A Non-cooperative Game Theoretic Approach to Energy-efficient Power Control in Wireless Sensor Networks

Aiming at the limited energy characteristics of wireless sensor networks, we apply game theory to solve the power control problem to reduce energy consumption in wireless sensor networks. In this paper, a distributed power control algorithm based on non-cooperative game theory under incomplete information is proposed, which adopts Signal-to-Interference Noise Ratio (SINR) as utility function. The purpose of power control algorithm for noncooperative game is to achieve the largest utility by optimal power control strategy, thus improve the total network energy efficiency. Moreover, Bayesian Nash equilibrium theorem is introduced to study the existence and uniqueness proof of Nash equilibrium algorithm. Simulation results show that there exist points for each of the cost functions considered, which give the maximum net utility given the strategies taken by all other nodes as fixed. And the proposed algorithm is efficient and can achieve better performance.

[1]  Fredrik Gunnarsson,et al.  Power control in wireless networks: characteristics and fundamentals , 2004 .

[2]  Yashwant Prasad Singh,et al.  Adaptive clustering with transmission power control in wireless sensor networks , 2012, ICWCA.

[3]  Chen He,et al.  A Novel Price-Based Power Control Algorithm in Cognitive Radio Networks , 2013, IEEE Communications Letters.

[4]  Mohammad Hayajneh,et al.  Distributed joint rate and power control game-theoretic algorithms for wireless data , 2004, IEEE Communications Letters.

[5]  Sun Yi,et al.  Power control strategy for wireless sensor networks based on node clustering , 2013, 2013 22nd Wireless and Optical Communication Conference.

[6]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[7]  Jui Teng Wang Admission Control With Distributed Joint Diversity and Power Control for Wireless Networks , 2009, IEEE Transactions on Vehicular Technology.

[8]  Andrea J. Goldsmith,et al.  Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[9]  Gregory M. P. O'Hare,et al.  The Impact of Transmission Power Control in Wireless Sensor Networks , 2013, 2013 IEEE 12th International Symposium on Network Computing and Applications.

[10]  Ananthram Swami,et al.  Power control in cognitive radio networks: how to cross a multi-lane highway , 2008, IEEE Journal on Selected Areas in Communications.

[11]  Ki-Hyung Kim,et al.  Power Sharing and Control in Distributed Generation With Wireless Sensor Networks , 2012, IEEE Transactions on Smart Grid.

[12]  Yan Chen,et al.  On cognitive radio networks with opportunistic power control strategies in fading channels , 2008, IEEE Transactions on Wireless Communications.

[13]  Shamik Sengupta,et al.  A Game Theoretic Framework for Power Control in Wireless Sensor Networks , 2010, IEEE Transactions on Computers.

[14]  Vikram Krishnamurthy,et al.  Decentralized adaptation in sensor networks: Analysis and application of regret-based algorithms , 2007, 2007 46th IEEE Conference on Decision and Control.

[15]  Stephen B. Wicker,et al.  Game theory in communications: motivation, explanation, and application to power control , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[16]  John W. Byers,et al.  Utility-based decision-making in wireless sensor networks , 2000, MobiHoc.

[17]  John W. Byers,et al.  Utility-based decision-making in wireless sensor networks , 2000, 2000 First Annual Workshop on Mobile and Ad Hoc Networking and Computing. MobiHOC (Cat. No.00EX444).

[18]  Sudharman K. Jayaweera,et al.  Dynamic spectrum leasing in cognitive radio networks via primary-secondary user power control games , 2009, IEEE Transactions on Wireless Communications.

[19]  Gianluigi Ferrari,et al.  Optimal Transmit Power in Wireless Sensor Networks , 2006, IEEE Transactions on Mobile Computing.

[20]  Daryoush Habibi,et al.  Transmission power control in multihop wireless sensor networks , 2011, 2011 Third International Conference on Ubiquitous and Future Networks (ICUFN).

[21]  A. Uhl,et al.  Foreword and Editorial International Journal of Future Generation Communication and Networking , .

[22]  Andrea J. Goldsmith,et al.  Energy-constrained modulation optimization , 2005, IEEE Transactions on Wireless Communications.

[23]  Dongfeng Yuan,et al.  Distributed Geometric-Programming-Based Power Control in Cellular Cognitive Radio Networks , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[24]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.