With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.

Today, ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency, support highpower missile systems and reduce emissions. However, the electric propulsion of the shipboard system experiences torque fluctuation, thrust, and power due to the rotation of the propeller shaft and the motion of waves. In order to meet these challenges, a new solution is needed. This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system. The battery type and ultracapacitor are ZEBRA and MAXWELL, respectively. The 3-, 4-and 5-blade propellers are considered to produce power and move rapidly. The loss factor has been reduced, and the sea states have been found through the Elephant Herding Optimization algorithm. The efficiency of the proposed system is greatly enhanced through torque, thrust and power. The model predictive controller control strategy is activated to reduce load torque and drive system Root Average Square (RMS) error. The implementations are conducted under the MATLAB platform. The values for torque, current, power, and error are measured and plotted. Finally, the performance of the proposed methodology is compared with other available algorithms such as BAT and Dragonfly (DF). The simulation results show that the results of the proposed method are superior to those of various techniques and algorithms such as BAT and Dragonfly.