# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Performance and Resources Metrics  
#### 5.7.1 Computational Load and Communication Efficiency  
This section analyzes the communication overhead and computational load associated with each swarm model considered. Areas of focus are operational latency, memory requirements, bandwidth consumption, and CPU utilization monitoring. This analysis aims to investigate the impact of algorithmic and control mechanism complexities on processing and communication requirements. This analysis is on how different control schemes, whether centralized or decentralized, affect the computational and communication efficiency of a swarm. The relationship between computational efficiency and communication overhead have been examined, particularly in the context of large-scale operations where the number of swarm robots must work well together given the network resource constraints, as shown in Figure 16.  
Figure 16: Communication overhead (CO) in Hz and computational load (CL) in Kb/s for different swarm models. (**a**) Baseline model, (**b**) Ant model, (**c**) Firefly model, (**d**) Honeybee model, each with 5, 10, and 15 robots over a 10-min period. The impact of communication overhead, computational load, and the number of robots in a swarm on overall system performance is significant, especially as the number of robots increases from 5 to 15. The honeybee model shows a dramatic increase in both communication overhead and computational load. Compared to the baseline model, the honeybee model experiences a 200% increase in communication overhead and a 300% increase in computational load. This sharp rise is due to the potential bottleneck of the complex hybrid control system, in which the master robot acts as the central coordinator and involves extensive data sharing with the master robot and nearby agents, receiving location data, real-time coordination, and robot recruitment from the master robot. These processes strain the computational resources significantly, making the honeybee model's system less stable in larger operations compared to decentralized models. The firefly model, while less demanding than the honeybee model, still shows notable increases in communication overhead and computational load by 150% and 167%, respectively, compared to the baseline model. This increase is attributed to the firefly model's bioluminescence light communication system, where beacon lights are used to recruit neighboring agents. As the number of robots grows, the need to detect and recruit via these light beacons escalates, increasing both communication demands and computational load. In contrast, the ant model exhibits a more moderate increase, with a 100% rise in communication overhead and a 133% rise in computational load compared to the baseline model. This increase is primarily due to the model's division of labor, where the robot group is split, necessitating twice the number of ore block detections. However, these increases remain manageable, allowing the system to maintain stability even as more robots are added. The baseline model maintains the lowest communication overhead and computational load, thanks to its simple finite state machine (FSM) and model design. As the number of robots increases, the baseline model shows only a slight rise in communication overhead and computational load, keeping latency low and minimizing strain on CPU resources. The number of robots in a swarm is crucial, as it directly influences system operation. In the honeybee model, adding more robots significantly amplifies communication overhead and computational load, leading to higher operational latency, greater CPU utilization, and increased memory and bandwidth requirements. For instance, in a scenario with 15 robots, the honeybee model fully utilizes 16 CPU cores, drains 15 GB of memory, and even uses an additional 2 GB of swap space due to high computational demands.