For time and space constraints, 5G base stations will have more serious energy consumption problems in some time periods, so it needs corresponding sleep strategies to reduce energy consumption. Based on the analysis of 5G super dense base station network structure, through the analysis of current situation and user demand, a cluster sleep method based on genetic algorithm is constructed under the support of genetic algorithm, which can realize the dynamic matching of energy consumption in time domain and space, and the low load base station enters the sleep state. In order to verify the performance of the algorithm, the simulation network structure is built on the MATLAB platform, and the advantages of the algorithm in this study are obtained through comparative analysis, and the relevant test parameters are set for the technical performance analysis of this study. The research shows that the method proposed in this paper has a certain energy-saving effect, can meet the energy efficiency requirements of 5G ultra dense base station, and in the ultra dense base station group, the complexity can also meet the system operation requirements, which has a certain degree of practicality, and can provide reference for the follow-up related research.

In the current WSN operation process, there are two major problems of data collection difficulty and network energy consumption, which seriously affects the reliability of the WSN. In this paper, the improved ant colony algorithm proposed is compared with other algorithms. Wireless sensor network nodes based on improved ant colony algorithm have lower energy consumption, and sensor nodes have more residual energy. In addition, the energy model, data transmission balance model is established and verified in the WSN transmission target function. The experimental results show that the WSN node after the improved ant colony algorithm is used to help in the determination of the location information of the public node, and then use the location information of the node to make the protocol have effective routing performance and effective target node location discrimination ability. Thus, the improved ant colony algorithm studied in this paper has important practical significance for improving the life cycle/energy consumption of wireless sensor networks. In addition, aiming at the characteristics and routing performance of wireless sensor networks, a low-power routing method based on location and direction is designed to make the message reach the target node accurately and safely, which effectively increases the data packet transmission rate.

In recent years, several factors such as environmental pollution, declining fossil fuel supplies, and product price volatility have led to most countries investing in renewable energy sources. In particular, the development of photovoltaic (PV) microgrids, which can be standalone, off-grid connected or grid-connected, is seen as one of the most viable solutions that could help developing countries such as Rwanda to minimize problems related to energy shortage. The country’s current electrification rate is estimated to be 59.7%, and hydropower remains Rwanda’s primary source of energy (with over 43.8% of its total energy supplies) despite advances in solar technology. In order to provide affordable electricity to low-income households, the government of Rwanda has pledged to achieve 48% of its overal electrification goals from off-grid solar systems by 2024. In this paper, we develop a cost-effective power generation model for a solar PV system to power households in rural areas in Rwanda at a reduced cost. A performance comparison between a single household and a microgrid PV system is conducted by developing efficient and low-cost off-grid PV systems. The battery model for these two systems is 1.6 kWh daily load with 0.30 kW peak load for a single household and 193.05 kWh/day with 20.64 kW peak load for an off-grid PV microgrid. The hybrid optimization model for electric renewable (HOMER) software is used to determine the system size and its life cycle cost including the levelized cost of energy (LCOE) and net present cost (NPC) for each of these power generation models. The analysis shows that the optimal system’s NPC, LCOE, electricity production, and operating cost are estimated to 1,166,898.0 USD, 1.28 (USD/kWh), 221, and 715.0 (kWh per year, 37,965.91 (USD per year), respectively, for microgrid and 9284.4(USD), 1.23 (USD/kWh), and 2426.0 (kWh per year, 428.08 (USD per year), respectively, for a single household (standalone). The LCOE of a standalone PV system of an independent household was found to be cost-effective compared with a microgrid PV system that supplies electricity to a rural community in Rwanda.