## 1 Introduction
Second, a self-organized DTA called hotspot is proposed to utilize the mathematical model developed in the first contribution and allocate the tasks dynamically during foraging. This method allows the robots to allocate themselves to a region without needing continuous communication with other robots. The robots do not have prior knowledge about the resources' real distribution or their deadline states. They search for resources and communicate possible hotspots to the nearest collection zone. The reported information is shared with nearby robots to update their models and predict critical regions. The proposed DTA gives priority to more critical spots to reduce the expiry rate as much as possible. Third, to analyze the performance of ED_Foraging, the dynamic foraging environment was implemented using the autonomous robots go swarming (ARGoS) simulator [14]. This tool is used to implement the new foraging model with simulated foot-bot robots. The implemented model is then used with different DTAs, configurations, and setups. ED_Foraging is adopted for well-known DTA methods in swarm foraging, i.e., ant colony optimization (ACO), blind, and centralized DTAs. The performance of these methods is compared with that of the hotspot DTA method, which allocates tasks based on the proposed model. The foraging and expiry rate measures are collected from 40 trials repeated with three main design issues. Such issues include flexibility over different setups of an unknown environment, scalability for different numbers of robots, and the robots' robustness on different map sizes. The average and detailed performance is assessed in addition to the distribution using the Friedman statistical significance test and visualized using box plots. From practical experiments, it is possible to confirm that the result of using one collection zone is significantly worse than that of using multiple collection zones in terms of the expiration and foraging rates. Moreover, when comparing the DTA methods that use multiple collection zones, it is found that they have similar foraging rates but different expiry rates. From the flexibility experiments, it is concluded that hotspot DTA is best suited to the ED_Foraging approach. It produces consistent results for different map sizes, and it has an expiry rate that is either better than those of the other methods or similar to the best one in most setups. The same result is found when testing the scalability of the methods; that is, the results for the hotspot DTA method are consistent when the number of robots changes, while the other methods, especially ACO and centralized DTA, are highly inconsistent. Finally, in regard to robustness, it can be concluded that the hotspot DTA method is the least affected method. It aims to maintain an average behavior irrespective of the map size even though the resources are reduced. The remainder of the paper is organized as follows: The related background is discussed in Section 2, and the related works are reviewed in Section 3. The proposed approach is described in detail in Section 4. The experimental results are discussed in Section 5, and the findings are discussed in Section 6. The conclusions and suggestions for future work are presented in Section 7.