Bit Error Rate (BER) QoS Attribute in Solving Wireless Pricing Scheme on Single Link Multi Service Network

ABSTRACT


INTRODUCTION
The use of the internet by large segments of the community provides an important role in economic life. In this era of internet usage has reached the wireless internet. Economically, the use of wireless internet is cheaper than using a wired internet. This situation provides a great challenge for ISPs in arranging appropriate financing scheme and can provide maximum benefit ISPs and service users [1]. Many applications are developed by utilizing the wireless network, one of these are wireless sensor network applied in many application in wide area or technology [2] such as military or in agriculture [3], [4].
Problem of internet pricing was introduced by [5] and followed by [6][7][8] in wired network both multi classes and multi service. Then the research continue on focusing the wireless network with the advancement of this era. Problem concerning with the wireless network is not only focus on the pricing schemes but many aspect can be reviewed and discussed. Scheduling and routing are one of the probles occurring in optimizing the wireless network [9] or using heuristics method described in [10].
Recent research is already discussed by [11] that focused on pricing scheme on wireless of multi class QoS network where by applying some classes in single link network with the various QoS change and connection change. Their result can prove that by increasing connection cost and QoS change along with the cost change, can benefit providers by applying bit error rate (BER) QoS attribute. Based on the critical views of advancement of pricing strategy involving wireless network in multi service network, then it is important to have deeply discussion about that matter. So, in this paper, the financing schemes bottled single link formed by with bit error rate QoS attribute and the network model multi service proposed by [12], [13] to fix the basic price ( α) and premium (β) will be resolved by considering the financing model wireless networks optimally solved using LINGO

Modified Models
In the modified model, the model developed by combining with the network model in multi service network by adding parameters, variable decisions and constraints of each model and setting base price (α) and premium quality (β). wireless internet financing schemes on the modified model for QoS attribute BER is divided into four (4)  There are four cases which are the case of α and β as parameters, as the case α and β parameter and variables, case α and β as variables and case variables α and β as parameter.

Modified Model for and Parameter of BER QoS Attribute
Wireless pricing scheme model of modified case of α and β sebagai parameter, then the objective function will be as : Based on Objectivefunction (1) and Equation (2) to Equation (32), the optimal solution for each case based on BER QoS attribute will be solvd by using LINGO 11.0. Based on Table 1, the value will achieve the most optimal results in the first case which is equal to 5.64192 x 108. These results will be obtained by iterating 14 iterations of the infeasibility of 0. Generated Memory Used (GMU) t is 32K and Elapsed Runtime (ER) is 0 seconds.
Based on Table 2 it can be seen that the values of variables for case 1 is very big, for case 2 and 3 is quite big, while in four case 4 the values of variables is 0. In case 1 the value of x is 1, whereas in cases 2 and 3 the value of x adala h 0, in case 4the value of x is 10-7 or close to 0. Value of for case 1 and 2 is different but not much different whereas the value of for cases 3 and 4 approaches 0 and quite different from the value of in case 1 and 2. Values of in case 1 is 2.375273 while in cases 1, 2 and 3, the cases have variable the same values of . Value of aik in case 1 and 3 is the same one, not much different from the case 2 and case 4 in which case 2 and 4 have the values of the same variable.

Modified Model of Parameter and Variable of BER QoS Attribute
The model of wireless pricing for the case of parameter and variable is as follows: Subject to Equation (2) to Equation (32) with added constraints as follows: When we modify the quality index i (I i ) and quality premium ( ) then if , add the cosntraints (39) According to objective (33) and Equation (2) to Equation (32) also Equation (34) to Equation (40) the optimal solution can be completed by applying LINGO 11.0. Based on Table 3, the value achieves the most optimal results in the first case which is equal to 5.64192 x 108. These results will be obtained by iterating 13 times with the infeasibility of 0. The GMU is equal to 34k and ER is 0 seconds. According to Table 4, it can be examined that the value of for case 1 is very big, while in case 2 and 3 are quite big and case 4 is 0. x value of case 1 is 1, as for case 2 and 3 are 0, then for case 4 is 10 -7 or close to 0. value for case 1 and 2 is not much different, but it occurs differently from case 1 and 2 for case 3 and 4 which are tend to 0 Value of in case 1 is 2.375273 where I case 1, 2 and 3, the value of is the same. Value of a ik in case 1 and 3 is same, but not quite much different with the value of case 2 and 4 which have the same variable value.

Modified Model for dan Variable of BER QoS attribute
The objective function will be Subject to Equation (2)  Based on Table 5 the value will achieve the most optimal results in the first case is equal to 5.64192 x 108. These results will be obtained by iterating by 14 iterations of the infeasibility of 0. The GMU is equal to 35K and ER is 0 seconds. According to Table 6  is 0. In case 1, the value x is 1, while for case 2 and 3 the value of x is 0, and for case 4 the value of x is 10 -7 or close 0. The other values of decision variable can be seen completely in Table 5.

Modified Model Modifikasi for Variable and dan Parameter of BER QoS Attribute
The objective function will be as follows.
Then the solution is presented in Table 7 and Table 8. In Table 7, the optimal solution is achieved in case 1 of 5.64192 x 10 8 while in Table 8 Table 9, Table 10 and Table 11 displays the comparison between other QoS attribute such as bandwidth, End to End delay and BER. According to Table 10, the optimal solution .was achieved for case 3 and 4. It can be seen in Table 11, the same value occurs for ech case of BER QoS attribute which is 5,64192 x 10 8 . It can be concluded for each QoS attribute, the optimal solution occurs in case 1 where increasing cost along with QoS change ( ) and increase the value of (x) where the revenue of IDR 564,192,000 is obtained.

CONCLUSION
According to results above, the model for each attribute, we can conclude that ISP obtains maximum revenue by fixing the pricing strategy in multi service according to BER QoS attribute by increasing the cost along QoS change ( ) and the value of QoS (x) eith the profit of IDR 564,192,000.