Exploring Effective Sensor Deployment Techniques for Dynamic Region of Interest

The increasing demand for real-time, adaptive monitoring across various domains, such as environmental surveillance and disaster management, necessitates a shift from static to dynamic sensor deployments within Mobile Wireless Sensor Networks (MWSNs). One prominent application scenario involves dynamic changes in the Region of Interest (RoI), requiring sensor redeployment. However, defining dynamic RoI scenarios lacks specificity. To address this gap, this study introduces two distinct categories defining a change in RoI: transformed RoI, where the initial RoI undergoes affine transformations, and evolved RoI, representing entirely new polygonal configurations. Additionally, the study explores limitations within current state-of-the-art distributed deployment algorithms, which hinder performance in dynamic RoI settings. These limitations are investigated through experiments involving two main types of distributed algorithms: geometric-based and virtual force-based deployment algorithms. The findings underscore significant performance challenges faced by existing algorithms in dynamic RoI scenarios, emphasizing the need for the development of more resilient algorithms capable of adapting to dynamic RoIs while ensuring network connectivity and minimizing node movement.