## 6 Discussion  
In this paper, we propose two meta-heuristic algorithms to solve the moving object coverage problem and compare their performance in terms of solution optimality and system running time. Because of the increasing use of mobile sensors, their applications in traditional WSNs, such as target tracking and moving object coverage, have become broader and more practical. The key factors in designing an energy-efficient mobile WSN for a specific purpose include residual energy utilization, mobility, topology, scalability, localization, data collection routing, and quality of service. Therefore, the effective management of the energy consumption of mobile sensors is a crucial research topic, and this paper contributes to the body of literature on this topic by proposing algorithms that can be used to achieve energy-saving mobility. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Scales** & **Similar Quality** & **GA Wins** & **DPSO Wins** \\ \hline
20 & 0 & 40 & 0 \\
50 & 0 & 40 & 0 \\
60 & 0 & 40 & 0 \\
70 & 0 & 40 & 0 \\
80 & 0 & 40 & 0 \\
90 & 0 & 40 & 0 \\
100 & 0 & 40 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average difference in the distance traveled under the GA and DPSO algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Scales** & **Similar Quality** & **GA Wins** & **DPSO Wins** \\ \hline
20 & 23 & 4 & 13 \\
30 & 15 & 5 & 20 \\
40 & 15 & 7 & 18 \\
50 & 13 & 11 & 16 \\
60 & 10 & 15 & 15 \\
70 & 10 & 29 & 1 \\
80 & 5 & 31 & 4 \\
90 & 0 & 40 & 0 \\
100 & 0 & 40 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average traveled distance under the GA and DPSO algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Scales** & **Similar Quality** & **GA Wins** & **DPSO Wins** \\ \hline
20 & 0 & 40 & 0 \\
50 & 0 & 40 & 0 \\
60 & 0 & 40 & 0 \\
70 & 0 & 40 & 0 \\
80 & 0 & 40 & 0 \\
90 & 0 & 40 & 0 \\
100 & 0 & 40 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average fitness values of the GA and DPSO algorithm. Although the research has proposed two meta-heuristic algorithms for resolving the moving object coverage problem effectively, it still has the following limitations: (1) We only take into account the moving distance as the main factor of energy consumption of mobile sensors; (2) we partition the sensor spots into several small zones to simplify the complexity of the problem; (3) we assume that all mobile sensors are identical, which means that the battery energy drain can be identical to each mobile sensor; (4) the experiment only considers the randomly generated trajectories. In real-world applications, the moving trajectory may be specific paths; the proposed meta-heuristic algorithms have disadvantages, such as the computational expensiveness of the GA, the premature convergence in the DPSO algorithm, and no guarantees for achieving the optimal solutions. Despite the aforementioned limitations, the experimental results show that they are still more suitable than traditional heuristic algorithms for solving complex optimization problems, such as the moving object coverage problem. Our analyses revealed that the GA outperformed the DPSO algorithm in all the evaluation metrics, especially in solving large problems. However, the GA required more running time than did the DPSO algorithm. This is because the operations involved in the GA are mainly selection, crossover, and mutation. Because selection involves identifying and retaining higher-quality chromosomes for the next generation, the GA requires more computation time but obtains more accurate solutions than does the DPSO algorithm. Conversely, the advantage of DPSO is its simplicity, which results in rapid convergence. To overcome the limitations and draw on the advantages of both algorithms, a combination of the algorithms can be used. Fusing these two algorithms together results in the creation of a composite algorithm with practical value and high performance. Therefore, a hybrid of the GA and DPSO algorithms should be explored in future research.