In this previous section, the proposed user centric service-oriented modeling approach has been applied to a smart environment which includes a number of appliance services and 10 staffs in an open office. Since we designed a simulated environment using LED lights instead of control system to instruct appliances in order to demonstrate the system feasibility, the evaluation on service consumers’ subjective opinions and preferences on these composite services presents a difficulty. The adjustments on the appliances, however, were carried out manually according to predefined configurations and questionnaire was used to collect their feedbacks. In addition, we have evaluated the system effectiveness by examining whether the LED lights have behaved consistently according to the user requirements. In other words, composite services should be selected in line with the group consensus. We have varied the values in the preference database, so different composite services are selected in response to the changes. The experimental results given by the previous sections shows composite service 10 having been selected initially and LED lights have been switched on accordingly. The composite service 9 was chosen later, as the users have changed their preferences. This demonstrates the system coherence and SCA can be used to model software and hardware components as services.
The system performance and complexity are important criteria for evaluating the system. The system cannot be scaled up, if it required huge computational resources when the number of services and QoS criteria increases. We have evaluated TOPSIS method performance and complexity, as it is a critical task in the system. We also compare it with other existing approaches to express the distinct features of the proposed approach.
In addition to ev alias pairs, it is important to putational complexity that would determine or even limit their actual real-world applications. Opinion similarity measure is the most computational resource demanding step in the proposed approach. The opinions collected from the users are fuzzy terms. The traditional methods [22] would calculate the maximum and minimum intersection area of two membership functions given by the users. For example, the triangle membership function is composed of four piecewise linear segments.
The first segment function given by the user 1 would check the point of intersection in respect to the four piecewise linear segments from the user 2. After that, the second segment function would check others until all the four segments have been done. Computing similarity measures and constructing an agreement matrix would be done in exponential time .
aluation SAM in terms of discovered investigate the com
( )
nO m
Similarly, the Ordered Weighted Averaging (OWA) aggregation method to generation all pair-wise similarity value is O m n
(
2 2)
. In contrast, the Fuzzy-TOSIS is rather merely to the SAM as it begins with aggregating all users using the graded mean integration representation method. The crisp values can be derived from the graded mean integration representation method. So, the required computation on the crisp number significantly reduces the complexity compared with fuzzy values. It can be complete in linear timeO m n( ⋅ ).According to the complexity of computing users' evaluation, we also used the TOPSIS method to eliminate the problem associated with the duplicated calculation on weightings by introducing the Minkowski distance function. This can increase the accuracy in measurement. Moreover, our approach can significantly reduce computation complexity in similarity measure, so the proposed approach can be
sca
nce, the approach is suitable for on-line applications which often involve larg
led up. Figure 34 (left) shows the system performance of our proposed TOPSIS method against Huang’s work-SAM with 2 criteria. Figure 34 (right) shows the system performance with 3 criteria. The computational time of SAM would increase exponentially as the number of user opinions and criteria grow. On contrast, the computational time of TOPSIS method only increases slightly when the number of criteria rises to 16. The computational time does not exceed 10 seconds (see Figure 35). Overall, the TOPSIS is very efficient in the cases where the large number of users involves. He
e amount of data.
Figure 34 Performance analysis under difference users
Figure 35 Performance Analysis of different criteria
From the above case study, we can get the ranking order of the three alternative web services isA ,10 A ,9 A ,8 A ,…, and6 A . The resulting order of these preferences is 4 derived from 10 users. While the number of users increases significantly, the proposed approach still outperforms Huang's work [21].
We setup the system within an open office in a research lab. We designed an experiment in which 10 users had to rank the alternatives and choose them according to the predefined criteria. Three different types of services such as air-condition, dehumidifiers and lighting are included for service provision, but with 10 different combinations (10 composited services). Before the system is introduced to the users, the average satisfaction rate from 10 users was just under 43.75%. After the system reasoned over their opinions and preferences, a new service composition was selected to reflect most users’ requirements by changing office appliance configuration. As a result, the average satisfaction rate increases to 68.75%. This evidences usability of the system.
bers of criteria and users, so we can conclude that the method is very efficient when the number of users is under 320 and the number of criteria does not exceed 16 (see Figure 35). The number of services that can be supported or executed depends on the capacity of web container. We deploy all the services to one server in this experiment, as we only 10 composite services. In this case, we have run the tests on an Apache Axis2 server with various numbers of services. The total volume of data relating to users’ preferences and opinions which has been used for reasoning is around 40K. The results in Table 13 show that the system is scalable. When the number of services increases two folds, anaging concurrent processes with great efficiency. The total execution time of the whole platform is around 6000ms in the above experiment without taking into account of
The performance of TOPSIS is analyzed by varying num
it did not require twice execution time. This is due to Apache Axis2 server m
tim
Execution Time
2 services 5187 ms
e for manual operations on appliances.
Table 13 Service execution time on axis2 Number of services
4 services 5357 ms
10 services 5398 ms
16 services 5448 ms
Taking into account the fact that the experiment was carried out under two conditions. After the new setting of the air-conditioner, lighting, and dehumidifiers, the satisfaction rate from users is increased. Overall, the result of users' satisfaction have been very positive. The results reflected in Figure 36 indicate that the satisfaction rate for the regulation setting is lower. A more detailed understanding of the usability can be gained from the "after-rate" .
Figure 36 Usability analysis of satisfaction rate
We observed the whole data volume increment in different period. The collected data are composed of users' opinion and commend. Furthermore, the sensor services e data volume increment will increase with time.
We estimated the whole data volume increment in dif
data are composed of 32 users' opinion and commend. The data volume increment
will increase urthermore, the sensor se rom
environment.
can collect the vary from environment. Th
ferent period. The collected
with time. F rvices can collect the vary f
Figure 37 Data volume increment