## 3 The Proposed Sensor Deployment Algorithms
### The Particle Swarm/Genetic Hybrid Algorithm
As a result, deployment costs and energy consumption significantly increase, and the overall perception probability and connectivity also improve. However, the increase in deployment costs and energy consumption outweighs the increase in connectivity and perception probability, which in turn leads to an increase in the overall optimization objective function \(F\). A similar conclusion applies in scenario 2. Next, we compare the running time. As the required perception probability increases, the time taken by the algorithm to search for solutions that meet the conditions also increases,

Figure 5: Average optimization effect of the objective function for two algorithms under different scenarios and varying minimum sensing probabilities. Figure 6: Average runtime of the two algorithms under different scenarios and varying minimum sensing probabilities. which results in an overall increase in the running time. The algorithm's running time in scenario 1 is generally lower than in scenario 2, possibly because scenario 2 has more monitored targets, requiring more checks for perception probability and connectivity, thus consuming more time. Finally, comparing the PSGH algorithm with the FSWH algorithm, it is evident that when the minimum connectivity requirement is met and the weights of various indicators are the same, regardless of whether in scenario 1 or scenario 2, and under different minimum perception probability requirements, the PSGH algorithm outperforms the FSWH algorithm in terms of both optimizations of the overall objective function \(F\) and the corresponding program running time. ### Comparison of Comprehensive Optimization Metrics Under Different Connectivity Requirements

This section primarily studies the comprehensive optimization metrics under different minimum connectivity requirements. In the simulation process, we set the minimum perception probability to 50%, and the minimum connectivity requirements are set to 2, 3, and 4. The weights of the sub-functions are set to \(\alpha_{1}=0.25,\ \alpha_{2}=0.25,\ \alpha_{3}=0.25,\ \alpha_{4}=0.25\). Independent simulations of 100 experiments are conducted under these three different connectivity requirements, comparing the comprehensive optimization objective function \(F\) and the program's running time under two different scenarios. The results are shown in Figures 7 and 8. First, we compare the optimization effects on the overall objective function \(F\). In scenario 1 (i.e., when the monitored targets are uniformly distributed), both algorithms show an increase in the objective function \(F\) as the connectivity requirement is raised. This is because when the connectivity requirement increases, it results in a higher number of deployed sensors or the use of sensors with higher performance/cost ratios. Consequently, both deployment cost and energy consumption significantly increase, while the overall perception probability and connectivity also increase. However, the increase in deployment cost and energy consumption is greater than the increase in connectivity and perception probability, leading to an overall increase in the objective function \(F\). The same conclusion holds in scenario 2. Additionally, compared with the scenario where the perception probability is increased, it can be observed that improving connectivity from 2 to 4 has a less significant impact on the overall objective function \(F\) than increasing the perception

Figure 7: Average optimization effectiveness on the objective function of the two algorithms under different scenarios and varying minimum connectivity requirements. probability from 50% to 70%. Next, we compare the running time. As the connectivity requirement increases, the algorithm takes longer to search for feasible solutions, resulting in a longer overall runtime. Furthermore, the runtime in scenario 1 is generally lower than that in scenario 2.