# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 10 Conclusions  
In massive IoT networks, large numbers of devices are connected for various applications, and hence, the power consumption of these devices is a crucial factor in determining sustainability and cost-effectiveness. The power consumption of such devices depends on two main factors:  
1. Types of devices: power-hungry applications and the type of sensors, communication, and processing power consumption;
2. Communication frequency and data rate: a device will consume more power with frequent communication at a high data rate  
Hence, to minimize power in massive IoT networks, energy-efficient hardware and power-saving strategies must be applied to low-power communicating protocols. To decrease the energy consumption in devices with limited battery power, network providers are seeking environmentally friendly energy solutions to fulfill the energy needs of these networks. This paper presented an optimization technique along with a user-scheduling algorithm that allocates optimal power to each user and increases the network performance. A weight-based user-scheduling algorithm was proposed in which equal weights were  
Figure 13: Spectral efficiency with respect to \(SIR_{T}\) for various user-scheduling algorithms. assigned to the distance and received signal strength to select a set of users to be served by each AP. A perfect CSI was assumed for downlink transmission, and the performance of the proposed scheduling algorithm was evaluated with a modified genetic algorithm for optimal power allocation. The performance was measured in terms of the power consumption and spectral efficiency. The results showed the improvement in power consumption with the use of the genetic approach, which helped in the optimal assignment of powers to each users, so that the received SIR was always greater than \(SIR_{T}\). The current research on the proposed weighted user-scheduling algorithm considered an equal weight; however, further improvements can be made and the performance can be evaluated under various weights. Moreover, the assignment of a proper weight helps in the enhancement of the performance metrics. Conceptualization and methodology: A.G., N.B.A., R.K.M., S.S., and F.Q. ; formal analysis: A.G., N.B.A., R.K.M., S.S., and F.Q. ; software, validation, and writing: A.G., N.B.A., F.Q., and Q.N.N. ; writing--review and editing: A.G., N.B.A., F.Q., S.H.A.K., and Q.N.N. ; data curation: A.G., N.B.A., R.K.M., and S.S.; supervision: A.G., R.K.M., S.S., and N.B.A. funding acquisition: A.G., F.Q., and Q.N.N. All authors have read and agreed to the published version of the manuscript. This paper was supported by the Universiti Kebangsaan Malaysia Fundamental Research Grant Scheme (FRGS) from the Ministry of Higher Education with the code: FRGS/1/2022/ICT11/UKM/02/1. This work was also supported by Universiti Kebangsaan Malaysia Gearan Galakan Penyelidik Muda (GGPM) with the code: GGPM-2021-040. We would like to thank editors, reviewers and supervisors for their support. The authors declare that they have no conflicts of interest to report regarding the present study. The following abbreviations are used in this manuscript: \begin{tabular}{l l} IoT & Internet of Things \\ D2D & Device-to-device \\ BS & Base station \\ RAT & Radio access technology \\ SIC & Successive interference cancellation \\ EE & Energy efficiency \\ SE & Spectrum efficiency \\ SBS & Small cell base station \\ POLITE & Power optimization for low interference and throughput enhancement \\ BLA & Baseline link adaptation \\ MDS & Minimum distance scheduling \\ MCS & Maximum channel gain scheduling \\ CR & Cooperative relay \\ CSI & Channel state information \\ SER & Symbol error rate \\ PSO & Particle swarm optimization \\ GA & Genetic algorithm \\ LTE & Long-Term Evolution \\ MIMO & Multiple-input, multiple-output \\ SIR & Signal-to-interference ratio \\ CU & Cellular user \\ RUI & Relay-to-user interference \\ CCI & Cellular-to-cellular user interference \\ DDI & Device-to-device interference \\ CDI & Cellular-to-device interference \\ \end{tabular}