## 7 Conclusions  
In this study, our work is a proof of concept to assert the feasibility of combining a mix of physical and virtual sensors to advance any models similar to our experimental SSWSN model by expanding the deployment area while obtaining acceptable performance. We advanced our previously developed SSWSN model by integrating virtual sensor data with physical sensor readings. Our experiments demonstrated that the inclusion of virtual sensors significantly influences the model's predictive performance. Among the configu \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Objective** & **Use of Virtual Sensing** & **Focus Area** & **Novel Contribution** \\ \hline \multirow{4}{*}{**Proposed Methodology**} & Enhance forest fire prediction by integrating physical and virtual sensors. & Generate virtual sensors using polynomial regression. & Forest fire prediction in WSNs. & Combines physical and virtual sensors for enriched predictions. \\  & Develop virtual soil moisture sensors for cost-effective smart farming. & Predict soil moisture using LSTM networks. & Smart farming applications. & Reduces physical sensor dependence in agriculture. \\  & Improve WSN fault tolerance with fuzzy logic-based virtual sensors. & Approximate missing data with fuzzy logic rules. & WSN fault tolerance and robustness. & Improves data reliability under sensor failures. \\  & Enhance WSN resource allocation & Use trust levels to enhance virtual sensing in resource allocation. & Secure and reliable resource allocation in WSNs. & Introduces trust constraints for secure WSN virtualization. \\  & Define virtual sensing as a cost-effective alternative to physical sensors. & Simulate real sensors using models and data from physical sensors. & Remote sensing applications. & Highlights virtual sensing’s versatility and advantages. \\  & Explore virtual sensors for IoT and cyber–physical systems. & Aggregate data to create new measurable properties. & IoT and digital twin systems. & Emphasizes cost and reliability improvements for IoT systems. \\  & Enable complex data aggregation using virtual sensors. & Combine data from diverse sensors for abstract measurements. & Adaptive in-network processing for sensor networks. & Prototype middleware for flexible and reusable virtual sensing. \\  & Provide customized views of physical sensors through a sensor cloud. & Aggregate data from multiple sources for user-defined views. & Sensor cloud architecture for scalable sensing. & Defines layered management of virtual sensors in sensor clouds. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of proposed methodology with related works. rations evaluated, the supervector combining 15 physical sensors with 30 virtual sensors yielded the most consistent improvements, effectively balancing feature diversity and model performance. Although some scenarios incurred slight decreases in their accuracies, on the other hand, the benefit gained by the potential of a huge field coverage expansion compensates for this drawback. These findings highlight the potential of virtual sensors to enhance the representation of sensor-based data in our SSWSN model to better capture complex environmental interactions. While this work provides valuable insights into the use of virtual sensors, several avenues for future research remain. First, we plan to explore the use of more advanced feature generation techniques, such as neural network-based embeddings, for virtual sensor creation. Second, incorporating domain-specific knowledge into the generation process may further enhance the interpretability and relevance of the virtual features. Lastly, scaling this approach to larger and more diverse datasets will allow for validation of the methodology across various applications, paving the way for its adoption in real-world general predictive systems.