An IoT and machine learning‐based routing protocol for reconfigurable engineering application.

With new telecommunications engineering applications, the cognitive radio (CR) network‐based internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR‐IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross‐layer routing protocol based on CR‐IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine‐learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR‐IoT. [ABSTRACT FROM AUTHOR]