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Cellular Goore Game and its application to quality-of-service control in wireless sensor networks

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Abstract

The Goore Game (GG) is a model for collective decision-making under uncertainty, which can be used as a tool for stochastic optimization of a discrete variable function. The Goore Game has a fascinating property that can be resolved in an entirely distributed manner with no intercommunication between the players. In this paper, we introduce a new model called Cellular Goore Game (CGG). CGG is a network of Goore Games in which, at any time, every node (or node in a subset of the nodes) in the network plays the role of a referee that participates in a GG with its neighboring players (voters). Like GG, each player independently selects its optimal action between two available actions based on their gains and losses received from its adjacent referees. Players in CGG know nothing about how other players are playing or even how/why they are rewarded/penalized by the voters. CGG may be used for modeling systems that can be described as massive collections of simple objects interacting locally with each other. Through simulations, the behavior of CGG for different networks of players/voters is studied. This paper presents a novel CGG-based approach to efficiently solve the Quality-of-Service (QoS) control for clustered WSNs to show the potential of CGG. Also, a CGG-based QoS control algorithm for WSNs with multiple sinks is proposed that dynamically adjusts the number of active sensors during WSN operation. Several experiments have been conducted to evaluate the performance of these algorithms. The obtained results show that the proposed CGG-based algorithms are superior to the existing algorithms in terms of the QoS control performance metrics.

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References

  1. Tsetlin ML (1973) Automaton theory and modeling of biological systems, vol 102. Academic Press, New York

    MATH  Google Scholar 

  2. Thathachar MAL, Arvind MT (1997) Solution of Goore game using modules of stochastic learning automata. J Indian Inst Sci 77(1):47–61

    MathSciNet  MATH  Google Scholar 

  3. Thathachar MAL, Sastry PS (2002) Varieties of learning automata: an overview. IEEE Trans Syst Man Cybern Part B Cybern 32(6):711–722. https://doi.org/10.1109/TSMCB.2002.1049606

    Article  Google Scholar 

  4. Chen D, Varshney PK (2004) QoS support in wireless sensor networks: a survey. Int Conf Wirel Netw 233:1–7

    Google Scholar 

  5. Narendra K, Thathachar M (2012) Learning automata: an introduction. IEEE Trans Syst Man Cybern B Cybern, vol. 32, no. 6.

  6. Tung B, Kleinrock L (1996) Using finite state automata to produce self-optimization and self-control. IEEE Trans Parallel Distrib Syst 7(4):439–448

    Article  Google Scholar 

  7. Li S, Ge H, Liang Y-C, Zhao F, Li J (2016) Estimator Goore Game based quality of service control with incomplete information for wireless sensor networks. Signal Process 126:77–86

    Article  Google Scholar 

  8. Iyer R and Kleinrock L (2003) “QoS control for sensor networks,” in IEEE International Conference on Communications, 2003. ICC’03, vol 1, pp 517–521

  9. Frolik J (2004) “QoS control for random access wireless sensor networks,” in 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No. 04TH8733), vol 3, pp 1522–1527

  10. Nayer SI and Ali HH (2008) “A dynamic energy-aware algorithm for self-optimizing wireless sensor networks,” in International Workshop on Self-Organizing Systems, pp 262–268

  11. Ayers M and Liang Y (2011) “Gureen Game: An energy-efficient QoS control scheme for wireless sensor networks,” in 2011 International Green Computing Conference and Workshops, pp 1–8

  12. T. Semprebom, A. R. Pinto, C. Montez, and F. Vasques, “Energy consumption and spatial diversity trade-off in autonomic Wireless Sensor Networks: The (m, k)-Gur Game approach,” in 2013 11th IEEE International Conference on Industrial Informatics (INDIN), 2013, pp. 135–140

  13. Elshahed EM, Ramadan RA, Al-Tabbakh SM, El-zahed H (2014) Modified gur game for WSNs QoS control. Proced Comput Sci 32:1168–1173

    Article  Google Scholar 

  14. Semprebom T, Montez C, de Araújo GM, and Portugal P (2015) “Skip game: an autonomic approach for QoS and energy management in IEEE 802.15. 4 WSN,” in 2015 IEEE Symposium on Computers and Communication (ISCC), 14(2), 1–9, 2014.

