NDLI: Cognitively Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks

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Cognitively Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks

Content Provider	Springer Nature Link
Author	Kim, Sung Soo McLoone, Sean Byeon, Ji Hwan Lee, Seokcheon Liu, Hongbo
Copyright Year	2017
Abstract	The swarm cognitive behavior of bees readily translates to swarm intelligence with “social cognition,” thus giving rise to the rapid promotion of survival skills and resource allocation. This paper presents a novel cognitively inspired artificial bee colony clustering (ABCC) algorithm with a clustering evaluation model to manage the energy consumption in cognitive wireless sensor networks (CWSNs). The ABCC algorithm can optimally align with the dynamics of the sensor nodes and cluster heads in CWSNs. These sensor nodes and cluster heads adapt to topological changes in the network graph over time. One of the major challenges with employing CWSNs is to maximize the lifetime of the networks. The ABCC algorithm is able to reduce and balance the energy consumption of nodes across the networks. Artificial bee colony (ABC) optimization is attractive for this application as the cognitive behaviors of artificial bees match perfectly with the intrinsic dynamics in cognitive wireless sensor networks. Additionally, it employs fewer control parameters compared to other heuristic algorithms, making identification of optimal parameter settings easier. Simulation results illustrate that the ABCC algorithm outperforms particle swarm optimisation (PSO), group search optimization (GSO), low-energy adaptive clustering hierarchy (LEACH), LEACH-centralized (LEACH-C), and hybrid energy-efficient distributed clustering (HEED) for energy management in CWSNs. Our proposed algorithm is increasingly superior to these other approaches as the number of nodes in the network grows.
Starting Page	207
Ending Page	224
Page Count	18
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	9
Issue Number	2
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2017-01-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cognitively inspired algorithm Artificial Bee Colony (ABC) Cognitive Wireless Sensor Network (CWSN) Clustering Clustering evaluation model Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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