## 5 Performance Evaluation  
This section examines the performance improvement of the proposed \(EBDC\) mechanism compared with the _Angle-based_ approach. Furthermore, the proposed \(EBDC\) mechanism is also compared with the existing approaches proposed by studies [6] and [11] which are referred to as \(Fixed\) and \(RMDC\), respectively. The _Angle-based_ approach is a heuristic-based algorithm which initially partitions the circular monitoring region \(M\) into \(f=360/g\) fans based on the angle \(g\). These fans can be sequentially numbered from 1 to \(f\) in the clockwise direction. In general, the fans will be classified into two sets: \(odd\) and \(even\) sets. The odd set consists of fans numbered with odd numbers while the even set comprises the remaining fans numbered with even numbers. As shown in Figure 5(a,b), in the odd (even) round, the mobile sink traverses the edges of each fan belonging to the odd (even) set one by one in an increasing order of fan number. Figure 5: In the odd (even) round, the mobile sink traverses the edges of each fan belonging to odd (even) set one by one in an increasing order of fan number. (**a**) Odd round; (**b**) Even round. The odd and even rounds will be applied by the mobile sink in turn until the energy of the mobile sink is exhausted. Herein, we assume that all sensors know the traverse rules as mentioned above and are able to estimate the current location of the mobile sink at any given time. The _Fixed_ approach employs a fixed sink located at the central point of \(M\) to collect data while the _RMDC_ approach uses a mobile sink moving along the boundary track of \(M\) to collect the readings as shown in Figure 1.  
Table 3 gives the parameters used in our simulation. Each simulation result is obtained from the average of 100 independent runs and the 95% confidence interval is always smaller than 5% of the reported values. The following depicts the results of our performance evaluations. Figure 6(a,b) compare the proposed _EBDC_ mechanism with the _Fixed_, _Angle-based_, and _RMDC_ approaches in terms of network lifetime. Herein, the network lifetime is measured by the time interval starting from the time that sensors have been deployed to the time that a coverage hole appears. The four mechanisms are compared by varying the number of sensors and data report time \(t\) in Figure 6(a,b), respectively. As shown in Figure 6(a), since there is no sleep-wake scheduling mechanism applied to the WSN, all sensors should keep working on sensing and communication. Thus, the time that the first coverage hole appears does not change a lot in the four approaches compared. As a result, the network lifetimes of _Fixed_, _Angle-based_, _RMDC_, and the proposed _EBDC_ approaches maintain constant curves. On the \begin{table}
\begin{tabular}{l l} \hline Network radius \(R\) & 1,500 m \\ The number of sensors & 500\(\sim\)1,500 nodes \\ Initial energy of each sensor & 10,000 J \\ Packet transmission cost & 0.075 J/s \\ Packet reception cost & 0.030 J/s \\ Idle cost & 0.025 J/s \\ Data report time \(t\) & 3\(\sim\)12 h \\ \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 6: Comparison of _Fixed_, _Angle-based_, _RMDC_, and _EBDC_ in terms of network lifetime. (**a**) Comparison of four mechanisms in terms of network lifetime by varying the number of sensors; (**b**) Comparison of four mechanisms in terms of network lifetime by varying the data report frequency. contrary, as shown in Figure 6(b), the network lifetimes of _Fixed_, _Angle-based_, _RMDC_, and the proposed _EBDC_ approaches increase with the data report time \(t\). This is because the lower value of data report time means that all sensors will report their readings more frequently, resulting in poor performances in terms of network lifetime. Moreover, as shown in Figure 6(a,b), in the _Fixed_ approach, the sink is fixed and thus the data-relaying workloads totally concentrate on a small number of sensors, leading to a poor network lifetime.