  15. Oommen BJ, Granmo O-C, Pedersen A (2007) “Using stochastic AI techniques to achieve unbounded resolution in finite player Goore Games and its applications”, In: IEEE Symposium on Computational Intelligence and Games, pp 161–167

  16. Calitoiu D (2009) “New search algorithm for randomly located objects: A non-cooperative agent based approach,” in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp 1–6

  17. Yoon B-J (2011) Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics. BMC Bioinformat 12(1):1–11

    Article  Google Scholar 

  18. Thathachar MAL, Sastry PS (2004) Networks of learning automata : techniques for online stochastic optimization. Springer, Boston

    Book  Google Scholar 

  19. Beigy H and Meybodi MR (2002) “A New Distributed Learning Automata Based Algorithm For Solving Stochastic Shortest Path Problem,” in JCIS, pp 339–343

  20. Anari B, Torkestani JA, Rahmani AM (2017) Automatic data clustering using continuous action-set learning automata and its application in segmentation of images. Appl Soft Comput 51:253–265

    Article  Google Scholar 

  21. Wheeldon A, Shafik R, Rahman T, Lei J, Yakovlev A, Granmo O-C (2020) Learning automata based energy-efficient AI hardware design for IoT applications. Philos Trans R Soc A 378(2182):20190593

    Article  Google Scholar 

  22. Rahmanian AA, Ghobaei-Arani M, Tofighy S (2018) A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Futur Gener Comput Syst 79:54–71

    Article  Google Scholar 

  23. Akbari Torkestani J, Meybodi MR (2010) An intelligent backbone formation algorithm for wireless ad hoc networks based on distributed learning automata. Comput Netw 54(5):826–843. https://doi.org/10.1016/j.comnet.2009.10.007

    Article  MATH  Google Scholar 

  24. Esnaashari M, Meybodi MR (2011) A cellular learning automata-based deployment strategy for mobile wireless sensor networks. J Parallel Distrib Comput 71(7):988–1001. https://doi.org/10.1016/j.jpdc.2010.10.015

    Article  MATH  Google Scholar 

  25. Mostafaei H, Meybodi MR (2013) Maximizing lifetime of target coverage in wireless sensor networks using learning automata. Wirel Pers Commun 71(2):1461–1477. https://doi.org/10.1007/s11277-012-0885-y

    Article  Google Scholar 

  26. Saghiri AM, Meybodi MR (2018) An adaptive super-peer selection algorithm considering peers capacity utilizing asynchronous dynamic cellular learning automata. Appl Intell 48(2):271–299

    Article  Google Scholar 

  27. Rezvanian A, Meybodi MR (2015) Finding minimum vertex covering in stochastic graphs: a learning automata approach. Cybern Syst 46(8):698–727

    Article  MATH  Google Scholar 

  28. Khomami MMD, Rezvanian A, Meybodi MR (2018) A new cellular learning automata-based algorithm for community detection in complex social networks. J Comput Sci 24:413–426

    Article  Google Scholar 

  29. Khomami MMD, Rezvanian A, Bagherpour N, Meybodi MR (2018) Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl Intell 48(3):570–593

    Article  Google Scholar 

  30. Khomami MMD, Rezvanian A, Meybodi MR (2016) Distributed learning automata-based algorithm for community detection in complex networks. Int J Mod Phys B 30(8):1650042

    Article  MathSciNet  Google Scholar 

  31. Rezvanian A, Saghiri AM, Vahidipour SM, Esnaashari M, Meybodi MR (2018) Recent advances in learning automata. Stud Comput Intell 754:1–458. https://doi.org/10.1007/978-3-319-72428-7

    Article  MathSciNet  MATH  Google Scholar 

  32. Norman MF (1968) On the linear model with two absorbing barriers. J Math Psychol 5(2):225–241. https://doi.org/10.1016/0022-2496(68)90073-4

    Article  MathSciNet  MATH  Google Scholar 

  33. Thathachar MAL, Sastry PS (2011) Networks of learning automata: techniques for online stochastic optimization. Springer Science & Business Media, Heidelberg

    Google Scholar 

  34. Oommen BJ, Granmo O-C, and Pedersen A (2006) “Empirical verification of a strategy for unbounded resolution in finite player goore games,” In Australasian Joint Conference on Artificial Intelligence, pp 1252–1258.

  35. Granmo O-C, Oommen BJ, Pedersen A (2012) Achieving unbounded resolution in finite player goore games using stochastic automata, and its applications. Seq Anal 31(2):190–218

    Article  MathSciNet  MATH  Google Scholar 

  36. Granmo O-C, Glimsdal S (2013) Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore game. Appl Intell 38(4):479–488

    Article  Google Scholar 

  37. Yaacoub E, Abu-Dayya A, and Matin MA (2012) “Multihop routing for energy efficiency in wireless sensor networks,” In Wireless sensor networks-technology and protocols, In Tech Press, pp 165–186, Springer, Berlin, Germany.

  38. Shirazi GN, Wang P, Dong X, Eu ZA, and Tham C-K, (2008) “A QoS network architecture for multi-hop, multi-sink target tracking WSNs,” In 2008 11th IEEE Singapore International Conference on Communication Systems, pp 17–21.

  39. Tang S, Li W (2006) QoS supporting and optimal energy allocation for a cluster based wireless sensor network. Comput Commun 29(13–14):2569–2577

    Article  Google Scholar 

  40. Choe HJ, Ghosh P, Das SK (2010) QoS-aware data reporting control in cluster-based wireless sensor networks. Comput Commun 14(2), 1–9, February 2014.

