Cognitively-Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks

Sung-Soo Kim, Sean McLoone, Ji-Hwan Byeon, Seokcheon Lee, Hongbo Liu

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)
304 Downloads (Pure)


The swarm cognitive behavior of bees readily translates to swarmintelligence with “social cognition”, thus giving rise to the rapid promotion ofsurvival skills and resource allocation. This paper presents a novel cognitivelyinspiredartificial bee colony clustering (ABCC) algorithm with a clusteringevaluation model to manage the energy consumption in cognitive wireless sensornetworks (CWSNs). The ABCC algorithm can optimally align with thedynamics of the sensor nodes and cluster heads in CWSNs. These sensornodes and cluster heads adapt to topological changes in the network graphover time. One of the major challenges with employing CWSNs is to maximizethe lifetime of the networks. The ABCC algorithm is able to reduceand balance the energy consumption of nodes across the networks. ArtificialBee Colony (ABC) optimization is attractive for this application as the cognitivebehaviors of artificial bees match perfectly with the intrinsic dynamics in cognitive wireless sensor networks. Additionally, it employs fewer controlparameters compared to other heuristic algorithms, making identification ofoptimal parameter settings easier. Simulation results illustrate that the ABCCalgorithm outperforms PSO (particle swarm optimisation), GSO (group searchoptimisation), LEACH (low-energy adaptive clustering hierarchy), LEACH-C(LEACH-centralized), and HEED (hybrid energy-efficient distributed clustering)for energy management in CWSNs. Our proposed algorithm is increasinglysuperior to these other approaches as the number of nodes in the networkgrows.
Original languageEnglish
Pages (from-to)207-224
JournalCognitive Computation
Issue number2
Early online date17 Jan 2017
Publication statusPublished - Apr 2017


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