# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 4 Comparative Analysis
### Delay  
The task allocation and reassignment in the UAV cluster for dense task processing and neighbor information exchange are illustrated in Figure 13. In the proposed method, the wireless sensor clusters satisfied the requirement of less delay, and the completion time for task allocation was performed through state learning for continuous task processing within the cluster. In the current work, the cluster sustainability and UAV longevity analysis, \(DT_{P}\in UAV_{L}\) for task reassignment, and cluster deformation were computed to satisfy the condition \(1-\left[\frac{(T_{alloc})_{i}}{\Sigma(P_{T}-T_{R})_{i}}\right]\) for preventing completion time and task overloading. The state learning-identified UAV longevity, pending, and reassignment were mitigated for dense task processing and the information exchanging process. The task handling was processed in a densely populated UAV scenario and was proceeded using the above Equations (6)-(10). In the proposed method, the available slots and longevity were computed for leveraging the UAVs' performance at different time intervals instead of the task allocation relying on high-slot UAVs, preventing task incompletion/failures and delays using state learning. The completion time was computed in the cluster formation and task-processing analysis. Tables 1 and 2 present the comparative analysis summary for varying clusters and slots.  
Table 1 shows that the proposed RCM-IE maximized the task processing rate by 8.16% and the completion ratio by 10.3%. This method reduced reassignment, failure, and delay by 12.5%, 9.87%, and 11.79%, respectively. The table summarizes the results of a comparison study using five performance metrics to evaluate four distinct clusters: FDSA, CLA-TO, ETCTMA, and RCM-IE. The table shows that when comparing different metrics, such as Task Processing Rate, Completion Ratio, and Delay, RCM-IE consistently outperformed the other methods. Except for the Reassignment rate, where it excelled, FDSA had the lowest values for all other metrics, while ETCTMA had the lowest Reassignment rate and second-lowest delay, and CLA-TO had the second-highest Completion Ratio.  
Figure 12: Failure analysis.  
Table 2 shows that the proposed RCM-IE maximized the task processing rate by 9.44% and the completion ratio by 9.64%. This method reduced reassignment, failure, and delay by 11.83%, 10.52%, and 9.26%, respectively. The table summarizes the comparison of four clusters (FDSA, CLA-TO, ETCTMA, and RCM-IE) based on five performance metrics, measured with respect to time slot. The data show that RCM-IE excelled in most categories, including Task Processing Rate, Completion Ratio, Failure Rate, and Delay. Although its Reassignment rate was the highest of any metric, FDSA performed poorly in every other respect. The Completion Ratio of CLA-TO was the highest, and the Reassignment rate and delay of ETCTMA were the lowest, and both were considered to be relatively good. Each metric's values varied depending on whether they were measured per slot or per task, and this could have affected how the data were interpreted.