## 2 Related Works
The authors in [19] address the challenge of secure resource allocation in wireless sensor networks (WSNs) using a novel trust-aware virtual network embedding (VNE) approach. Traditional VNE techniques focus on optimizing resource usage and quality of service (QoS) but often overlook the security risks inherent in virtualization. The proposed research aimed at the enhancement of both the trustworthiness and the efficiency of WSNs by integrating trust as a key constraint in the VNE process. The author's methodology involved formulating the trust-aware VNE problem as a mixed-integer linear programming (MILP) model [19], incorporating constraints for node measurement accuracy (QoI), link reliability (QoS), and trustworthiness. To address the computational complexity of the MILP solution, the authors developed a heuristic algorithm called TA-VWSN. This algorithm prioritizes embedding virtual nodes and links onto substrate resources that meet the required trust levels, while minimizing resource consumption. Their system also utilizes memoization, pruning, and combinatorial search techniques to improve computational efficiency. Simulation results demonstrated that the TA-VWSN algorithm effectively enhances network security by embedding only onto trusted resources. This improvement in trust awareness leads to better network throughput and reduces measurement errors compared to non-secure VNE methods. While adding trust constraints slightly lowers the acceptance ratio of virtual network requests (VNRs), it significantly improves overall system reliability and security. The key contribution of this work is the integration of trust attributes into the VNE process, marking a significant advancement in virtual sensing. By combining security with traditional QoI and QoS measures, the authors presented a comprehensive framework for secure and efficient resource allocation in virtualized WSNs. In [20], the author explains that a sensor measures a physical quantity and converts it into a signal interpretable by an observer or instrument. Sensors are classified into physical sensors (PSs), which measure phenomena directly at a specific location, and virtual sensors (VSs), which simulate real sensors by processing data from actual sensors using models. While physical sensors can be costly, unreliable, and limited in deployment, virtual sensors offer advantages such as replacing temporarily installed sensors, providing continuous outputs, predicting data for predictive control, and ensuring robustness when physical sensors fail. The author also highlights that virtual sensing, which infers data from remote locations without direct contact, has become an important alternative to physical sensing, with applications in remote sensing by aircraft, satellites, and ships. In [21], the authors discuss the growing importance of sensor technology in linking information systems (ISs) to the physical world, especially with the rise of the Internet of Things (IoT) and cyber-physical systems. Sensors gather data from physical phenomena like temperature, pressure, and humidity, which are integral to AI-based systems and digital twins. However, physical sensors face limitations such as high costs, signal noise, and limited deployment flexibility. Virtual sensors offer a solution by aggregating data from multiple physical sensors, providing benefits such as lower costs, increased reliability, and the ability to measure otherwise unobservable properties. The article explores the concept of virtual sensors, their various applications, and the challenges they face, including data access and platform deployment. The authors also emphasize the need for systems thinking to effectively integrate virtual sensors in cyber-physical systems and IoT solutions. In [22], the authors introduce the concept of virtual sensors to address the limitations of traditional sensor network aggregation methods, which typically rely on standard mathematical operations over homogeneous data types.