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. more retransmitted packets wasting even more energy. Hitherto energy efficiency has been investigated extensively and various approaches to achieve an energy efficient network includes, scheduling sensor nodes to alternate between energy-conserving modes of operation, competent routing algorithms, clustering, incorporating astuteness and use of spatial localization at every node to lessen transmission of redundant data. Nodes specialization to different roles such as idle, sensing, routing and routing/sensing to maximize the utility of the nodes [3] has been proposed. An approach for optimizing transmit power for an ad-hoc network scenario [4] where all the nodes uses a uniform transmit power, and numerical results of transmit power sufficient to satisfy the network connectivity has been proposed. In recent years there has been a growing interest in applying game theory to study wireless systems [5] , which used game theory to investigate power control and rate control for wireless data. The authors provide motivations for using game theory to study communication systems, and in particular power control [6] . Distributed iterative power control algorithms have been proposed for cellular networks; these algorithms examine to find the power vector for all the nodes that minimizes the total power with good convergence. Decentralized, game theoretic adaptive mechanisms, which can be deployed to manage sensor activities with low coordination overhead has been explained [7] . Game theory is a theory of decision making under conditions of uncertainty and interdependence. We now provide a brief introduction of the basics of game theory with the help of examples modeled on scenarios prevalent in WSNs. Game theory offers models for distributed allocation of resources and thus provides a way of exploring characteristics of wireless sensor networks. Energy harvesting technologies essential for independent sensor networks using a non cooperative game theoretic technique [ 8] is analyzed. Nash equilibrium was projected as the solution of this game to attain the optimal probabilities of sleep and wake up states that were used for energy conservation. The energy efficiency problem in wireless sensor networks as the maximum network lifetime routing problem is looked upon [9] . Here the transmit power levels is adjusted to just reach the anticipated next hop receiver such that the energy consumption rate per unit information transmission can be reduced. transmission success rate: the total number corrected received packets are divided by the total number of transmitted packets. The system is saturated. Simulation parameter settings are as follows:
In REF , Yang et al. proposed a distributed power control that modifies the device transmit powers to satisfy a Bayesian Nash equilibrium where the power consumption is minimal in the network.
A Non-cooperative Game Theoretic Approach to Energy-efficient Power Control in Wireless Sensor Networks
International Journal of Future Generation Communication and Networking
International Journal of Future Generation Communication and Networking
Computer Science