Energy Optimization in Spectrum Sensing Using Cognitive Radio Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have gained widespread adoption across various applications due to their capacity to collect and wirelessly transmit data. Cognitive Radio (CR) technology presents a promising solution for enhancing spectrum utilization efficiency within WSNs. The fusion of CR with WSNs results in Cognitive Radio Wireless Sensor Networks (CR-WSNs), capable of dynamically selecting optimal spectrum bands for seamless data transmission. However, the operation of CR-WSNs comes with notable energy consumption, and the efficient use of energy is pivotal for prolonging the network's lifespan. This study introduces an energy optimization technique specifically tailored for CR-WSNs, focusing on the crucial aspect of spectrum sensing. The proposed technique deploys a two-stage sensing strategy involving a primary sensor and a secondary sensor. The primary sensor identifies the presence of primary users in the spectrum band, activating the secondary sensor only when a primary user is detected. The secondary sensor conducts more detailed sensing to ascertain the available bandwidth for subsequent data transmission. Machine learning algorithms are employed in the proposed technique to finely tune the energy consumption of the CR-WSN during the spectrum sensing process. Through simulation assessments, the effectiveness of the proposed technique is gauged, revealing significant reductions in the energy consumption of the CR-WSN while upholding a high level of accuracy in spectrum sensing. This technique offers practical applicability in real-world CR-WSN scenarios, contributing to the extension of the network's operational life and enhancement of overall energy efficiency.