Document Type: Research Paper


1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

3 Computer Engineering and Information Technology Department, Amirkabir University of Technology, Tehran, Iran


Network throughput and energy conservation are two conflicting important performance metrics for wireless sensor networks. Since these two objectives are in conflict with each other, it is difficult to achieve them simultaneously. In this paper, a joint duty cycle scheduling and energy aware routing approach is proposed based on evolutionary game theory which is called DREG. Making a trade-off  between energy conservation and network throughput, the proposed  approach prolongs the network lifetime. The paper is divided into the following sections: Initially, the discussion is presented on how the sensor nodes can be scheduled to sleep or wake up in order to reduce energy consumption in idle listening. The sensor wakeup/sleep scheduling problem with multiple objectives is formulated as an evolutionary game theory. Then, the evolutionary game theory is applied to find an optimal wakeup/sleep scheduling policy, based on a trade-off between network throughput and energy efficiency for each sensor. The evolutionary equilibrium is proposed as a solution for this game. In addition, a routing approach is adopted to propose an energy aware fuzzy logic in order to prolong the network lifetime. The results show that the proposed routing approach balances energy consumption among the sensor nodes in the network, avoiding rapid energy depletion of sensors that have less energy. The proposed simulation study shows the more efficient performance of the proposed system than other methods in term of network lifetime and throughput.


[1] A. Abrardo, L. Balucanti and A. Mecocci, A game theory distributed approach for energy
optimization in WSNs, ACM Trans SensNetw, 9(4) (2013), 44.
[2] I. F. Akyildiz and W. Su, Y. Sankarasubramaniam and E. Cayirci, Wireless sensor networks:
a survey, Elsevier Computer Networks, 38 (2002b), 393-422.
[3] T. AlSkaif, M. G. Zapata and B. Bellalta, Game theory for energy eciency in Wireless
Sensor Networks: Latest trends, Journal of Network and Computer Applications, 54 (2015),
[4] S. Arafat, A. AziziMohd, N. CheeKyun, N. Nor Kamariah, S. Aduwati and Y. MohdHanif,
Review of energy conservation using duty cycling schemes for IEEE 802.15.4 wireless sensor
networks, Wireless Personal Communications, Springer, 77 (2014), 589-604.
[5] T. Arampatzis, J. Lygeros and S. Manesis, A survey of applications of wireless sensors
and wireless sensor networks, In 13th Mediterrean Conference on Control and Automation.
Limassol, Cyprus, (2005), 719-724.
[6] M. Ayers and L. Yao, Gureen Game, An energy-ecient QoS control scheme for wireless
sensor networks In Proceedings of 2011 International Green Computing Conference, Orlando,
FL, USA, (2011), 25-28.
[7] A. Behzadan and A. Anpalagan, Prolonging network life time via nodal energy balancing in
heterogeneous wireless sensor networks, In: 2011 IEEE international conference on commu-
nications, Kyoto, Japan (2011), 1-5.
[8] M. Buettner, G. V. Yee, E. Anderson and R. Han, X-MAC: a short preamble MAC protocol
for duty-cycled wireless sensor networks, In Proc. of the 4th International Conference on
Embedded Networked Sensor Systems , (2006), 307-320.
[9] S. S. Chiang and C. H. Huang, A minimum hop routing protocol for home security systems
using wireless sensor networks, IEEE Transactions on Consumer Electronics, 53(4) (2007).
[10] A. M. Colman, Cooperation, psychological game theory, and limitations of rationality in
social interaction, Behavioral and Brain Sciences, 26 (2003), 139-198.

