz S, Camacho-Gómez C, Molina D, Herrera F (2016b) A Coral Reefs Optimization algorithm with substrate layers and local search for large scale global optimization. IEEE Congress on Evolutionary Computation, VancouverCrossRefGoogle Scholar
  59. Salcedo-Sanz S, Camacho-Gómez C, Mallol-Poyato R, Jiménez-Fernández S, Del Ser J (2016c) A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids. Soft Comput 20(11):4287–4300CrossRefGoogle Scholar
  60. Salcedo-Sanz S, Muñoz-Bulnes J, Vermeij M (2017a) New Coral Reefs-based approaches for the model type selection problem: a novel method to predict a nation’s future energy demand. Int J Bio-inspired Comput 10(3):145–158CrossRefGoogle Scholar
  61. Salcedo-Sanz S, Camacho-Gómez C, Magdaleno A, Pereira E, Lorenzana A (2017b) Structures vibration control via tuned mass dampers using a co-evolution coral reefs optimization algorithm. J Sound Vib 393:62–75CrossRefGoogle Scholar
  62. Salcedo-Sanz S, Deo RC, Cornejo-Bueno L, Camacho-Gómez, Ghimire S (2018a) An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Appl Energy 209:79–94Google Scholar
  63. Salcedo-Sanz S, García-Herrera R, Camacho-Gómez C, Aybar-Ruíz A, Alexandre E (2018b) Wind power field reconstruction from a reduced set of representative measuring points. Appl Energy 228:1111–1121CrossRefGoogle Scholar
  64. Seyedebrahimi M, Bouhafs F, Raschella A, Mackay M, Shi Q (2016) SDN-based channel assignment algorithm for interference management in dense Wi-Fi networks. In: Proceedings of the IEEE European conference on networks and communications (Eu-CNC), pp 128–132Google Scholar
  65. Silva HM, Canuto AM, Medeiros Inácio G, Xavier-Júnior JC (2016) Cluster ensembles optimization using the Coral reefs Optimization Algorithm. Artificial Neural Networks and Machine Learning—ICANN 2016, Lecture Notes in Computer Science, vol 9887, pp 275–282Google Scholar
  66. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRefGoogle Scholar
  67. Storn R, Price K (1997) Differential Evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetzbMATHCrossRefGoogle Scholar
  68. Soua R, Minet P (2015) Multichannel assignment protocols in wireless sensor networks: a comprehensive survey. Pervasive Mobile Comput A 16:2–21CrossRefGoogle Scholar
  69. Thakur R, Kotagi VJ, Ram Murthy CS (2017) Resource allocation and cell selection framework for LTE-unlicensed femtocell networks. Comput Netw 129:273–283CrossRefGoogle Scholar
  70. Vermeij MJ (2005) Substrate composition and adult distribution determine recruitment patterns in a Caribbean brooding coral. Mar Ecol Prog Ser 295:123–133CrossRefGoogle Scholar
  71. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of the World conference on nature & biologically inspired computing, pp 210–214Google Scholar
  72. Yang XS (2010) A new metaheuristic Bat-inspired algorithm. In: Proceedings of the nature inspired cooperative strategies for optimization, studies in computational intelligence, vol 284, Springer, pp 6574Google Scholar
  73. Yang Z, Zhang T, Zhang D (2016) A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training. Cognit Neurodyn 10(1):73–83MathSciNetCrossRefGoogle Scholar
  74. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102CrossRefGoogle Scholar
  75. Wang H, Lu X, Zhang X, Wang Q, Deng Y (2014) A bio-inspired method for the constrained shortest path problem. The Scientific World Journal, vol 2014, art. ID: 271280Google Scholar
  76. Wang J, Shi W, Cui K, Jin F, Li Y (2015) Partially overlapped channel assignment for multi-channel multi-radio wireless mesh networks. EURASIP J Wirel Commun Netw 2015:1–25Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Signal Processing and CommunicationsUniversidad de AlcaláAlcalá de Henares, MadridSpain
  2. 2.Computer Engineering DepartmentUniversidad de AlcaláAlcalá de Henares, MadridSpain

Personalised recommendations