## 6 Proposed Approach
### Multi-Objective Area Coverage Optimization Based on IAPSO
## 7 Performance Evaluation

### Simulation Environment

In this section, we conducted simulation experiments to verify the efficiency and the stability of the proposed approach in MATLAB (version 7.11) and on an Intel core i7 processor with chipset 2600, 3.40 GHZ CPU 4 GB RAM running on the platform Microsoft Window 10. In simulation environments, the number of directional sensor nodes vary from 100 to 200, and the cluster head ratio is set as 6%. The network parameter and IAPSO parameter are shown in Tables 1 and 2 respectively. ### Performance Evaluation

In the process of simulation, we compare the proposed coverage and energy consumption optimization approach with existing ones. In terms of coverage optimization, the proposed approach is compared with simulated anneal (SA) and random deployment (RD) under the circumstance that cluster-based energy efficiency approach is the same. In terms of energy consumption optimization, the approach is compared with LEACH, LEACH-C, PSO-C. To evaluate the performance of our proposed \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Target area & \(500\times 500\) m\({}^{2}\) \\ Base Station position & \((250,250)\) \\ Number of directional sensor nodes & 100–200 \\ Number of cluster heads & 6–12 \\ Sensing radius of directional sensor nodes & 6 m \\ Sensing angle of directional sensor nodes & \(\pi/6\) \\ Initial energy of directional sensor nodes & 2 J \\ \(E_{dec}\) & 50 nj/bit \\ \(\varepsilon_{fs}\) & 10 pj/bit/m\({}^{2}\) \\ \(\varepsilon_{mp}\) & 0.0013 pj/bit/m\({}^{4}\) \\ \(d_{0}\) & 87 m \\ Packet length & 4000 bits \\ Message size & 500 bits \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of particles & 30 \\ \(C_{1}\) & 2 \\ \(C_{2}\) & 2 \\ \(\omega\) & \([0,1]\) \\ \(D_{1}\) & \([0,200]\) \\ \(D_{2}\) & \([6,12]\) \\ \(V_{\max}\) & \(2\pi\) \\ Number of iterations & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IAPSO parameters. approach, we consider the four factors, namely coverage ratio, redundancy ratio, number of alive nodes and number of data packets received by BS. #### 7.2.1 Comparison of Coverage Ratio

In this experiment we run the approaches for comparing the coverage ratio of DSN by varying number of sensor nodes from 100 to 200 and number of cluster heads from 6 to 12. The performance of IAPSO-MOACO is compared with simulated anneal (SA) and random deployment (RD) under the circumstance the cluster-based energy efficiency approach is the one proposed in this paper. The comparison results of the IAPSO-MOACO with other approaches are shown in Figure 4a-c.

As can be seen from the figures, the IAPSO-MOACO has better performance than SA and RD in most of time because the proposed IAPSO algorithm can effectively avoid local optima and improve coverage ratio. With the round of data transmission increase, the coverage ratio will gradually decrease because the number of alive nodes decreases. However, the IAPSO-MOACO can still guarantee higher covergae ratio in comparison with other approaches when the round is no more than 700. It also can be noted that the IAPSO-MOACO still has better optimization performance as the size of the network increases. #### 7.2.2 Comparison of Redundancy Ratio

In this experiment we run the approaches for comparing the redundancy ratio of DSN by varying number of sensor nodes from 100 to 200 and number of cluster heads from 6 to 12. The performance of the IAPSO-MOACO is compared with SA and RD under the circumstance the cluster-based approach is the same one proposed in this paper. The comparison results of the IAPSO-MOACO with other approaches are shown in Figure 5a-c.

Figure 4: Comparison in terms of coverage ratio with 100 to 200 nodes. (**a**) 100 nodes with 6 cluster heads; (**b**) 150 nodes with 9 cluster heads; (**c**) 200 nodes with 12 cluster heads.