# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 3 Proposed Rational Clustering Method (RCM) Using Information Exchange
### Balancing Factor Estimation  
UAV cluster formation-based task processing was used for analyzing the available slots and UAV longevity in the distributed network. This is a common feature for improving UAV cluster performance in these information exchanging processes, preventing task incompletion/failures and delays. The balancing factor in UAV cluster formation is processed for improving task allocation through state learning. RCM considers dense information exchange in UAV clusters. Let \(UAV_{\text{C}}(f)\) denote the UAV cluster formation within the network. The instance of dense task processing is represented as \(T_{P}\) and it can be handled through a sensor network given as  
\[UAV_{\text{C}}(f)=\frac{UAV_{L}}{P_{T}\times T_{R}}\times B_{F}\times\left[ \frac{UAV_{L}}{P_{T}}-\frac{UAV_{L}}{T_{R}}\right] \tag{1}\]  
Such that,  
\[T_{P}=\frac{UAV_{L}}{P_{T}\times T_{R}}\times Inf_{ex}\times\left[UAV_{L}\left( \frac{1}{P_{T}-T_{R}}\right)\right]\cdot 3 UAV_{\text{C}} \tag{2}\]  
where \(SL\) is used to denote the state learning process, \(P_{T}\) and \(T_{R}\) represent the pending tasks and task reassignment, respectively, in UAV units for which completing tasks and free slots are identified. The task reassignment process is given in Figure 2.  
The reassignment process relies on \(T_{P}\) at the initial stage, for which \(t\) is allocated on a first-come-first-served basis. Depending on the \(T_{alloc}\)\(\forall\) for the input tasks, the \(B_{p}\) is verified. The verification requires either \(UAV_{c}(f)\) or an independent node for task completion. This requires \((s,UAV_{L})\)\(\forall\)\(P_{T}\) and \(t\) is reassigned for further (extended) processing (Figure 2). The variable \(B_{F}\) is used for balance factor computation based on the information exchanging process; the balancing factor is computed as  
\[B_{F}=\sum_{s}\sum_{i\in UAV_{\text{C}}}\parallel(C_{T}+S_{L})-T_{alloc} \parallel\ ^{2} \tag{3}\]  
where  
\[T_{alloc}=\left\{ \begin{array}{ll}C_{T_{1}}-C_{T_{1}}&\therefore DT_{P}=1\\ S_{L_{1}}-S_{L_{1}}&\therefore DT_{P}=2\end{array} \right. \tag{4}\]  
From Equations (3) and (4), the variable \(s\) represents the cluster sustainability in that UAV network using dense task neighbor information exchange through a UAV network connected with wireless sensors. Let \(C_{T}\) and \(S_{L}\) denote completing a task and free slots, respectively, in a particular UAV cluster \(i\) connected with sensor networks. \(T_{alloc}\) is task allocation based on available slots and UAV longevity, pending, and reassignment in this cluster formation, given the appropriate UAV cluster formation \(i\) in those units within the communication range \(UAV_{\text{C}}(f)\) for which the dense task processing \(DT_{P}=1\). The triple feature-based method is processed for leveraging the UAV cluster performance based on \(T_{P}\). This cluster formation serves as the input to the state learning for dense task processing and  
Figure 2: Task reassignment. \(T_{P}\) considers the distributed nature of the UAV network. The balance factor computation is performed based on triple feature analysis within the cluster. The pending tasks are identified through sensor networks and mobile information. Hence, the pending task is detected as  
\[P_{T}(s,UAV_{L})=exp\left[\frac{(C_{T})^{2}+(S_{L})^{2}}{T_{alloc}}\right]\sin \left(3C_{T}\frac{Inf_{ex}}{T_{P}}+i\right) \tag{5}\]  
In Equation (5), \(P_{T}(s,UAV_{L})\) represents the available pending tasks identified with \(UAV_{L}\) through state learning. Based on the information exchanging process, cluster formation and task processing are prominent in balancing UAV longevity and available slots in that sensor network. Therefore, the balancing factor is handled for identifying task incompletion/failures and delays relying on UAV longevity, pending, and reassignment verification. The task allocation is performed using available slots and UAV longevity based on the condition \(DT_{P}\in UAV_{L}\) and is connected with wireless sensors for identifying failures and delays, as in Equation (5).