Application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in Drone Thermal Imaging for Solar Farm Monitoring

Abstract

The impact of multimedia in day-to-day life and its applications will be increased greatly with the proposed model (MSVPC)–5G Multicast SDN network eminence video transmission obtained using PSO and cross layer progress in wireless nodes. The drone inspection and analysis in a solar farm requires a very high number of transmissions of various videos, data, animations, along with all sets of audio, text and visuals. Thus, it is necessary to regulate the transmissions of various videos due to a huge amount of bandwidth requirement for videos. A software-defined network (SDN) enables forwarder selection through particle swarm optimization (PSO) mode for streaming video packets through multicast routing transmissions. Transmission delay and packet errors are the main factors in selecting a forwarder. The nodes that transfer the videos with the shortest delay and the lowest errors have been calculated and sent to the destination through the forwarder. This method involves streaming to be increased with the highest throughput and less delay. Here, the achieved throughput is shown as 0.0699412 bits per second for 160 s of simulation time. Also, the achieved packet delivery ratio is 81.9005 percentage for 150 nodes on the network. All these metrics can be changed according to the network design and can have new results. Thus, the application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in drone thermal imaging helps in monitoring solar farms more effectively, and may lead to the development of certain algorithms in prescriptive analytics which recommends the best practices for solar farm development.

Data Fusion and Ensemble Learning for Advanced Anomaly Detection Using Multi-Spectral RGB and Thermal Imaging of Small Wind Turbine Blades

“…For example, in wind turbine inspections, deep learning models analyze thermal images to detect blade defects by identifying thermal patterns indicative of different types of damage, thereby optimizing the precision and efficiency of inspections [19]. In the context of solar farms, drones capture images that are processed by deep learning models to identify issues such as dirt accumulation, panel breakages, or other anomalies that could reduce the efficiency [20]. Similarly, deep learning models analyze images or videos captured by drones or robotic devices to detect rust, cracks, or leaks in oil and gas pipelines [21].…”