# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
###### Abstract  
Cluster formation and task processing are standard features for leveraging the performance of unmanned aerial vehicles (UAVs). As the UAV network is aided by sensors, functions such as clustering, reformation, and autonomous working are adaptively used for dense task processing. In consideration of the distributed nature of the UAV network coupled with wireless sensors, this article introduces a Rational Clustering Method (RCM) using dense task neighbor information exchange. The Rational Clustering Method (RCM) is an algorithm for dense task neighbor information exchange that can be used to cluster objects according to their shared properties. Each object's task neighbors, and the similarities between them, are calculated using this method. Starting with the task density of its neighbors, the RCM algorithm gives each object in the dataset a weight. This information exchange process identifies a UAV units' completing tasks and free slots. Using this information, high-slot UAVs within the communication range can be grouped as clusters. Unlike wireless sensor clusters, task allocation is performed on the basis of available slots and UAV longevity within the cluster; this prevents task incompletion/failures and delays in a densely populated UAV scenario. Cluster sustainability or dispersion is recommended when using distributed state learning. State learning transits between the pending task and UAV longevity; an intermediate state is defined for task reassignment amid immediate cluster deformation. This triple feature-based distributed method balances tasks between failures, overloading, and idle UAVs. The RCM was verified using task processing rate, completion ratio, reassignment, failures, and delay. Task processing rate was increased by 8.16% and completion ratio was increased by 10.3% with the proposed RCM-IE. Reassignment, failure, and delay were all reduced by 12.5%, 9.87%, and 11.99%, respectively, using this method. clustering; sensor network; state learning; task assignment; UAV +
Footnote †: journal: Medical  
clustering technique is used for UAV task management systems. RL identifies essential tasks based on priorities and characteristics [7]. The RL clustering technique improves the decision-making accuracy level, which maximizes the UAVs' performance. Task management requires a proper plan and strategy to perform tasks; as such, the UAV clustering technique provides optimal and effective planning data that reduces the delay time in performing tasks [8]. The cluster-based planning strategy is also used for UAVs in task management systems. The cluster-based method identifies essential factors and features that are needed to be performed in task management [9]. The feature extraction technique is used in the present study to extract essential features based on specific functions. Multi-UAV clusters are used in clustering techniques that provide the necessary data related to tasks and the operation of task management [10]. Artificial intelligence (AI) is an emerging technology in various applications and systems and utilizes human-like intelligence to perform certain tasks in machines, mainly computers [11]. AI techniques reduce labor or worker ratio in many fields. The AI-based clustering technique is used for task-handling processes and is mainly used for multi-task offloading in UAVs [12]. AI tackles the problems presented in task offloading and increases the accuracy level in problem prediction, which improves the efficiency of task management systems. The UAV clustering technique is used in task handling; it controls the server and provides individual tasks to every server [13]. The UAV clustering technique uses specific algorithms to provide good communication services to management systems and detects disasters anticipated during task planning. The task-handling method requires accurate information to perform further tasks in UAVs [14]. AI-based, large-scale task offloading methods are used in task handling to maximize effectiveness levels in UAVs.