The integration of wireless sensor network (WSN) and cognitive radio (CR) technology enables a new paradigm of communication: cognitive radio sensor networks (CRSN). The existing WSN clustering algorithm cannot consider the advantage of channel resource brought by CR function in CRSN, and the CR network (CRN) clustering algorithm is designed based on the infinite energy nodes; thus both algorithms cannot operate with energy efficiency in CRSN. The paper proposes a low-energy adaptive uneven clustering hierarchy for CRSN, which can not only consider the advantage of the channel resource in reducing the energy consumption but also employ uneven clustering method for balancing the energy consumption among the cluster heads under multiple hops transmission means. Simulation results show that compared with the existing several typical clustering algorithms including WSN and CRSN clustering algorithms, low-energy adaptive clustering hierarchy (LEACH), HEED, energy-efficient unequal clustering (EEUC), cognitive LEACH (CogLEACH), and distributed spectrum-aware clustering (DSAC), the proposed algorithm can not only efficiently balance the energy consumption among cluster heads and network load in CRSN but also remarkably prolong the network lifetime.

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In order to cope with the resource shortage problem brought by cognitive radio technology in cognitive radio sensor network (CRSN), a new CRSN called heterogeneous CRSN (HCRSN) is proposed, where cognitive nodes (CNs) and sensor nodes (SNs) are separated and undertake different functions. Different from the existing clustering algorithms for homogeneous nodes based WSN, the clustering algorithm for HCRSN needs to consider the distribution of CNs among clusters such that enough high channel detection probability of each cluster can be guaranteed by the lowest deployment cost. Therefore, this paper first proposes a heterogeneous nodes based low energy adaptive clustering hierarchy (HLEACH) algorithm. In the algorithm, the sink node first updates the global information including the optimal number of clusters and average cluster radius and then broadcast it. Each CN calculates its competition radius after receiving the broadcasting information, and then start the competition for CHs based on the proposed competition rules. The elected CHs are finally censored targeting the optimal number of clusters to optimize the distribution of final CHs. In clusters' formation stage, non-CH CNs and SNs synthetically consider the distance and the connection degree of CHs such that the distribution of CNs among clusters and the energy consumption among CHs can be energy-efficiently balanced. The simulation results show that the proposed algorithm can not only effectively balance the distribution of CNs among clusters, guaranteeing enough high channel detection probability of each cluster and network energy utilization, but also balance the energy consumption among CHs, eventually prolong the network lifetime. Finally, the optimal deployment proportion of numbers and initial energy of the two types of nodes is also theoretical derived to maximize the energy utilization efficiency (i.e. the ratio of the network lifetime to the deployment cost).

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