Search for a command to run...
The rapid growth of IoT has created an emerging demand for scalable and energy-efficient communication systems that can support millions of devices connected to the Internet. Essentially, LPWANs are meant for low-power, low-throughput networking, and they definitely are useful in enabling devices to connect to the internet. All in all, LPWAN is the future! It is very difficult to enhance the performance of LPWANs because of trade-offs between the important factors like energy efficiency, latency, throughput and reliability. This research puts forward a cross-layer optimization model which aims to enhance the IoT communication in LPWAN systems. The three proposed strategies are complementary. First, there is the adaptive spreading factor (SF) allocation to maximize physical layer performance. Second, the traffic scheduling based on reinforcement learning maximizes medium access control efficiency. Lastly, energy harvesting mechanisms increase lifetime of devices. When used in tandem, the modules respond dynamically to different network conditions, saving energy while improving the reliability of the transmitted data. According to the results of the simulations, the proposed framework can greatly outperform standard static schemes. According to relevant metrics the results attain a significant gain in energy efficiency and capacity of mobile networks compared to conventional operation. Above all, optimum energy efficiency and capacity is achieved with a distance of 9metres either side. We can say, integrating adaptive and intelligent systems is useful to optimize the IoT networks based on LPWAN. The model that is being suggested is a sustainable solution with scalability that will help to enhance the efficiency, reliability and sustainability of the next-generation IoT systems.