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International Journal of Antennas and Propagation
Volume 2017, Article ID 8591206, 13 pages
Research Article

Study on Selfish Node Incentive Mechanism with a Forward Game Node in Wireless Sensor Networks

1School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2School of Computing and Digital Technologies, Staffordshire University, Stoke-on-Trent ST4 2DE, UK

Correspondence should be addressed to Yun Liu; nc.ude.utjb@nuyuil

Received 4 February 2017; Revised 1 August 2017; Accepted 16 August 2017; Published 8 October 2017

Academic Editor: Francisco Falcone

Copyright © 2017 Mohammed Ahmed Ahmed Al-Jaoufi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In a wireless sensor network, some nodes may act selfishly and noncooperatively, such as not forwarding packets, in response to their own limited resources. If most of the nodes in a network exhibit this selfish behavior, the entire network will be paralyzed, and it will not be able to provide normal service. This paper considers implementing the idea of evolutionary game theory into the nodes of wireless sensor networks to effectively improve the reliability and stability of the networks. We present a new model for the selfish node incentive mechanism with a forward game node for wireless sensor networks, and we discuss applications of the replicator dynamics mechanism to analyze evolutionary trends of trust relationships among nodes. We analyzed our approach theoretically and conducted simulations based on the idea of evolutionary game theory. The results of the simulation indicated that a wireless sensor network that uses the incentive mechanism can forward packets well while resisting any slight variations. Thus, the stability and reliability of wireless sensor networks are improved. We conducted numerical experiments, and the results verified our conclusions based on the theoretical analysis.