23-03-2015, 03:35 PM
A preventive method for resiratory acidosis in smart grid environment
ABSTRACT:
High data rate applications consumes much energy. Higher energy consumption has a significant impact on the environment due to the emission of CO2.Most of the people are affected due to its enormous emission. Respiratory acidosis,a lung disease which greatly affects the human being occurs due to inhaling of CO2.Green cognitive mobile network with small cells is a technique for reducing CO2 emission and improving the energy efficiency in mobile multimedia communication.The proposed work considers the smart grid environment where both the radio spectrum and smart grid environment is sensed based on which power allocation and interference management is performed.A three stage stackelberg game is used for electricity price decision,power allocation and interference management.
Index terms:
Cognitive radio, Energy efficiency, interference, small cells, smart grid.
Introduction:
Presence of excess of CO2 in the inspired gas causes respirotary acidosis. This causes depression of central respiratory system, inability to ventilate adequately causes asthma, chronic obstructive pulmonary disease.The level of CO2 in the atmosphere must be reduced. Moreover,high energy consumption increases CO2 concentration. Hence,the usage of small cells is an important technique for increasing the energy efficiency of mobile cellular network.Due to their small coverage area ,small cell requires much less transmission power than macrocell. Cognitive radio can be used to collect information on spectrum usage and to try to access the unused frequency bands intelligently. Therefore, to reduce energy consumption and make efficient use of the spectrum, the spectrum in mobile multimedia networks should be managed optimally.
• In the existing methodology, both the radio spectrum and the smart grid environment is sensed. In the proposed method, by sensing the smart grid environment, respiratory acidosis disease is controlled by reducing the emission of co2.
• Real-time pricing (RTP) for demand-side management(DSM) is considered in the smart grid, where multiple retailers provide real-time electricity prices to the heterogeneous mobile network. The macrocell BS (MBS) and SCBSs sense the smart grid environment and adjust the amount of electricity they consume by performing energy efficient resource allocation.
• The electricity cost for each retailer includes the electricity procurement cost and other costs, such as CO2 tax fees if the procured electricity is produced by nonrenewable energy generators. We use a homogeneous Bertrand game
with asymmetric costs to model price decisions made by the electricity retailers.
• To suppress the effect of interference between the MBSs and SCBSs, an interference price is proposed to allow the MBSs to protect their users by charging the SCBSs.
• We formulate the problems of electricity price decision, energy-efficient power allocation, and interference management in cognitive mobile networks as a three-stage Stackelberg game. Stackelberg games have been successfully used in relay selection and power-allocation problems in cooperative communication networks, among other areas. A backward induction method is used to analyze this proposed Stackelberg game since it can capture the sequential dependence of the decisions in the stages of the game. An iterative algorithm is proposed to obtain the Stackelberg equilibrium.
Algorithm of proposed model:
We assume that there are R electricity retailers in the smart grid, one MBS, and K SCBSs in the system. At each time slot, each subchannel can be allocated to one MU, and the K nearby SCBSs can also use the same subchannel as the MU, i.e., each SCBS assigns one most appropriate user to each subchannel during that time slot. We only demonstrate the situation of one subchannel since we mainly focus on how the decisions in the smart grid, the MBS, and SCBSs affect each other. The model can be extended to multiple subchannel situations using various techniques, such as a dual decomposition technique .The system operates as follows. Each electricity retailer in the smart grid offers a real-time electricity price to the MBS and SCBSs. The MBS procures electricity from the appropriate retailer (e.g., the retailer offering the lowest price) and performs energy-efficient power allocation. The MBS also imposes a unified price on the received interference from the SCBSs to the scheduled MU. Each SCBS chooses the electricity retailer with the lowest offered price to procure electricity and updates its power-allocation strategy to maximize its net utility based on the interference price and the electricity price. Because of these conditions, the Stackelberg game model is applicable in this scenario. A Stackelberg game is a strategic game, which consists of leaders and followers competing with each other for certain resources. The leaders act first, taking into consideration the behaviors of the followers, and the latter act alternatively.
The following graph represents the Utility function of the SCBS versus power allocation of the SCBS. Its utility is affected by its energy efficient power-allocation decision and the interference price offered by the MBS. Each SCBS makes its power-allocation decision based on the interference price offered by the MBS and the lowest price among those offered by the electricity retailers in the smart grid. Therefore, there exists an optimal transmit power for the SCBS to maximize its net utility. The utility increases with the increase in the transmit power at the beginning due to the corresponding increase in transmission rate. However, when the transmit power reaches a certain level, the utility starts to decrease since the gain on the transmission rate cannot balance the electricity cost and interference cost. It also represents that a higher interference price leads to a lower utility since
the same level of interference has a higher interference cost.
Each SCBS tries to decrease its interference cost by lowering its transmission power as the interference price offered by the MBS increases. This graph
also represents that, for the same interference price, lower optimal electricity price leads to a higher transmission power, due to the lower electricity cost.
The following graph depicts the Power allocation of the SCBS with the different interference prices offered by the MBS.This graph also predicts how the interference price offered by the MBS affects its utility and how the interference price combined with the optimal price offered by the electricity retailers in the smart grid affects its power-allocation strategy.
The following graph depicts that the utility of the MBS is a concave function of
the interference price offered by the MBS and finally reaches a stable level when the interference price is higher than a certain level. The reason for that is because the SCBSs adjust their transmission power to zero to avoid the expensive interference cost, when the interference price is too high. The shape of this figure can vary with the values of the parameters. However, in all situations, there exists a sole optimal interference price to maximize the MBS’s net utility.
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Conclusion:
In this paper, Cognitive mobile network with small cells using smart grid environment was described. Multiple retailers
provide electricity price to heterogenous network and adjust the amount of electricity they consume by performing energy efficient power
allocation. A three stage Stackelberg game is used for electricity price decision, power allocation and interference management. Using the simulation result,the proposed scheme will significantly reduce the emission of CO2 thereby reducing the occurrence of the disease.