Abstract: The explosive growth of wireless sensor networks have increased demand for radio spectrum which have resulted in spectrum scarcity problem, as the entire spectrum bands are already been allocated to different wireless services and technologies. Cognitive radio has emerged as a promising solution for the inherently resource-constrained wireless sensor networks to opportunistically access the reserved underutilized frequency bands. It adopts artificial intelligence algorithms to enable sensor nodes to avoid crowded unlicensed congested bands by sensing underutilized licensed frequency bands and make decision to adapt their transmission parameters. Clustering is used to decrease energy consumption and support scalability of sensor networks, however current clustering is based on fixed spectrum allocation and cannot deal with the dynamic spectrum allocations needed for next generation networks. This paper proposes an energy efficient spectrum aware clustering that groups neighboring nodes with similar sets of idle channels and optimally form energy efficient clusters based on three fuzzy parameters of residual energy, closeness to base station, and node degree to compute the chance of each node to be a cluster head. The performance is evaluated using data packets sent to base station, number of nodes alive during rounds, number of dead nodes during rounds, and energy consumption as performance metrics. Comparative performance evaluation in different simulation scenarios show that the proposed protocol have outperformed other implemented protocols and prolonged the stability period and the networks’ lifetime ¬due to reduction in packet collision and retransmission.