# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 3 Improved Eagle Strategy
### Classification High Fitness and Low Fitness Value  
Apart from that, the non-functional attribute, which is the QoS attribute of the service, is focused on selecting the available services and ensuring a high response time for the services. The IES additionally chooses the services with a high fitness value during the service selection process. The low-fitness value services are least prioritized as they have lower response times to achieve high performance during the service invocation. The fitness value is determined by the QoS attributes of services, followed by the threshold value calculation. Once the threshold value is determined, the services that have a higher fitness value compared to the threshold value are determined to have a high fitness value.  
On the other hand, the services with lesser value than the threshold value are determined to be low-fitness value services. The threshold value is determined by calculating the average value of services. The equation below shows the formula to calculate the fitness value for each service, where \(\beta\) is the fitness value, \(\lambda\) is the QoS of the attribute value, and a, b, and c are the weightage based on percentage. Weightage is the value set by the user based on their requirement.  
\[\begin{array}{l}\text{Rule 1: Total Weightage}=(a+b+c+\ldots=n)\\ \text{Rule 2: Total Weightage}=100\\ \beta=\left(\frac{\lambda\lambda}{\lambda_{\text{max}}}\right)(\textbf{a} \%)+\left(\frac{\lambda\lambda}{\lambda_{\text{max}}}\right)(\textbf{b}\% )+\left(\frac{\lambda\lambda}{\lambda_{\text{max}}^{3}}\right)(\textbf{c}\% )\ldots+\left(\frac{\lambda\textbf{n}}{\lambda\textbf{n}_{\text{max}}} \right)(\textbf{n}\%)\\ \beta=\text{Fitness Value of service}\\ \lambda=\text{QoS attribute value}\\ a,b,c=\text{Weightage based on the percentage}\end{array} \tag{9}\]  
Equation (11) below shows the formula to calculate the threshold value of the available services, where \(\tau\) represents the threshold value and \(\beta\) represents the fitness value.  
\[\tau=\frac{\sum\beta}{\textbf{Total number of services}} \tag{10}\]  
Figure 6: Process of a service request by the user.  
\(If\), \(\beta\geq\tau=HighFitnessVal\)  
\(\mathit{Else}\), \(\beta<\tau=\mathit{LowFitnessVal}\)  
The overall structure of the IES, as shown in Figure 7, demonstrates how the services are grouped and classified according to the names of the methods and their respective fitness values. In addition, the cloud-based environment serves as the storage location for the services. The functions are described in the flowchart above, and IoT devices call upon the set of flowcharts. Each circle represents a different workflow feature; some of the services invoke other WS stored in the cloud repository.  
Figure 8 depicts a Venn diagram featuring variables that elucidate the IES formula and the retrieval of services from the repository. The notation \(\in\)\(\partial\) signifies services within the repository, while \(\in\)(\(\partial(\alpha)\)) pertains to the accessible web service. \(\in\)(\(\partial\). \(\alpha(c)\)) denotes services clustered based on method name similarity. The fitness value of a web service, \(\beta\), is a measure of its performance, which is reflected in its response time. Ultimately, services are categorized into high and low fitness values utilizing an if-else statement that compares them with the threshold value, \(\tau\).  
Figure 8: Venn diagram illustrating the IES algorithm.  
Figure 7: Overall Architecture of Improved Eagle Strategy.  
Rule 1:  
\(\alpha=\) available service, when ping value returns value \(1\), read complete  
Rule 2:  
\(c=\) clustered services from the list of available services  
Rule 3:  
\(\beta=\) fitness value, the service response time, calculated based on the ping value.  
Rule 4:  
\(\tau=\) threshold value is the mean value of fitness value for each cluster.  
\[\omega= \in_{\partial.\alpha(c,\beta)>\tau} \tag{11}\]