Energy-Aware Clustering in the Internet of Things by Using the Genetic Algorithm

Authors

  • Mohammad Esmaeili Department of Computer Engineering, Science and Research Branch of Islamic Azad University, Ardabil, Iran.
  • Shahram Jamali Department of Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
  • Hamed Shahbazi Fard Computer Networks Research Lab, Electrical Engineering Technologies Research Center, Sahand University of Technology, Sahand New Town, Tabriz, Iran.

Keywords:

Clustering, Energy-Aware, Genetic Algorithm, Internet of Things,

Abstract

Internet of things (IoT) uses a lot of key technologies to collect different types of data around the world to make an intelligent and integrated whole. This concept can be as simple as a connection between a smartphone and a smart TV, or can be complex communications between the urban infrastructure and traffic monitoring systems. One of the most challenging issues in the IoT environment is how to make it scalable and energy-efficient with regard to its growing dimensions. Object clustering is a mechanism that increases scalability and provides energy efficiency by minimizing communication energy consumption. Since IoT is a large scale dynamic environment, clustering of its objects is a NP-Complete problem. This paper formulates energy-aware clustering of things as an optimization problem targeting an optimum point in which, the total consumed energy and communication cost are minimal. Then. it employs the Genetic Algorithm (GA) to solve this optimization problem by extracting the optimal number of clusters as well as the members of each cluster. In this paper, a multi objective GA for clustering that has not premature convergence problem is used. In addition, for fast GA execution multiple implementation, considerations has been measured. Moreover, the consumed energy for received and sent data, node to node and node to BS distance have been considered as effective parameters in energy consumption formulation. Numerical simulation results show the efficiency of this method in terms of the consumed energy, network lifetime, the number of dead nodes and load balancing.

References

L. Atzori, A. Iera, and G. Morabito, "The internet of things: A survey," Computer networks, vol. 54, pp. 2787-2805, 2010.

N. Maleki, H. S. Fard, M. Dadkhah, and M. Movassagh, "Scenarios for the Transition to NGN," IJCSNS, vol. 17, p. 115, 2017.

J. Tang, Z. Zhou, J. Niu, and Q. Wang, "An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things," Journal of Network and Computer Applications, vol. 40, pp. 1-11, 2014.

D.-L. Yang, F. Liu, and Y.-D. Liang, "A survey of the internet of things," in Proceedings of the 1st International Conference on EBusiness Intelligence (ICEBI2010), 2010.

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, "Internet of things: A survey on enabling technologies, protocols, and applications," IEEE Communications Surveys & Tutorials, vol. 17, pp. 2347-2376, 2015.

P. López, D. Fernández, A. J. Jara, and A. F. Skarmeta, "Survey of internet of things technologies for clinical environments," in Advanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on, 2013, pp. 1349-1354.

M. Bahrami and M. Esmaeili, "Convergence of IoT and WBSN for Smart Control of Patients Health," Networking and Communication Engineering, vol. 9, pp. 161-168, 2017.

E. Fleisch, M. Weinberger, and F. Wortmann, "Business models and the internet of things," in Interoperability and Open-Source Solutions for the Internet of Things, ed: Springer, 2015, pp. 6-10.

E. Bucherer and D. Uckelmann, "Business models for the internet of things," in Architecting the internet of things, ed: Springer, 2011, pp. 253-277.

M. R. Palattella, M. Dohler, A. Grieco, G. Rizzo, J. Torsner, T. Engel, et al., "Internet of things in the 5G era: Enablers, architecture, and business models," IEEE Journal on Selected Areas in Communications, vol. 34, pp. 510-527, 2016.

R. Khan, S. U. Khan, R. Zaheer, and S. Khan, "Future internet: the internet of things architecture, possible applications and key challenges," in Frontiers of Information Technology (FIT), 2012 10th International Conference on, 2012, pp. 257-260.

J.-Y. Chang, "A distributed cluster computing energy-efficient routing scheme for internet of things systems," Wireless Personal Communications, vol. 82, pp. 757-776, 2015.

S. Zhou, K.-J. Lin, and C.-S. Shih, "Device clustering for fault monitoring in Internet of Things systems," in Internet of Things (WFIoT), 2015 IEEE 2nd World Forum on, 2015, pp. 228-233.

Y. Sung, S. Lee, and M. Lee, "A Multi-Hop Clustering Mechanism for Scalable IoT Networks," Sensors, vol. 18, p. 961, 2018.

M. Esmaeili and S. Jamali, "IoT based scheduling for energy saving in a wireless ecosystem," Wireless Communication, 2016.

H. Lin, L. Wang, and R. Kong, "Energy efficient clustering protocol for large-scale sensor networks," IEEE Sensors Journal, vol. 15, pp. 7150-7160, 2015.

D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, "Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach," in Global Telecommunications Conference, 2002. GLOBECOM'02. IEEE, 2002, pp. 62-66.

