IoT' de kullanıcı isteğine dayalı akıllı veri toplama

Ensuring effective, efficient, and fast data communication in IoT networks is a critical challenge, given the resource limitations of nodes within these networks. Data aggregation emerges as a promising solution to address this challenge by consolidating data from multiple sources before transmission, thereby reducing memory consumption, enhancing energy efficiency, and improving transmission speed. This study proposes an approach to examine the impact of different data aggregation methods and transmission frequencies on memory usage in resource-limited sensor nodes, considering these client requests. Specifically, we will investigate how memory consumption changes when data is transmitted to the sink node at various frequencies using different aggregation techniques. Additionally, memory usage is measured when the aggregation method and transmission frequency are dynamically determined, and aggregated data is transmitted to the requested server or service. In the proposed system for user-request driven smart data aggregation in IoT networks, the data flow follows a structured process to efficiently handle client requests while optimizing resource usage. Based on these requests, sensors will aggregate the data and transmit it to the sink node, the client system, or the server at the specified frequency. By comparing the measured memory usage values, we aim to identify the aggregation method and transmission frequency that result in the least memory usage. These optimal values are expected to enhance energy efficiency and memory usage efficiency in the IoT network, consequently prolonging its lifespan. The proposed data flow ensures that client requests are efficiently handled while minimizing resource consumption in the IoT network. In summary, this study aims to contribute insights into optimizing memory usage in IoT networks through efficient data aggregation and transmission strategies, considering client requests.