Samenvatting

This research investigates power optimization in Thread mesh wireless networks through an algorithmic approach, aiming to reduce overall power consumption while maintaining reliable network performance. Transmission power serves as a key parameter for achieving energy efficiency, and the study focuses on two algorithmic approaches: the Monte Carlo Method (MCM) and the Genetic Algorithm (GA). The research involves determining the optimal network configuration and transmission power constraints, selecting appropriate hardware, building the network, and developing the algorithms. Data is collected and analyzed from various network modes and devices across two locations, including lab and home environments, to ensure diverse and representative results. MCM emphasizes optimal network configuration alongside initial transmission power, while GA targets optimal transmission power settings. The findings indicate that both MCM and GA outperform the maximum method in power optimization, with GA offering the best results. By effectively minimizing energy usage, GA ensures network performance is not compromised. The research emphasizes the importance of sustainability by promoting energy-efficient solutions that minimize environmental impact. The project’s focus on energy efficiency and reduced power consumption makes it environmentally friendly and sustainable, contributing to reduced energy waste and lowering the carbon footprint associated with Internet of Things (IoT) networks. Additionally, the research process involves the application and development of professional skills, such as data analysis, algorithm design, and critical thinking, to ensure the reliability and relevance of the results. While the ethical aspects of the research may not be directly evident, the focus on sustainability and responsible technological development inherently involves ethical considerations, such as resource conservation and minimizing negative impacts on society and the environment. The findings contribute to the development of energy-efficient IoT networks and serve as a foundation for further exploration into power optimization techniques, encouraging the expansion of sustainable IoT ecosystems.