# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
###### Abstract
The UAV clustering technique is used in the current work to identify required communication resources and produce data for task handling systems [15]. For UAV (Unmanned Aerial Vehicle) cluster task assignment using sensor network and mobile data, the Rational Clustering Method (RCM) is a crucial component of the distributed algorithm. The RCM is an effective method for rapidly evaluating the relative value of alternative clustering assignments with respect to a specific criterion of interest. This allows for a low-overhead method of delegating tasks to different UAVs. Moreover, it enables UAVs to modify their cluster assignment depending on the circumstances. This is especially helpful when the UAVs' applications require them to quickly adjust to new conditions. Through the use of the RCM, the distributed algorithm can more efficiently determine which UAVs should be assigned which tasks, resulting in better overall performance. In this work, we introduce a decentralized algorithm for assigning tasks within UAV clusters by utilizing data gathered from sensor networks and mobile devices. To efficiently utilize a large fleet of unmanned aerial vehicles (UAVs), the algorithm takes into account data from a sensor network and each vehicle's location while in flight. There are two components to this algorithm. To begin, we use the UAVs' locations to create a graph structure, with each UAV serving as a node and edge weights determined by the physical distance between them. Second, the UAVs are given missions based on the data gleaned from the mobile devices and the sensor network. Different algorithms are used to optimize task assignment based on the type of task being performed. The unmanned aerial vehicles (UAVs) can begin their missions once the task allocation is finalized. Regarding the assignment of tasks to UAV clusters, the Rational Clustering Method (RCM) is a decentralized algorithm that takes into account data from sensor networks and mobile devices. To accomplish this, the RCM algorithm facilitates extensive communication between tasks through the use of dense task neighbor data. The RCM algorithm considers where the UAVs are, what they need to do, and how the communication network is set up. Due to its decentralized nature, the RCM algorithm facilitates rapid responses to shifting conditions by minimizing the need for human intervention in task allocation. The algorithm also guarantees a fair distribution of work between UAV clusters and a higher degree of efficiency in task allocation. As an added bonus, the RCM algorithm can be used to reduce the required number of UAVsfor a mission without sacrificing efficiency. The main contribution of the current work is as follows:  
* Dense task neighbor information exchange is used by the RCM algorithm, which entails UAVs in the area communicating with one another to decide the allocation and frequency of jobs. * As a result, each UAV will be better able to make decisions based on accurate information about its neighbors' preferences and task assignments. * The effectiveness and efficiency of task allocation can be increased by using this method, which ensures that each UAV makes a decision that is optimal not only for itself but also for the cluster as a whole.