Over the past several decades, Unmanned Aerial Vehicles (UAVs) have been used in a variety of applications with 2 basic classifications of UAVs' a scivilian and military drones. Drones capture a variety of multimedia data. Among the multimedia data, images with overlapping regions need to be stitched to generate a panorama which would provide image data of 'n' number of images captured by a drone. The data captured by drones should be effectively communicated to a Ground Control Station (GCS). Hence in the research, 4 drones capture both text data and images. Each drone generates a corresponding panorama for the set of images captured by it and communicates both its text data and panorama to the GCS. 2 desktops are used for performing the experiments using client-server communication. Client desktop is used for performing simulations using AirSim simulator (which consists of 4 drones) on the Unreal Engine 4.25 platform, and generate panoramas for the set of images captured by each drone. Server desktop acting as GCS is used to accumulate text data and image data from 4 drones. Image stitching analysis has been done using 2 Python versions and Open CV versions, and 2 AirSim environments. Image stitching results were more effective with the use of Python version 3.7.1 and Open CV version 3.4.2 pair (image stitching success rate, and image stitching accuracy = 100%) when compared to that with Python version 3.9.1 and Open CV version 4.5.2 pair (image stitching success rate = 75%, image stitching accuracy = 33.33%). Both the text data and panoramas from 4 drones were successfully transmitted to the GCS.