# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 2 Related Works  
[PERSON] et al. [16] developed an adaptive fault-tolerant distributed control algorithm for the unmanned aerial vehicle (UAV). During its development, the local information interaction method was used to identify essential factors for UAVs, and the feature extraction method was also used to provide necessary data for distributed control. Vertical take-off and landing (VTOL) problems were identified that increased the feasibility of its application, and the final proposed algorithm improved the effectiveness level of UAVs. [PERSON] et al. [17] introduced a dynamic fault-tolerant task scheduling model using flexible network architecture (DSM-FNA) for UAV clusters. The main aim of the proposed model was to provide proper task scheduling services to UAV clusters. DSM-FNA achieved high accuracy in fault-tolerant detection that reduced delay time in computations. The proposed DSM-FNA model maximized the performance of UAV clusters. [PERSON] et al. [18] proposed a large-scale clustering approach to identify UAV swarms. A 3D trajectory optimization approach was also used to collect the necessary data to minimize identification time consumption. The proposed large-scale UAV swarm data collection provided better service in various applications, and the proposed approach improved the overall performance and reliability of UAV systems. [PERSON] et al. [19] introduced combinational pigeon-inspired optimization (CMOPIO) based on multi-objective clustering analysis. CMOPIO is mainly used to solve clustering problems presented in multi-objective systems. The PIO method reduced computation time and energy consumption levels, enhancing the feasibility of its application. The proposed CMOPIO method improved effectiveness and reliability by solving clustering problems. [PERSON] et al. [20] proposed a new UAV-based intelligent on-demand meal delivery system (UIOMDS). Stochastic event scheduling (SES) was used to schedule tasks based on priorities. UIOMDS reduced UAV clustering problems by solving them via SES. The proposed UIOMDS method improved the efficiency and robustness of the service for users. [PERSON] et al. [21] designed a standard global and local path planning optimization method for unmanned aerial vehicle task scheduling (JGLPP-UTS) systems. The main aim of the proposed method was to provide a better schedule and to reduce UAV problems. The global path rate was also reduced by JGLPP, which maximized performance quality. The proposed JGLPP-UTS improved the overall reliability, feasibility, and effectiveness of task scheduling systems. [PERSON] et al. [22] designed an energy and task completion time minimization algorithm (ETCTMA) for task scheduling. The large-scale user equipment that provided the necessary information for task scheduling was identified. The proposed method reduced overall task computation time, thus improving the efficiency of applications. The proposed ETCTMA method also reduced scheduling time and energy consumption levels. [PERSON] et al. [23] developed an optimal spraying task assignment problem (OSTAP) for crop protection in multi-agent systems. OSTAP identified problems via a particular set of characteristics and a particle swarm optimization (PSO) algorithm was used to detect problems. The proposed OSTAP resolved every crop protection problem, improving the efficiency of multi-agent systems. [PERSON] et al. [24] proposed multi-unmanned aerial vehicle surveillance using dynamic task scheduling architecture. Tasks were classified according to functions, priorities, and characteristics. Behaviors and relations among functions were also identified that provided necessary information to task scheduling systems. The proposed method was a hierarchical task-scheduling process that improved the overall significance level of UAVs. [PERSON] et al. [25] introduced a task offloading mechanism for a UAV-aided wireless sensor network (WSN). A utility-based learning algorithm was used that identified important details and produced data for the task scheduling mechanism.