# Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices
## 4 Simulations and Experiments  
To evaluate the proposed scheme, this section provides the simulation results of the received SNRs and the complexity. Furthermore, to verify whether the proposed algorithm is applicable in practical sensors [62], the execution time and the current dissipation in an MCU are investigated. Here, note that the MCU performance experiment is conducted for the feasibility of our algorithm in a practical embedded environment. The wireless link performance is only verified by MATLAB simulations. As illustrated in Figures 7 and 8, and Table 2, an experiment setup is composed of a STM32F4-Discovery, an Universal Asynchronous Receiver Transmitter (UART) module, and an Atmel power debugger. The STM32F4-Discovery is a MCU with a 32-bit ARM Cortex-M4 and an FPU core, 1 Mbbyte of flash memory, and 192 Kbytes of RAM. In addition, the UART module and the Atmel power debugger are exploited to forward environmental parameters of the WSN, being generated by MATLAB, to the MCU and to measure the current consumption of the MCU, respectively. Prior to the detailed studies, the assumptions of the system and algorithm are given as follows. Twelve sensors (\(M=12\)) are uniformly distributed over a disk of radius \(\tilde{R}=1\lambda\), and the beams aiming at \(\varphi_{0}=0\) are selected. \(P_{m}\) is equally set as \(\sigma_{n}^{2}\eta/M\), where the power budget \(\eta\) is equal to 20 dB, and \(\sigma_{n}^{2}\) is set to 0.2. Finally, the parameter of \(\rho\) is fixed to \(10^{5}\). As shown in Figure 6b, setting \(\rho>10^{5}\) can increase the possibility of approaching the global optimum. However, this behavior would lead to excessive power consumption and overhead overwhelming the SNR improvement. Figures 9 and 10 illustrate the simulated results with respect to the received SNRs and the number of the iterations. In Figure 9, it is observed that our algorithm provides a significant SNR improvement (e.g., when \(\zeta=50\) dB and \(\
u=16\) dB, 8.1 dB, 11.4 dB, and 14.1 dB at \(N_{t}=3\), \(N_{t}=6\), and \(N_{t}=12\), respectively). Additionally, as seen in Figure 10, the lower \(\zeta\) and \(\
u\) are set, the easier it is for the algorithm to increase the SNR within the relative smaller iterations. Interestingly, this phenomenon is also observed when \(N_{t}\) is higher, due to the fact that the power concentration by the directional beams surpasses the possibility of the absence of sensors in the CB. In addition, Figure 11 verifies that the proposed algorithm  
Figure 8: Experiment configuration. The Amtel power debugger and the UART modules are used to measure the operating cycle and current dissipation in the MCU, respectively. Figure 7: Operating interaction between MCU and MATLAB for emulating the proposed algorithm. requires the following execution time (=operating cycles/MCU clock (\(\mathfrak{M}\))) between 0.2 s and 7.2 s. From Figure 12a, it is also confirmed that the supplied current into the MCU is marginally increased (averagely, 11.23%) even when the proposed algorithm is executed under the worst conditions of \(\zeta=50\) dB, \(\
u=16\) dB, and \(N_{t}=3\). Finally, Figure 12b shows that as \(\mathfrak{M}\) is increased, the dissipated current is conversely decreased. This tendency occurs because the reduction in the execution time is more dominant than the increase in the supplied current. Considering the above results, it is concluded that the proper combinations of \(N_{t}\) and \(\mathfrak{M}\) result in the acceptable execution time and current dissipation even when the high values of \(\zeta\) and \(\
u\) are configured (e.g., when \(N_{t}=12\), \(\zeta=46\) dB, \(\
u=16\) dB, and \(\mathfrak{M}=168\) MHz are set, the execution time and the dissipated current is 0.26 s and 0.023\([A\times s]\), respectively). Figure 9: The simulated SNRs of the proposed algorithm versus \(\zeta\) when \(\
u\) is set as (**a**) 2 dB, (**b**) 4 dB, (**c**) 8 dB, and (**d**) 16 dB, respectively.