H. S. Fard and A. G. Rahbar, "Physical constraint and load aware seamless handover for IPTV in wireless LANs," Computers & Electrical Engineering, vol. 56, pp. 222-242, 2016.

T. Sánchez López, D. C. Ranasinghe, M. Harrison, and D. McFarlane, "Adding sense to the Internet of Things," Personal and Ubiquitous Computing, vol. 16, pp. 291-308, 2012.

E.-G. Talbi, Metaheuristics: from design to implementation vol. 74: John Wiley & Sons, 2009.

M. Dorigo and M. Birattari, "Ant colony optimization," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 36-39.

F. Marini and B. Walczak, "Particle swarm optimization (PSO). A tutorial," Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153-165, 2015.

M. E. Celebi, H. A. Kingravi, and P. A. Vela, "A comparative study of efficient initialization methods for the k-means clustering algorithm," Expert systems with applications, vol. 40, pp. 200-210, 2013.

J.-L. Liu and C. V. Ravishankar, "LEACH-GA: Genetic algorithmbased energy-efficient adaptive clustering protocol for wireless sensor networks," International Journal of Machine Learning and Computing, vol. 1, p. 79, 2011.

J. A. Stankovic, T. Abdelzaher, C. Lu, L. Sha, and J. C. Hou, "Realtime communication and coordination in embedded sensor networks," Proceedings of the IEEE, vol. 91, pp. 1002-1022, 2003.

S. Hussain, A. W. Matin, and O. Islam, "Genetic algorithm for energy efficient clusters in wireless sensor networks," in Information Technology, 2007. ITNG'07. Fourth International Conference on, 2007, pp. 147-154.

A. Norouzi and A. H. Zaim, "Genetic algorithm application in optimization of wireless sensor networks," The Scientific World Journal, vol. 2014, 2014.

J. Jia, X. Wu, J. Chen, and X. Wang, "Exploiting sensor redistribution for eliminating the energy hole problem in mobile sensor networks," EURASIP Journal on Wireless Communications and Networking, vol. 2012, p. 68, 2012.

S. Jamali and P. Jafarzadeh, "An intelligent intrusion detection system by using hierarchically structured learning automata," Neural Computing and Applications, vol. 28, pp. 1001-1008, 2017.

L. Kong, J.-S. Pan, V. Snášel, P.-W. Tsai, and T.-W. Sung, "An energyaware routing protocol for wireless sensor network based on genetic algorithm," Telecommunication Systems, vol. 67, pp. 451-463, 2018.

W. Mardini, Y. Khamayseh, M. B. Yassein, and M. H. Khatatbeh, "Mining Internet of Things for intelligent objects using genetic algorithm," Computers & Electrical Engineering, vol. 66, pp. 423-434, 2018.

S. Bayraklı and S. Z. Erdogan, "Genetic algorithm based energy efficient clusters (gabeec) in wireless sensor networks," Procedia Computer Science, vol. 10, pp. 247-254, 2012.

M. Esmaeili and S. Jamali, "A Survey: Optimization of Energy Consumption by using the Genetic Algorithm in WSN based Internet of Things," CiiT International Journal of Wireless Communication, 2016.

S. Babaie, A. K. Zadeh, and M. G. Amiri, "The new clustering algorithm with cluster members bounds for energy dissipation avoidance in wireless sensor network," in Computer Design and Applications (ICCDA), 2010 International Conference on, 2010, pp. V2-613-V2-617.

T. Ducrocq, N. Mitton, and M. Hauspie, "Energy-based clustering for wireless sensor network lifetime optimization," in Wireless Communications and Networking Conference (WCNC), 2013 IEEE, 2013, pp. 968-973.

S. Jamali, L. Rezaei, and S. J. Gudakahriz, "An energy-efficient routing protocol for MANETs: a particle swarm optimization approach," Journal of applied research and technology, vol. 11, pp. 803-812, 2013.

D. Tse and P. Viswanath, Fundamentals of wireless communication: Cambridge university press, 2005.

E. Heidari and A. Movaghar, "An efficient method based on genetic algorithms to solve sensor network optimization problem," arXiv preprint arXiv:1104.0355, 2011.

V. Vashishth, A. Chhabra, A. Khanna, D. K. Sharma, and J. Singh, "An Energy Efficient Routing Protocol for Wireless Internet-of-Things Sensor Networks," arXiv preprint arXiv:1808.01039, 2018.

W.-J. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy, "Modeling spatial and temporal dependencies of user mobility in wireless mobile networks," IEEE/ACM Transactions on Networking (ToN), vol. 17, no. 5, pp. 1564-1577, 2009.

Downloads

Published

2020-06-30

How to Cite

Esmaeili, M., Jamali, S., & Fard, H. S. (2020). Energy-Aware Clustering in the Internet of Things by Using the Genetic Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(2), 29–37. Retrieved from https://jtec.utem.edu.my/jtec/article/view/5466

Issue

Vol. 12 No. 2: April - June 2020

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.