# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 3 Improved Eagle Strategy
### Calculate the Semantic Similarities  
Building upon the foundational concepts introduced, we now delve into the specific algorithms and processes that underpin the IES model. The IES is developed in a few steps by determining the semantic similarity between the WS. To perform the semantic similarities calculation, the repository of the WS should be prepared. The WS-DREAM repository (Team, WS DREAM. Retrieved from [[http://wsdream.github.io/dataset/wsdream_dataset1.html/](http://wsdream.github.io/dataset/wsdream_dataset1.html/)]([http://wsdream.github.io/dataset/wsdream_dataset1.html/](http://wsdream.github.io/dataset/wsdream_dataset1.html/)) accessed on 5 September 2024) used an open dataset that contained services from different countries and was collected from real-world WS. As this dataset has been widely used in research, the validation would be much more accurate for comparing the outcome. The benefit of using this dataset is that WS Dream makes the dataset and source code available to the public for WS research.  
The ping method was first used to check the responsiveness and availability of a WS. Ping functions as a computer network administration tool that assesses the duration required for a signal to traverse from its origin point to its target destination. The response time of QoS will serve as a metric to evaluate the effectiveness of the WS. Response time, which is the overall amount of time it takes to execute a WS, is frequently measured in  
Figure 4: The overall progression of the IES.  
milliseconds (ms). Both availability and response time will be predicted or observed when the WS is evaluated. Ping's response is shown below:  
\[\alpha=\text{availability of service} \tag{1}\]  
\[\text{t}=\text{ping response time} \tag{2}\]  
\[\alpha.t=[(1,\text{ ``ping responded''}),(0,\text{ ``no ping respond''})] \tag{3}\]  
As shown in the algorithm above, all available services were marked \"Read Complete\". In contrast, unavailable services were marked \"Error\" in the Status column, and the round-trip response time from the Ping method was recorded in the Elapsed Time column. The method names of all available services were retrieved. Many method names can be found in a single WS.  
The document that holds the Web Service Description Language (WSDL), as referenced in [10], underwent parsing to extract the method name, as well as the input and output parameters, for each service. To gauge the semantic similarities among the services, the Wordnet 3.0 algorithm was employed. The only thing that the similarity measures were used on were the names of the operations and methods. The method names were extracted from the WSDL document and compared using the Wordnet algorithm to determine their similarity scores. Because the clustering of WS requires measuring the distance between different services, calculating semantic similarities was a key stage in the process. A more in-depth analysis and a comparison of other techniques for similarity measurement and clustering are not included in the scope of this work.  
WordNet is a conceptually organized dictionary of English words described by [28]. This dictionary follows a hierarchical structure for its organization. At Princeton, a group led by [PERSON] is responsible for developing Wordnet [29]. If there are two words, namely, w1 and w2, the algorithm used to determine semantic similarities is sim (w1, w2). The algorithm employed to assess semantic similarities between two words, denoted as w1 and w2, is represented as sim (w1, w2). The resulting value falls within the range of 0 to 1, where a value of 0 signifies no similarity whatsoever, and a value of 1 indicates a perfect or exact similarity.