PROCESOS DE OXIDACION DE SULFUROS

In order to reflect user valuation about the data information in the server, a simple valuation function is proposed:

(5-1)

where is the importance of the data information offered by the source node; which is assumed to be known to all destinations and its set by each destination randomly according to the need of each individual node. is the normalized channel capacity, which can be expressed as the tightest upper bond on the amount of information that can be reliably transmitted over a communication channel, and is user valuation to the data offered by the source about his strategic situation defined in percentage. Shannon Channel capacity is defined as:

(5-2)

where is the channel coefficient between user transmitter and user receiver (i.e. the transmission data rate between the two points over the named channel). is the

channel bandwidth in ; is the amount power that the source need to transmit the data to the next hop, and is the mean channel noise power (The mean noise power in the receiver N is given by ; . Where is the Blotzmann’s Constant

and is the system temperature, which is generally assumed to be ). Then each user valuation can derived from equation (5-2) and expressed as:

(5-3)

The valuation of the data information can be interpreted that user uses the importance of the data (already known to all users) as a ruler to set his bid in the auction. This valuation measures the destination (if he wins the auction) capabilities to bid more for the offered data keeping in mind the capacity of his channel. We can see that when the channel condition is good (according to equation (5-2)), the user will be more willing to increase his bid for the offered data. As a result, a higher bid would be expected from him and vice versa.

It must be mentioned that the auction mechanism is designed in such a way that does not represent the real price that a destination node has to pay during the auction. Simply it is an interpretation of the strategic situation that a node is facing. In fact reflects the relationship between the node valuation and the channel condition. Additionally, since the channel coefficient is a constant random variable with a known distribution to each user, the distribution of the valuation is also known (according to their relationship shown in equation (5-2)), which means that; lies in the interval [ ].

is defined as the bid space in the auction, , which represent the set of possible bids submitted to the source. We can simply assign to zero without loss of generality, as it represent the null bid. Accordingly, is the lowest acceptable bid, and is the highest bid. The bid increment between two adjacent bids is taken to be the same in the typical case. In the event of ties (i.e. two bidders offer the same final price), the object would be allocated randomly to one of the tied bidders.

To find the winner of the first-price sealed-bid auction, a theoretical model is defined based on the work of H.J. Parrsch and J. Robert [1]. The probability of detecting a bid is denoted as , the probability of not participating in the named auction will be denoted as

. Then the vector , which equals to , and it is denoted as probability distribution over , where . Now we introduce the cumulative distribution

function, which is used to find out whether a user will bid with or less, , all of them are collected in the vector , which equals to .

Then, any rational potential bidder with a known valuation of faces a decision problem of maximizing his expected profit from winning the auction; i.e.

(5-4)

The equilibrium probability of winning for a particular bid is denoted as , and these probabilities are collected in , . Using , the elements of the vector can be calculated. It can be easily found that is known to be zero, as if any bidder submitted a null bid to the source, he is not going to win. We can calculate the remaining elements of as it can be directly verifying that the following constitute a symmetric, Bayes- Nash equilibrium of the auction game:

(5-5)

Throughout this chapter, we will use the notation of Bayes-Nash equilibrium as defined in [3], there approach is to transform a game of incomplete information into one of imperfect information, and any buyer who has incomplete information about other buyers’ values is treated as if he were uncertain about their types.

From equation (5-5), it can be seen that the numerator is the probability that the highest bid is exactly equal to , while the denominator is the expected number of user how are going to submit the same bid (i.e. ). For any user in the game, the best response will be to submit a bid which satisfies the following inequality;

The above inequality shows that user profit is weakly beat any other user profit. The above inequality is the discrete analogue to the equilibrium first-order condition for expected-profit maximization in the continuous-variation model [1], which takes the form of the following ordinary differential equation in the strategy function ;

(5-6)

where and are the probability density and cumulative distribution functions of each bidder valuation respectively. We assume that they are common knowledge to bidders along with , the number of bidders in the system. The reserve price is denoted by , (In many instance, sellers reserve the right not to sell the object if the price

determined in the auction is lower than some threshold amount [2-3], say ), and the above differential equation has the following solution;

(5-7)

In order to get to equation (5-7), we simply multiply both sides of equation (5-6) by

. This will lead us to; , which can be easily arranged

to; , and based on the fact that , it

can be easily shown that the solution is straight forward and lead us to equation (5-7). In the case of the first-price sealed-bid auction, the bidder will submit a bid of

in equilibrium and he will pay a proportional price to his bid if he wins. On the other hand, for the second-price sealed-bid auction, a user will submit his valuation truthfully. This is because the price a user has to pay if he wins the auction is not the winning bid but the second highest one. Therefore, there is nothing to drive a user to bid higher or lower than his true valuation to the data offered by the server. In this case,

, shown in equation (5-3), and the payment process is the same as in the first-price auction.