How can A Game Theory Based Strategy for Reducing Energy Consumption in Cognitive WSN from point of view Power Consumption as a Challenge in CR-WSN
When designing WSN optimization strategies, the fact that WSN nodes are very limited in terms of memory, computational power, or energy consumption is not insignificant. In the past 4 years a new game-theory-based strategy to optimize energy consumption in WSNs has been analyzed.This strategy takes advantage of a new opportunity offered by cognitive wireless sensor networks (CWSNs): the ability to change the transmission and reception channel.Take a look at “Energy-efficient power allocation in cognitive sensor networks: a game theoretic approach,” by B. Chai et al. A game is defined by several characteristics. The resource being modeled, the players, their strategies, and the actions they can take. Thereon, costs associated with each action will be defined, and, by combining this with the odds (suspected or known) of such actions occurring, the payoff matrix and function will be obtained. The resource is the energy available in each node. Players are CWSN nodes 𝑁 = {1, 2, . . . , 𝑛} and the strategies are those relating to the selection of the communication channel. Energy consumption is modeled as theresource forwhich players compete.
I hope it will be helpful,
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Thank you .. Dr. Andrea Piroddi
yes , that's will be helpful , but I have Question (a game theoretic approach) I Think it will be need more computational power depend on process and algorithm used ,,
I appreciate that
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To determine the optimal value of x (the odds of node n), each node n stores the observed number of accepted and sent requests from its neighbor nodes. From this stored data the optimal value of x that maximiazes the payoff can be obtained. For the implementation of this strategy, it could be possible to always run the maximization of the payoff in the background, but in terms of energy conservation and computing capabilities it is more efficient to optimize only when the transmission channel is noisy enough. In this way, the strategy considers that the optimization will be triggered, taking into account other parameters such as the RSSI received in the communication channel, which is related to noise presence. Even though in some cases gtCR increases node processing consumption, for the strategy calculation, this energy cost is offset by the energy consumption savings by avoiding noisy channels.
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