# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 3 Proposed Rational Clustering Method (RCM) Using Information Exchange  
The cluster task assignment was designed to consider the distributed nature of the UAV network when dense task processing output is used for leveraging its performance. From the UAV cluster, formation and task processing improve the balancing factors and completion ratio due to completing tasks and free slots being identified in the information exchange process. In cluster formation, dense task processing and neighbor information exchange are performed through wireless sensors. In the current work, the above-mentioned neighbor information analyzes the high-slot UAVs in the network grouped as clusters. The communication range between users and wireless sensor networks for dense information exchange is performed for task allocation, which refers to cluster formation and task handling. There are three processes in state learning: longevity, pending, and reassignment. These are identified and grouped with the above information. The balancing factors and completion time are analyzed for dense task processing to improve task handling. The task handling observed from the sensor network relies on high-slot UAV requirement within the cluster. Therefore, the task allocation is analyzed using available slots and UAV longevity for completing tasks at different time intervals via wireless sensor clusters. This analysis aids in the detection of task incompletion/features and delay based on densely populated UAV scenarios. In the current work, the dense information exchange relied on a particular UAV network connected with wireless sensors. It is used for clustering, reformation, and sensors, and continues working to improve task completion and free slots through a state learning process. The UAV cluster includes various users and sensor networks for performing dense tasks and responding to the users based on cluster sustainability. In a triple feature-based distributed method such as longevity, pending, and reassignment analysis in particular UAVs, the task completion and slots are identified with UAV units for dense task processing. The RCM-IE is depicted in Figure 1.  
The completing tasks and free slots are identified through the state learning paradigm for balancing task allocation and cluster formation in the UAV network. The triple feature-based method balances tasks between failures and overload for identifying pending tasks. UAV longevity is transmitted between the learning processes; an intermediate state is thus defined for task reassignment and immediate cluster deformation. The challenging role in the proposed method was the identification of idle UAVs, failures, and overloading at the time of balancing and completing tasks through a triple feature-based distributed method. The completed tasks and free slots were identified with UAV units within the communication range using the above-mentioned information. The triple feature-based distributed method for completing tasks is a consecutive process that relies on available slots and UAV longevity within the network through state learning. The high-slot UAVs were grouped as clusters within the UAV network for improving cluster sustainability or dispersion. The task reassignment and cluster deformation activities reduce overloading and failures while augmenting the task processing rate through balancing factor validation. The proposed method performs tasks and responses for the available users and sensor networks within the communication range. Based on the task allocation, the pursued UAV cluster is analyzed for task processing through state learning based on RCM using dense task neighbor information exchange. The consecutive processes of UAV cluster formation and task processing validation have been analyzed with completing tasks and free slots for improving the task completion ratio. Unsupervised learning methods, such as the rational clustering approach based on information exchange, can be used to organize data into meaningful groups.