Abstract
It is known that the energy of wireless sensor network nodes is very limited. In order to solve this problem, considering all the nodes in the cluster, we propose an optimal model of energy consumption for nodes cooperation based on game theory. The model establishes the payoff function of nodes’ residual energy with the main energy consumption factor of the nodes, energy consumption of communication, as the argument. The nodes’ coalitions are constructed through the combination of exhaustion and the sub-regions division. The Nash Equilibrium of cooperative game is solved by using the nodes income distribution method based on Shapley value. With the proposed method, the maximum residual energy of the nodes in the cluster is obtained. The experimental results show that, compared with the game model of non-cooperative, the optimal game model of energy consumption for nodes cooperation has obvious advantages in energy-saving efficiency. As the number of nodes increases, the energy-saving efficiency increases from 12.870 to 38.796%. This paper also verifies that the optimal game model of energy consumption for nodes cooperation has better stability.






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Abbasi M, Fisal N (2015) Noncooperative game-based energy welfare topology control for wireless sensor networks. IEEE Sens J 15(4):2344–2355
Abd MA, Singh BK, Al Rubeaai SF, Tepe KE, Benlamri R (2014) Game theoretic energy balanced (GTEB) routing protocol for wireless sensor networks. In: Wireless communications and networking conference (WCNC), 2014 IEEE. IEEE, pp 2564–2569
Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54:2688–2710
Alhmiedat T (2015) A survey on environmental monitoring systems using wireless sensor networks. J Netw 10(11):606–615
AlSkaif T, Zapata MG, Bellalta B (2015) Game theory for energy efficiency in wireless sensor networks: latest trends. J Netw Comput Appl 54:33–61
Behzadan A, Anpalagan A, Ma B (2011) Prolonging network lifetime via nodal energy balancing in heterogeneous wireless sensor networks. In: Communications (ICC), 2011 IEEE international conference on. IEEE, pp 1–5
Cubitt R (1991) The shapley value: essays in honor of Lloyd S. Shapley by Alvin E. Roth. Econ J 101(406):644–646. https://doi.org/10.2307/2233574
Deng X, Xu C, Liu Y (2009) Energy balanced scheme based on variable cell transmission range for wireless sensor networks. In: Wireless communications, networking and mobile computing, 2009. WiCom ‘09. 5th international conference on. IEEE, pp 1–5
Dong M, Ota K, Liu A (2016) RMER: reliable and energy-efficient data collection for large-scale wireless sensor networks. IEEE Internet Things J 3(4):511–519
Đurišić MP, Tafa Z, Dimić G, Milutinović V (2012) A survey of military applications of wireless sensor networks. In: Embedded computing (MECO), 2012 Mediterranean conference on. IEEE, pp 196–199
Heinzelman WR, 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 1–10
Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670
Iyer R, Kleinrock L (2003) QoS control for sensor networks. In: Communications, 2003. ICC ‘03. IEEE international conference on. IEEE, pp 517–521
Kazemeyni F, Johnsen EB, Owe O et al (2011) Group selection by nodes in wireless sensor networks using coalitional game theory. In: Engineering of complex computer systems (ICECCS), 2011 16th IEEE international conference on. IEEE, pp 253–262
Lin XH, Kwok YK, Wang H, Xie N (2015a) A game theoretic approach to balancing energy consumption in heterogeneous wireless sensor networks. Wirel Commun Mobile Comput 15(1):170–191
Lin DY, Wang Q, Lin DQ, Deng Y (2015b) An energy-efficient clustering routing protocol based on evolutionary game theory in wireless sensor networks. Int J Distrib Sens N 11(11):1–12. https://doi.org/10.1155/2015/409503
Liu XX (2015) An optimal-distance-based transmission strategy for lifetime maximization of wireless sensor networks. IEEE Sens J 15(6):3484–3491
Magno M, Polonelli T, Benini L, Popovici E (2015) A low cost, highly scalable wireless sensor network solution to achieve smart LED light control for green buildings. IEEE Sens J 15(5):2963–2973
Manshaei MH, Zhu Q, Alpcan T, Basar T, Hubaux J-P (2013) Game theory meets network security and privacy. ACM Comput Surv 45(3):1–39. https://doi.org/10.1145/2480741.2480742
Ojha T, Misra S, Raghuwanshi NS (2015) Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges. Comput Electron Agric 118:66–84
Sengupta S, Chatterjee M, Kwiat K (2010) A game theoretic framework for power control in wireless sensor networks. IEEE Trans Comput 59(2):231–242
Sheng Z, Mahapatra C, Zhu C, Leung Victor CM (2015) Recent advances in industrial wireless sensor networks towards efficient management in IoT. IEEE Access 3:622–637
Shi HY, Wang WL, Kwok NM, Chen SY (2012) Game theory for wireless sensor networks: a survey. Sensors 12(7):9055–9097
Vinoba VD, Sridevi S (2017) Enhancing the lifetime in wireless sensor networks using non-zero sum cooperative and non-cooperative repeated game theory. Int J Sci Res Comput Sci Eng Inf Technol (IJSRCSEIT) 2(4):679–687
Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior (commemorative edition). Princeton university press, Princeton
Wang Y, Luo S, Gao J (2017) Uncertain extensive game with application to resource allocation of national security. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0538-9
Xu R, Wang Y, Wang W, Ding Y (2018) Evolutionary game analysis for third-party governance of environmental pollution. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-1034-6
Zhu H, Poor HV (2009) Coalition games with cooperative transmission: a cure for the curse of boundary nodes in selfish packet-forwarding wireless networks. IEEE Trans Commun 57(1):203–213
Acknowledgements
National Natural Science Foundation of China; Award Number: 61379100; Recipient: Hongsheng Yin, National Natural Science Foundation of China; Award Number: 51574232; Recipient: Gang Hua, National Natural Science Foundation of China; Award Number: 61472388; Recipient: Honggang Qi.
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Zhang, J., Yin, J., Xu, T. et al. The optimal game model of energy consumption for nodes cooperation in WSN. J Ambient Intell Human Comput 11, 589–599 (2020). https://doi.org/10.1007/s12652-018-1128-1
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DOI: https://doi.org/10.1007/s12652-018-1128-1