# Efficient Wireless Charger Deployment for Wireless Reshapeable Sensor Networks
## 6 Experiments and Simulations
### Simulations
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Parameter** \\ \hline Sensor deployment cuboid & 20 m (\(L\)) \(\times\) 15 m (\(W\)) \(\times\) 2.3 m (\(H\)) \\ Number of sensors & 50, 100, 150, 200, 250 \\ Effective charging distance & 3 m (\(D\)) \\ Opening angle & 30\({}^{\circ}\) (\(A\)) \\ Height of grid points & 2.3 m (\(H\)) \\ Grid point separation & 1.8 m (\(S\)) \\ Coverage requirement of sensor nodes & 1-, 2-, or 3-coverage \\ Energy requirement of sensor nodes & 0.18, 0.54, 0.9 mW \\ Times of simulation per case & 30 times/case \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation settings. is 1.8 V and the working currents are 270 uA, 0.1 uA, 11.1 mA and 16.6 mA for processor processing, sleep mode, transmission mode, and reception mode, respectively. According to Reference [28], the sensor node can use the energy most efficiently when the ratios for the processor processing, sleep mode, transmission mode, and reception mode are 0.47%, 98.77%, 0.48% and 0.28%, respectively. According to the ratios, the current is about 0.1 mA for the eZ430-based sensor node to use the energy most efficiently, so the power consumption is thus about 0.1 mA \(\times\) 1.8 V = 1.8 mW for such ratios. We perform simulations for three scenarios of energy requirements, namely the low, medium, and high power requirement scenarios. For the low power requirement scenario, it is assumed 80% of sensor nodes are of 1.8 mW power requirement, 10%, 5.4 (=1.8 \(\times\) 3) mW and 10%, 9.0 (=1.8 \(\times\) 5) mW. For the medium energy requirement scenario, it is assumed 10% of sensor nodes are of 1.8 mW  
Figure 20: The comparisons of GCC and ACC in terms of the number of deployed chargers for: (a) 1-coverage; (b) 2-coverage; and (c) 3-coverage requirement. power requirement, 80%, 5.4 mW and 10%, 9.0 mW. And for the high power requirement scenario, it is assumed 10% of sensor nodes are of 1.8 mW power requirement, 10%, 5.4 mW and 80%, 9.0 mW. In the candidate cone generation phase, the GCC and the ACC algorithms generate the same number of candidate cones no matter what energy requirements (coverage requirements or power requirements) are considered. Please refer to Figure 19 for the comparisons of GCC and ACC in terms of the number of candidate cones. Figure 22 shows the comparisons of GCC and ACC in terms of the number of the chargers deployed for different power requirement scenarios. We can observe that ACC outperforms GCC for almost all scenarios. The superiority of ACC is most significant in the high power requirement scenario, while is least significant in the low power requirement scenario. This is because GCC greedily adjust directions of cones to the extreme for some sensor node-pair cases that nodes can cover by one cone. Consequently, some sensors are covered by chargers with larger horizontal and vertical angles. Figure 21: The comparisons of GCC and ACC in terms of the execution time for: (**a**) 1-coverage; (**b**) 2-coverage; and (**c**) 3-coverage requirements. Hence, those sensor nodes may need to be covered by more cones (i.e., chargers) to make their power requirements satisfied. On the contrary, ACC greedily and adaptively adjust the direction of every cone to cover as many as possible sensor nodes by vector composition. Consequently, most sensors are covered by chargers with smaller horizontal and vertical angles. Hence, sensor nodes may need to be covered by fewer cones (i.e., chargers) to make their power requirements satisfied. The explanation above also accounts for the reason why the superiority of ACC is more significant for scenarios of higher power requirements. Figure 23 shows the comparisons of GCC and ACC in terms of execution time for scenarios of different power requirements. We can observe that ACC runs much faster than GCC, especially for the scenario of high energy consumption. This is because ACC has \(O(m^{2}n^{3})\) time complexity, while GCC has \(O(m^{2}n^{5})\) time complexity.