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Home»Articles»A Comparative Study of Swarm Intelligence-Based Optimization Algorithms in WSN

A Comparative Study of Swarm Intelligence-Based Optimization Algorithms in WSN

Author : Aya Ayad Hussein and Rajaaaldeen Abd Khalid
Volume 8 No.3 July-December 2019 pp 1-7

Abstract

In the last decades, WSN gets all the attention in research and applications especially in science and engineering fields due to it is great benefits which been introduced. These networks are used in rough and inaccessible environments such as battlefields, volcanoes, and forests, so basically there is a low chance to recharge or change the low battery or dead nodes. Hence, WSNs are hypersensitive and vulnerable to energy more than other classic wireless networks. Therefore, this led to emerging improvements rapidly in WSN to reach the goal of achieving the network requirements and satisfying the user needs at the same time to get the best results. Artificial Intelligent systems Toke the biggest share of the WSN development process. So, In this paper we will focus on an important section in an intelligent system based on optimization algorithms, is the swarm intelligence that depends on the real behavior of animals and insects colonies, it is a system that based on many individuals that working within a group as a team and coordinate their behavior using decentralized control and self- organization. SI-based optimization algorithms have a great and affirmative influence on WSN represented by minimize delays in data transmission between nodes in the network, network balancing and avoiding network traffic and overhead, save energy, and maximize the network lifetime. The algorithms that will be covered in this paper are ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC) by going deeply in describing the criteria of their work and analyses their models.

Keywords

WSN, SI-Based Optimization Algorithms, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC)

Full Text:

References

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[2] S. Misra, and S. Goswami, “Network Routing: Fundamentals, Applications, and Emerging Technologies”, 1st ed, John Wiley & Sons Ltd., Ch.11, pp. 285-325, 2017.
[3] S. K. Singh, M. P. Singh, and D. K. Singh, “Routing Protocols in Wireless Sensor Networks – A Survey”, International Journal of Computer Science & Engineering Survey (IJCSES), Vol. 1, No. 2, pp. 63-83,2010.
[4] M. Dorigo, “Optimization, learning and natural algorithms”, Ph.D. Thesis, Politecnico di Milano, Milan.1992. [Online] Available: http://ci.nii.ac.jp/naid/10016599043/
[5] M. Dorigo, M.Birattari, and T. Stutzle, “Ant Colony Optimization. Computational Intelligence Magazine”, IEEE, pp. 28-39,2006
[6] Y. Pei, W. Wang, and S. Zhang, “Basic Ant Colony Optimization”. International Conference on Computer Science and Electronics Engineering, pp. 665-667, 2012.
[7] J. Kennedy, and R.Eberhart, “Particle swarm optimization”. IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
[8] Y. Del Valle, G. K. Venayagamoorthy, S.Mohagheghi, J. C. Hernandez, and R. G. Harley, “Particle Swarm Optimization: Basic Concepts, Variants, and Applications in Power System”, IEEE Trans Evolutionary Computer, pp. 171-195, 2008.
[9] A. Banks, J. Vincent, and C. Anyakoha, “A Review of Particle Swarm Optimization. Part I: Background andDevelopment”, Springer Science, pp. 467-484, 2007.
[10] S. Jiang, “LEACH Protocol Analysis and Optimization of Wireless Sensor Networks Based on PSO and AC”, IEEE, Proceedings of 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, 978-1-5386-5836-9/18, pp. 246-250, 2018.
[11] D. Karaboga, “An idea based on honeybee swarm for numeric optimization”, Technical Report TR06, Erciyes University, 2005.
[12] V. Selvi, and R. Umarani, “Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques”, International Journal of Computer Applications, pp. 1-6, 2010.
[13] B. Yang, Q. Ma, and J. Wang, “Study on the Routing Technology of Wireless Sensor Network Based on Ant Colony Optimization”, Journal of Sensor Technology, Vol. 6, pp. 141-158, 2016.
[14] A. Nayyar, “A Comprehensive Review of Ant Colony Optimization (ACO) based Energy-Efficient Routing Protocols for Wireless Sensor Networks”, International Journal of Wireless Networks and Broadband Technologies,Vol. 3, No. 3, pp. 33-55, 2014.
[15] Q. Bai, “Analysis of Particle Swarm Optimization Algorithm”, Computer and Information Science, pp.180-184, 2010
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[18] F. S. Abu-Mouti, and M. E. El-Hawary, “Overview of Artificial Bee Colony (ABC) algorithm and its applications”, International Systems Conference- SysCon, pp. 1-6, 2012.
[19] E. Gerhardt, and H. M. Gomes, “Artificial Bee Colony (ABC) Algorithm for Engineering Optimization Problems”, International Conference on Engineering Optimization, pp.1-11, 2012.
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In the last decades, WSN gets all the attention in research and applications especially in science and engineering fields due to it is great benefits which been introduced. These networks are used in rough and inaccessible environments such as battlefields, volcanoes, and forests, so basically there is a low chance to recharge or change the low battery or dead nodes. Hence, WSNs are hypersensitive and vulnerable to energy more than other classic wireless networks. Therefore, this led to emerging improvements rapidly in WSN to reach the goal of achieving the network requirements and satisfying the user needs at the same time to get the best results. Artificial Intelligent systems Toke the biggest share of the WSN development process. So, In this paper we will focus on an important section in an intelligent system based on optimization algorithms, is the swarm intelligence that depends on the real behavior of animals and insects colonies, it is a system that based on many individuals that working within a group as a team and coordinate their behavior using decentralized control and self- organization. SI-based optimization algorithms have a great and affirmative influence on WSN represented by minimize delays in data transmission between nodes in the network, network balancing and avoiding network traffic and overhead, save energy, and maximize the network lifetime. The algorithms that will be covered in this paper are ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC) by going deeply in describing the criteria of their work and analyses their models.

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Department of Environmental Toxicology, Texas Tech University, Texas
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Department of Electrical Engineering, University of Malaya, Malaysia
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Department of Mechanical Engineering, University of Akron, Akron, USA
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Dr. Mohd Faiz Bin Mohd Salleh
Department of Electrical Engineering, University of Malaya, Malaysia
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Dr. Suhana Binti Mohd Said
Department of Electrical Engineering, University of Malaya, Malaysia
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Department of Electrical Engineering, University of Malaya, Malaysia
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    Editorial Note

    Editorial Dr. Seshadri Ramkumar

    Editor-in-Chief
    Dr. Seshadri Ramkumar
    Department of Environmental Toxicology, Texas Tech University, Texas
    [email protected]
    Editorial Advisory Board
    Dr. Kamarul Ariffin Bin Noordin
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Benjamin T.F. Chung
    Department of Mechanical Engineering, University of Akron, Akron, USA
    [email protected]
    Dr. Mohd Faiz Bin Mohd Salleh
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Suhana Binti Mohd Said
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Norrima Binti Mokhtar
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Mohamadariff
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]

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