Machine Learning-Based Spectrum Decision Algorithms for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) employ Industrial, Scientific and Medical (ISM) spectrum bands for communication, which are overloaded due to various technologies such as WLANs and other WSNs. Therefore, such networks must employ intelligent methods such as Cognitive Radio (CR) to coexist with other networks. This study investigates the use of supervised Machine Learning (ML) for channel selection in WSNs. The proposed models were analyzed using ML tools and techniques, and the best algorithms were evaluated on real sensor nodes. The experiments show performance improvements on the delivery rate and delivery delay when the proposed cognitive solutions are employed.