    Google Scholar 

  41. Mazaheri MR, Homayounfar B, Mazinani SM (2012) Qos based and energy aware multi-path hierarchical routing algorithm in wsns. Wirel Sens Netw 4(2):31

    Article  Google Scholar 

  42. Fapojuwo AO, Cano-Tinoco A (2009) Energy consumption and message delay analysis of QoS enhanced base station controlled dynamic clustering protocol for wireless sensor networks. IEEE Trans Wirel Commun 8(10):5366–5374

    Article  Google Scholar 

  43. Singh SK, Kumar P, Singh JP (2017) A survey on successors of LEACH protocol. Ieee Access 5:4298–4328

    Article  Google Scholar 

  44. Nazir B, Hasbullah H (2013) Energy efficient and QoS aware routing protocol for clustered wireless sensor network. Comput Electr Eng 39(8):2425–2441

    Article  Google Scholar 

  45. Diaz JR, Lloret J, Jimenez JM, Rodrigues JJPC (2014) A QoS-based wireless multimedia sensor cluster protocol. Int J Distrib Sens Netw 10(5):480372

    Article  Google Scholar 

  46. Shiva Prakash T, Raja KB, Venugopal KR, Iyengar SS, and Patnaik LM (2014) “Base station controlled adaptive clustering for Qos in wireless sensor networks,” Int J Comput Sci Netw Secur 14(2)

  47. Hammoudeh M, Newman R (2015) Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Inf Fusion 22:3–15

    Article  Google Scholar 

  48. Deepa O, Suguna J (2020) An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks. J King Saud Univ Inf Sci 32(7):763–774

    Google Scholar 

  49. Amjad M, Afzal MK, Umer T, Kim B-S (2017) QoS-aware and heterogeneously clustered routing protocol for wireless sensor networks. IEEE Access 5:10250–10262

    Article  Google Scholar 

  50. Hamidouche R, Aliouat Z, Gueroui AM (2018) Genetic algorithm for improving the lifetime and QoS of wireless sensor networks. Wirel Pers Commun 101(4):2313–2348

    Article  Google Scholar 

  51. Kaur T, Kumar D (2020) A survey on QoS mechanisms in WSN for computational intelligence based routing protocols. Wirel Netw 26(4):2465–2486

    Article  Google Scholar 

  52. Shen H, Bai G, Tang Z, Zhao L (2014) QMOR: QoS-aware multi-sink opportunistic routing for wireless multimedia sensor networks. Wirel Pers Commun 75(2):1307–1330

    Article  Google Scholar 

  53. Kumar S, Verma SK, Kumar A (2015) Enhanced threshold sensitive stable election protocol for heterogeneous wireless sensor network. Wirel Pers Commun 85(4):2643–2656

    Article  Google Scholar 

  54. Verma S, Sood N, Sharma AK (2019) QoS provisioning-based routing protocols using multiple data sink in IoT-based WSN. Mod Phys Lett A 34(29):1950235

    Article  Google Scholar 

  55. Rehan W, Fischer S, Rehan M, Mawad Y, Saleem S (2020) QCM2R: A QoS-aware cross-layered multichannel multisink routing protocol for stream based wireless sensor networks. J Netw Comput Appl 156:102552

    Article  Google Scholar 

  56. Knuth DE (2014) Art of computer programming, volume 2: Seminumerical algorithms. Addison-Wesley Professional

  57. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science (80-) 286(5439):509–512

    Article  MathSciNet  MATH  Google Scholar 

  58. Jain TK, Saini DS, Bhooshan SV (2015) Lifetime optimization of a multiple sink wireless sensor network through energy balancing. J. Sensors 2015:1–6

    Article  Google Scholar 

  59. Vincze Z, Vida R, and Vidacs A, (2007) “Deploying multiple sinks in multi-hop wireless sensor networks,” In IEEE international conference on pervasive services, pp 55–63.

  60. Nsnam, (2011) “Ns-3 a Discrete-Event Network Simulator for Internet Systems,” Ns-3, https://www.nsnam.org/ (Accessed Aug. 30, 2021).

  61. Rodríguez A, Del-Valle-Soto C, Velázquez R (2020) Energy-efficient clustering routing protocol for wireless sensor networks based on yellow saddle goatfish algorithm. Mathematics 8(9):1515

    Article  Google Scholar 

  62. Alazzawi L, Elkateeb A (2008) Performance evaluation of the WSN routing protocols scalability. J Comput Syst Netw Commun 2008:1–9

    Google Scholar 

  63. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

    Reyhaneh Ameri, Mohammad Reza Meybodi & Mohammad Mehdi Daliri Khomami

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  1. Reyhaneh Ameri
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  2. Mohammad Reza Meybodi
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  3. Mohammad Mehdi Daliri Khomami
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Correspondence to Mohammad Reza Meybodi.

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Ameri, R., Meybodi, M.R. & Daliri Khomami, M.M. Cellular Goore Game and its application to quality-of-service control in wireless sensor networks. J Supercomput 78, 15181–15228 (2022). https://doi.org/10.1007/s11227-022-04435-1

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