Parallel cuckoo search for cognitive wireless sensor networks

In cognitive wireless sensor networks (CWSNs), the limited energy of the sensor node is the core defect that restricts its comprehensive network performance. This paper proposes a parallel cuckoo search medoids (PCS-medoids) algorithm to manage the energy consumption in CWSNs efficiently. Firstly, a parallel cuckoo search algorithm (PCS) with communication is proposed to speed up the convergence of CS. Then, the PCS is applied to k-medoids to get cluster heads quickly. Finally, the PCS-medoids is presented to manage the consumption of sensor nodes. First experimental results illustrate that PCS tends to get optimal solutions quickly and accurately compared to CS and PSO. The other experimental results demonstrate that PCS-medoids has advantages over energy management in CWSNs compared to low-energy adaptive clustering hierarchy, LEACH-centralised, and hybrid energy-efficient distributed clustering. Besides, the ad-vantages are more obvious with the increase of sensor nodes in CWSNs.