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Strategic Network Formation Game for Energy Consumption Balancing

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Abstract

Creation of hot-spots is unavoidable, in multihop wireless sensor networks using the least power (or shortest path) routing method. This happens due to the irregularity of underlying network structures. Since hot-spots lose their energy faster than other nodes, they might create network partitions. In this paper, first, we study constructing of energy balanced topologies in a multihop sensor network using only structural information of a network with any-to-any traffic pattern. We consider both forwarding load and transmission power in energy consumption of sensors. Then, we present a strategic network formation game model. We use pairwise stability concept instead of traditional Nash stability and discuss about its advantages over Nash model in our game. After analyzing the game properties, two global and local algorithms for constructing balanced networks are introduced. Our evaluations on sparse and dense uniform networks show that our local algorithm when nodes use their limited neighborhood information, effectively reduces energy consumption imbalance and maximum power consumption while keeping the total power consumption in an acceptable level.

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Notes

  1. We will discuss about the complexity of computing the best response in Sect. 4.3.

References

  1. Álvarez, S. G., Hurajová, J., & Madaras, T. (2009). Notes on betweenness centrality of a graph. Math Slovaca, 62, 1–18.

    Google Scholar 

  2. Azad, A., & Kamruzzaman, J. (2011). Energy-balanced transmission policies for wireless sensor networks. Mobile Computing, IEEE Transactions on, 10(7), 927–940.

    Article  Google Scholar 

  3. Aziz, A. A., Sekercioglu, Y. A., Fitzpatrick, P., & Ivanovich, M. (2013). A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks. Communications Surveys & Tutorials, IEEE, 15(1), 121–144.

    Article  Google Scholar 

  4. Balakrishnan, H., & Padmanabhan, V. (2001). How network asymmetry affects TCP. IEEE Communications Magazine, 39(4), 60–67.

    Article  Google Scholar 

  5. van den Berg, E., Fecko, M.A., Samtani, S., Lacatus, C., Patel, M. (2010) .Cognitive topology control based on game theory. In: Military communications conference, 2010-MILCOM 2010, IEEE, pp 1869–1874.

  6. Bloch, F., & Jackson, M. O. (2006). Definitions of equilibrium in network formation games. International Journal of Game Theory, 34(3), 305–318.

    Article  MATH  MathSciNet  Google Scholar 

  7. Borgatti, S. (1998). network measures of social capital. Wire, 21, 27–36.

    Google Scholar 

  8. Borgatti, S. P., & Everett, M. G. (2006). A Graph-theoretic perspective on centrality. Social Networks, 28(4), 466–484.

    Article  Google Scholar 

  9. Bouabdallah, F., Bouabdallah, N. (2009). On balancing energy consumption in wireless sensor networks. EEE Transactions on vehicular Networking pp 1–16.

  10. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25, 163–177.

    Article  MATH  Google Scholar 

  11. Brandes, U., & Erlebach, T. (2005). Network analysis: Methodological foundations. Social Networks pp 1–6.

  12. Calvó-Armengol, A., & lklç, R. (2008). Pairwise-stability and Nash equilibria in network formation. International Journal of Game Theory, 38, 51–79.

    Article  Google Scholar 

  13. Chatterjee, P., Das, N. (2008). A distributed algorithm for load-balanced routing in multihop wireless sensor networks. In: Distributed computing and networking, Springer, pp 332–338.

  14. Chen, Y., Li, Q., Fei, L., Gao, Q. (2012). Mitigating energy holes in wireless sensor networks using cooperative communication. In: Personal indoor and mobile radio communications (PIMRC), 2012 IEEE 23rd international symposium on, IEEE, pp 857–862.

  15. Chiasserini, C., Rao, R. (2003). Cooperation in wireless ad hoc networks. In: IEEE INFOCOM., vol 2, pp 808–817.

  16. Closas, P., Pages-Zamora, A., Fernandez-Rubio, J.A. (2009a). A game theoretical algorithm for joint power and topology control in distributed WSN. In: 2009 IEEE international conference on acoustics, speech and signal processing, pp 2765–2768.

  17. Closas, P., Pages-Zamora, A., Fernandez-Rubio, J.A. (2009b). A game theoretical algorithm for joint power and topology control in distributed WSN. In: IEEE international conference on acoustics, speech and signal processing, pp 2765–2768.

  18. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). Cambridge: MIT Press.

    MATH  Google Scholar 

  19. Deng, J., Han, Y. S., Heinzelman, W. B., & Varshney, P. K. (2005). Balanced-energy sleep scheduling scheme for high-density cluster-based sensor networks. Computer Communications, 28(14), 1631–1642.

    Article  Google Scholar 

  20. Easley, D., & Kleinberg, J. (2012). Networks, crowds, and markets: Reasoning about a highly connected world. New York: Cambridge University Press.

