Interpolation-Based IoT Sensors Selection

Data traffic and network congestion remain some of the main challenges in Internet of Things (IoT) systems. In this article, we propose an innovative numerical method aimed at reducing the number of required sensors while maintaining data quality and integrity. The method proposed is an optimization technique based on a variant of the step method coupled with the interpolation technique. It allows the resizing of the existing IoT architecture by removing the latent redundancy within the system. By optimizing resources, our method addresses the critical issues of data traffic and energy efficiency in IoT networks. The algorithm intelligently reduces the number of sensors needed without compromising the accuracy and reliability of the collected data. Hence, it mitigates the adverse effects of network congestion and excessive energy demands. After determining the new IoT architecture, we tested the network on unseen data. The results indicate that the relative error follows a consistent trend observed in the training set, further demonstrating the robustness of the method. This optimization ensures efficient and effective IoT operations, ultimately contributing to the development of more sustainable and scalable IoT infrastructures. Through this method, we provide a viable solution to some of the most pressing challenges faced by modern IoT networks.