# Efficient Wireless Charger Deployment for Wireless Reshapeable Sensor Networks
## 6 Experiments and Simulations
### Simulations  
This subsection shows the simulation results of the proposed GCC algorithm and ACC algorithm. The simulator is implemented in C++ language, and the simulation parameters are shown in Table 3. The sensors are distributed randomly in a 20 m \(\times\) 15 m \(\times\) 2.3 m cuboid. The chargers can be deployed at grid points on the grid plane at the height of 2.3 m, and the grid point separation is 1.8 m. The charging distance is 3 m, and the charging cone opening angle is 30\({}^{\circ}\). It is noted that 30 experiments were executed per simulation case for averaging the simulation results. The parameter settings are shown in Table 3.  
Figure 17: The functions derived by performing power regression analysis on experimental data (ED) of the power received (PR) for horizontal angel \(\Phi\) = 0\({}^{\circ}\), 15\({}^{\circ}\) and 30\({}^{\circ}\), and vertical angle \(\theta\) = 0\({}^{\circ}\). Figure 18: The functions derived by performing power regression analysis on ED of the PR for horizontal angel \(\Phi\) = 0\({}^{\circ}\), and vertical angle \(\theta\) = 0\({}^{\circ}\), 15\({}^{\circ}\) and 30\({}^{\circ}\). We first perform simulations for three scenarios of coverage requirements: all sensor nodes require 1-, 2-, or 3-coverage of chargers. Figure 19 shows the comparisons of GCC and ACC algorithms in terms of the number of the candidate cones generated. The simulation results are all the same for the 1-, 2- and 3-coverage requirement scenarios, so only one diagram is shown in Figure 19. We can observe that GCC generates much more candidate cones than ACC. This is because GCC has \(O(mn^{2})\) of candidate cones, while ACC has \(O(nm)\) of candidate cones, where \(n\) is the number of sensor nodes and \(m\) is the number of grid points. Figure 20 shows the comparisons of GCC and ACC in terms of the number of the chargers deployed for the 1-, 2- and 3-coverage requirement scenarios. By Figure 20, we can observe that GCC outperforms ACC in terms of the number of chargers required. This is because GCC greedily adjusts charger antenna directions to the extreme for some cases to cover more sensor nodes by one candidate cone, and GCC generates more candidate cones to be selected. On the contrary, ACC adaptively adjusts antenna directions to cover sensor nodes with smaller horizontal angels and vertical angels, and ACC generates fewer candidate cones. Figure 21 shows the comparisons of GCC and ACC in terms of execution time for scenarios of different coverage 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. However, the time complexity of the big \(O\) notation is just an upper bound. Some experimental cases may have sensor nodes whose coverage requirements are met very early. For such cases, the algorithm execution may not be too long. This accounts for why the execution times of algorithms do not grow according to the functions of \(m^{2}n^{3}\) or \(m^{2}n^{5}\). We now consider the form of power requirements, in which every sensor node present its power in the unit of mW for compensating its power consumption. For setting simulation parameters, we derive the sensor node power consumption from the specification of TI eZ430 (Texas Instruments Inc., Dallas, TX, USA) [27], which a common off-the-shelf sensor node device. The working voltage of eZ430  
Figure 19: The comparisons of GCC and ACC in terms of the number of generated candidate cones.