  21. Eidenbenz, S., Anil Kumar, V. S., & Zust, S. (2006). Equilibria in topology control games for ad hoc networks. Mobile Networks and Applications, 11(2), 143–159.

    Article  Google Scholar 

  22. Everett, M., & Borgatti, S. P. (2005). Ego network betweenness. Social Networks, 27(1), 31–38.

    Article  Google Scholar 

  23. Felegyhazi, M., & Hubaux, J. (2006). Game theory in wireless networks: A tutorial. ACM Computing Surveys pp 1–15.

  24. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry pp 35–41.

  25. Gao, J., & Zhang, L. (2004). Load balanced short path routing in wireless networks. INFOCOM, 2(March), 1098–1107.

    Google Scholar 

  26. Gao, J., & Zhang, L. (2009). Trade-offs between stretch factor and load-balancing ratio in routing on growth-restricted graphs. IEEE Transactions on Parallel and Distributed Systems, 20(2), 171–179.

    Article  Google Scholar 

  27. Hao, X. C., Zhang, Y. X., Jia, N., & Liu, B. (2012). Virtual game-based energy balanced topology control algorithm for wireless sensor networks. Wireless Personal Communications, 69(4), 1289–1308.

    Article  Google Scholar 

  28. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In: System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on, IEEE, pp 10-pp.

  29. Ishmanov, F., Malik, A. A. S., & Kim, S. W. (2011). Energy consumption balancing (ECB) issues and mechanisms in wireless sensor networks (WSNs): A comprehensive overview. European Transactions on Telecommunications, 22, 151–167.

    Article  Google Scholar 

  30. Jackson, M. O. (2008). Social and economic networks (Vol. 23). Princeton: Princeton University Press.

    MATH  Google Scholar 

  31. Jackson, M. O., & Wolinsky, A. (1996). A strategic model of social and economic networks. Journal of Economic Theory, 71, 44–74.

    Article  MATH  MathSciNet  Google Scholar 

  32. Jarry, A., Leone, P., Nikoletseas, S., & Rolim, J. (2011). Optimal data gathering paths and energy-balance mechanisms in wireless networks. Ad Hoc Networks, 9(6), 1036–1048.

    Article  Google Scholar 

  33. Jia, Z., Chundi, M., & Min, J. (2007). Game theoretic distributed energy control in sensor networks. In: Computer and information technology, 2007. CIT 2007. 7th IEEE international conference on, IEEE, pp 1015–1019.

  34. Komali, R., & MacKenzie, A. (2006). Distributed topology control in ad-hoc networks: A game theoretic perspective. IEEE Consumer Communications and Networking Conference, 1, 563–568.

    Google Scholar 

  35. Komali, R., MacKenzie, A., & Gilles, R. (2008). Effect of selfish node behaviour on efficient topology design. IEEE Transactions on Mobile Computing, 7(9), 1057–1070.

    Article  Google Scholar 

  36. Komali, R., Thomas, R., Dasilva, L., & Mackenzie, A. (2010a). The price of ignorance: distributed topology control in cognitive networks. IEEE Transactions on Wireless Communications, 9(4), 1434–1445.

    Article  Google Scholar 

  37. Komali, R., Thomas, R., Dasilva, L., & Mackenzie, A. (2010b). The price of ignorance: Distributed topology control in cognitive networks. IEEE Transactions on Wireless Communications, 9(4), 1434–1445.

    Article  Google Scholar 

  38. Komali, R.S., Mackenzie, A.B. (2009). Analyzing selfish topology control in multi-radio multi-channel multi-hop wireless networks. Communications, 2009 ICC’09.

  39. Li, N. L. N., Hou, J. C., & Sha, L. (2003). Design and analysis of an MST-based topology control algorithm. IEEE Transactions on Wireless Communications, 3, 1195–1206.

    Google Scholar 

  40. Li, Y., Cheng, X., & Wu, W. (2005). Optimal topology control for balanced energy consumption in wireless networks. Journal of Parallel and Distributed Computing, 65(2), 124–131.

    Article  Google Scholar 

  41. Liang, W., Yu, H., Zeng, P., & Che, C. (2006). BESM: A balancing energy-aware sensor management protocol for wireless sensor network. International Journal of Information Technology, 12(4), 11–19.

    Google Scholar 

  42. Lin, X.H., Wang, H. (2012). On using game theory to balance energy consumption in heterogeneous wireless sensor networks. In: Proceedings of the 2012 IEEE 37th conference on local computer networks (LCN 2012), IEEE Computer Society, pp 568–576.

  43. Lindsey, S., Raghavendra, C.S. (2002). Pegasis: Power-efficient gathering in sensor information systems. In: Aerospace conference proceedings, 2002. IEEE, Vol. 3, pp 3–1125.