[11] J. C. Dagher, M. W. Marcellin and M. A. Neifeld, A theory for maximizing the lifetime of
sensor networks, IEEE Transaction on Communications, 55(2) (2007), 323-332.
[12] D. Fudenberg and D. K. Levine, The theory of learning in games. cambridge, MIT Press,
Cambridge, MA, 1998.
[13] T. He, J. A. Stankovic, C. Lu and T. Abdelzaher, SPEED: A stateless protocol for real-
time communication in sensor networks, Proceedings of IEEE International Conference on
Distributed Computing Systems, (2005), 46-55.
[14] M. Javidi and L. Aliahmadipour, Application of game theory approaches in routing protocols
for wireless networks, In proceedings of 2011 International Conference on Numerical Analysis
and Applied Mathematics, Halkidiki, Greece, (2011), 19-25.
[15] Z. Jia, M. Chundi and H. Jianbin, Game theoretic energy balance routing in wireless sensor
networks, In Chinese control conference, (2007), 420-424.
[16] R. Kannan and S. S. Iyengar, Game-theoretic models for reliable path-length and energy-
constrained routing with data aggregation in wireless sensor networks, IEEE JSAC,
22(6)(2004), 1141-1150.
[17] K. Lin, T. Xu, M. M. Hassan and A. Alamri An energy-eciency node scheduling game based
on task prediction in WSNs , Mobile NetwAppl, Springer Science and Business Media New
York, 20 (2015), 583-592.
[18] G. Lu, B. Krishnamachari and C. S. Raghavendra, An adaptive energy-ecient and low-
latency MAC for tree-based data gathering in sensor networks, Wirel. Commun. Mob. Com-
put, Published online in Wiley Inter Science., 7 (2007), 863-875.
[19] R. Machado and S. Tekinay, A survey of game theoretic approaches in wireless senso rnet-
works, ComputNetw, 52(16) (2008), 3047-3061.
[20] D. Niyato and E. Hossain, wireless sensor networks with energy harvesting technologies: a
game-theoretic approach to optimal energy management, IEEE Wireless Communications,
[21] D. Niyato and E. Hossain, Dynamics of network selection in heterogeneous wireless networks:
an evolutionary game approach, IEEE Transactions on vehicular technology, 58(4) (2009).
[22] N. A. Pantazis, S. A. Nikolidakis and D. D. Vergados, Energy-ecient routing protocols
in wireless sensor networks: a survey, IEEE Communications Surveys & Tutorials, 15(2)
[23] J. Polastre, J. Hill, and D. Culler, Versatile low power media access for wireless sensor
networks, In The Second ACM Conference on Embedded Networked Sensor Systems, (2004),
[24] O. Powell and A. Jarry, Gradient based routing in wireless sensor networks: a mixed strategy,
CoRR Distributed, Parallel and Cluster Computing, 2005.
[25] R. Rajagopalan and P. K. Varshney, Data aggregation techniques in sensor networks: A
survey, IEEE Commun. Surv. Tutor., 8 (2006).
[26] T. Rault, A. Bouabdallah and Y. Challal, Energy-eciency in wireless sensor networks: a
top-down review approach, ComputNetw, 67 (2014), 104-122.
[27] H. Ren and M. Meng, Game-theoretic modeling of joint topology control and power scheduling
for wireless heterogeneous sensor networks, IEEE Trans. Autom. Sci. Eng., 6 (2009), 610-625.
[28] A. Schillings and K. Yang, VGTR A collaborative, energy and information aware routing
algorithm for wireless sensor networks through the use of game theory, In Proceedings of 3rd
International Geosensor Networks Conference, Oxford, UK, (2009), 13-14.
[29] H. Shpungin and Z. Li Throughput and energy eciency in wireless AdHoc networks with
gaussian channels, IEEE Communications Society, (2010), 289-298.
[30] J. M. Smith, Evolution and the Theory of Games: In situations characterized byconict of
interest, the best strategy to adopt depends on what others are doing, American Scientist,
[31] R. S. Sutton and A. G. Barto, Reinforcement learning: an introduction (adaptive computation
and machine learning), MIT Press, Cambridge, MA, 1998.
[32] D. Tudose, L. Gheorghe and N. T. Apus, Radio transceiver consumption modeling for multi-
hop wireless sensor networks, UPB Scienti c Bulletin, Series C, 75(1) (2013), 17-26.

[33] Y. Wu, Zh. Mao and S. Fahmy, Constructing maximum-lifetime data-gathering forests in
sensor networks, IEEE/ACM Transactions on Networking, 18(5) (2010).
[34] G. Yang and G. Zhang, A power control algorithm based on non-cooperative game for wireless
sensor networks, In Proceedings of 2011 International Conference on Electronic & Mechanical
Engineering and Information Technology, Harbin, China, (2011), 12-14.
[35] KLA. Yau, P. Komisarczuk and P. D. Teal, Reinforcement learning for context awareness
and intelligence in wireless networks: review, new features and open issues, J Netw Comput
Appl., 35(1) (2012), 253-267.
[36] W. Ye, J. Heidemann, and D. Estrin, Medium access control with coordinated, adaptive
sleeping for wireless sensor networks, ACM Transactions on Networking, 12(3) (2004).
[37] L. Zhao, L. Guo, L. Cong and H. Zhang, An energy-ecient MAC protocol for WSNs: game-
theoretic constraint optimization with multiple objectives, WirelSensNetw, (2009), 358-364.
[38] M. Zheng, Game theory used for reliable routing modeling in wireless sensor networks, In
International Conference on Parallel and Distributed Computing, Applications and Technolo-
gies, China, (2010), 280-284.