  44. Lloyd, E., Liu, R., & Marathe, M. (2005). Algorithmic aspects of topology control problems for ad hoc networks. Mobile Networks and Applications, 10(1–2), 19–34.

    Article  Google Scholar 

  45. Marsden, P. V. (2002). Egocentric and sociocentric measures of network centrality. Social Networks, 24(4), 407–422.

    Article  Google Scholar 

  46. Mei, A., & Stefa, J. (2009). Routing in outer space: Fair traffic load in multihop wireless networks. IEEE Transactions on Computers, 58(6), 839–850.

    Article  MathSciNet  Google Scholar 

  47. Ok, C. S., Lee, S., Mitra, P., & Kumara, S. (2009). Distributed energy balanced routing for wireless sensor networks. Computers & Industrial Engineering, 57(1), 125–135.

    Article  Google Scholar 

  48. Olariu, S. (2006). Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting. IEEE INFOCOM.

  49. Pan, J., Hou, Y.T., Cai, L., Shi, Y., & Shen, S. X. (2003). Topology control for wireless sensor networks. In: Proceedings of the 9th annual international conference on mobile computing and networking, ACM, pp 286–299.

  50. Pathak, P. H., & Dutta, R. (2012). Centrality-based power control for hot-spot mitigation in multi-hop wireless networks. Computer Communications, 35(9), 1074–1085.

    Article  Google Scholar 

  51. Pfeffer, J., Carley, K.M. (2012). k-Centralities. In: Proceedings of the 21st international conference companion on World Wide Web, ACM Press, New York, p 1043.

  52. Popa, L., Rostamizadeh, A., Karp, R., Papadimitriou, C., & Stoica, I. (2007). Balancing traffic load in wireless networks with curveball routing. In: Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing—MobiHoc ’07. ACM Press, New York, p 170.

  53. Ramanathan, R., & Rosales-Hain, R. (2000). Topology control of multihop wireless networks using transmit power adjustment. Proceedings IEEE INFOCOM, 2, 404–413.

    Google Scholar 

  54. Ren, Z., Peng, S., Lei, H. J., & Li, J. B. (2013). Game theory-based routing algorithms for wireless multi-hop networks. Advanced Materials Research, 756, 1244–1248.

    Article  Google Scholar 

  55. Santi, P. (2005). Topology control in wireless ad hoc and sensor networks. ACM Computing Surveys, 37(2), 164–194.

    Article  Google Scholar 

  56. Sengupta, S., Chatterjee, M., & Kwiat, K. (2010). A game theoretic framework for power control in wireless sensor networks. IEEE Transactions on Computers, 59(2), 231–242.

    Article  MathSciNet  Google Scholar 

  57. Sha, K., Du, J., & Shi, W. (2006). WEAR: A balanced, fault-tolerant, energy-aware routing protocol in WSNs. International Journal of Sensor Networks, 1(3/4), 156.

    Article  Google Scholar 

  58. Srivastava, V., Neel, J., Mackenzie, A., Menon, R., Dasilva, L., Hicks, J., et al. (2005). Using game theory to analyze wireless ad hoc networks. IEEE Communications Surveys & Tutorials, 7(4), 46–56.

    Article  Google Scholar 

  59. Wasserman, S. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  60. Wu, X., Chen, G., & Das, S. K. (2008). Avoiding energy holes in wireless sensor networks with nonuniform node distribution. Parallel and Distributed Systems, IEEE Transactions on, 19(5), 710–720.

    Article  Google Scholar 

  61. Zarifzadeh, S., & Yazdani, N. (2012). Neighbor selection game in wireless ad hoc networks. Wireless Personal Communications, 70(2), 617–640.

    Article  Google Scholar 

  62. Zarifzadeh, S., Nayyeri, A., & Yazdani, N. (2008). Efficient construction of network topology to conserve energy in wireless ad hoc networks. Computer Communications, 31(1), 160–173.

    Article  Google Scholar 

  63. Zarifzadeh, S., Yazdani, N., & Nayyeri, A. (2012). Energy-efficient topology control in wireless ad hoc networks with selfish nodes. Computer Networks, 56(2), 902–914.

    Article  Google Scholar 

  64. Zeng, Z., Chen, Z., & Liu, A. (2010). Energy-hole avoidance for WSN based on adjust transmission power. Chinese Journal of Computers, 33(1), 12–22.

    Article  Google Scholar 

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Authors and Affiliations

  1. Router Laboratory, ECE Department, University of Tehran, Tehran, Iran

    Hamed Khanmirza & Nasser Yazdani

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  1. Hamed Khanmirza
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Correspondence to Hamed Khanmirza.

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Khanmirza, H., Yazdani, N. Strategic Network Formation Game for Energy Consumption Balancing. Wireless Pers Commun 84, 841–865 (2015). https://doi.org/10.1007/s11277-015-2664-z

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