Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)

  1. Sumit Kumar
  2. Vijender Kumar-Solanki
  3. Saket Kumar Choudhary
  4. Ali Selamat
  5. Rubén González-Crespo
Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2020

Volumen: 6

Número: 1

Páginas: 107-116

Tipo: Artículo

DOI: 10.9781/IJIMAI.2020.01.003 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits.

Referencias bibliográficas

  • O. Said, “Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization”, International Journal of Communication Systems, Wiley, 2016.
  • S. Kumar, Z. Raza, “Internet of Things: Possibilities and Challenges”, International Journal of Systems and Service-Oriented Engineering (IJSSOE), Vol.7, no.3, pp. 32-52, July-September 2017 (ISSN 1947-3052).
  • K. Kumar, S. Kumar, O. Kaiwartya, Y. Cao, J. Lloret, N. Aslam, “CrossLayer Energy Optimization for IoTEnvironments: Technical Advances and Opportunities”, Energies, 2017.
  • S. Kumar, Z. Raza, “Using Clustering Approaches for Response Time Aware Job Scheduling Model for Internet of Things(IoT)”, International Journal of Information Technology, Springer, Vol. 9, no. 2, pp. 177-195, June 2017.
  • S Vimal, M Khari, N Dey, RG Crespo, YH Robinson, “Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT”, Computer Communications 151, pp. 355- 364, 2020
  • E. Dave, “The Internet of Things: How the Next Evolution of the Internet Is Changing Everything”, Cisco Internet Business Solutions Group, pp. 1-11, April 2011.
  • Dr. Vermesanovidiu, Dr. F. Peter, G.Patrick, G.Sergio, S. Harald, Dr. B. Alessandro, J. Ignacio, Dr. M. Margaretha, Dr. H. Mark, Dr. E. Markus, Dr. D. Pat, “Internet of Things- Global Technological and Societal Trends”, published by: River Publishers, ISBN: 9788792329677.
  • A. C. Charu, A.Naveen, S. Amit, “The Internet of Things: A Survey from the Data-Centric Perspective”, Springer Science+Business Media New York, 2013, pp 383-428, ISBN 978-1-4614-6309-2.
  • A. Saima, Y. Kun “A QoS Aware Message Scheduling Algorithm in Internet of Things Environment”, IEEE Online Conference on Green Communications (Online Green Comm), 2013.
  • Y. Lu, W. Hu, Study on the Application of Ant Colony Algorithm in the Route of Internet of Things”, International Journal of Smart Home, Volume 7, No. 3, May, 2013.
  • S. Saatchi, C. C. Hung, “Hybridization of the Ant Colony Optimization with the K-Means Algorithm for Clustering”, Springer, pp. 511 – 520, 2005.
  • C.I. Mary, DR. S.V. K. Raja, “Refinement of Clusters from k-means with Ant Colony Optimization”, Journal of Theoretical and Applied Information Technology, 2009.
  • M. Khari, et al., “Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization”, Soft Computing, In Press, 2019
  • J. Lu, R. Hu, “A new hybrid clustering algorithm based on K-means and ant colony algorithm”, Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013).
  • C. Cheng, Z. Qian, “An IoT Ant Colony Foraging Routing Algorithm Based on Markov Decision Model”, International Conference on Soft Computing in Information Communication Technology (SCICT 2014).
  • M. Dorigo, G. D. Caro, “Ant Colony Optimization: A New MetaHeuristic”, IEEE, 1999.
  • D. Merkle, M. Middendorf, H. Schmeck, “Ant Colony Optimization for Resource-Constrained Project Scheduling”, IEEE, Transactions on Evolutionary Computation, Volume 6, No. 4, August, 2002.
  • M. Frey, M. Günes, “Attack of the Ants: Studying Ant Routing Algorithms inSimulation and Wireless Testbeds”, arXiv:1409.0988v1. 3 Sep 2014.
  • M.Głabowski, B.Musznicki, P. Nowak, P. Zwierzykowski, “Shortest Path Problem Solving Based on Ant Colony Optimization Metaheuristic”, Image Processing & Communication, Volume 17, No. 1-2, pp. 7-18, 2017.
  • P. Yuqing, H. Xiangdan, L. Shang, “The K-means Clustering Algorithm Based on Density and Ant Colony”, IEEE Int. Conf. Neural Networks & Signal Processing, Nanjing, China, December 14-17, 2003.
  • B. Mamalis, D. Gavalas, C. Konstantopoulos, G. Pantziou, “Clustering in Wireless Sensor Networks,” Zhang/RFID and Sensor Networks AU7777. Proof Page 323 2009-6-24, 2012.
  • R.Jain, “Introduction to Queueing. In: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling,” ch. 30. John Wiley & Sons, Inc., New York, 1991.
  • Trivedi, K. S., “Probability and Statistics with Reliability, Queuing and Computer Science Applications”, Prentice Hall, 1982.
  • R. L. Graham, E.L. Lawler, J.K. Lenstra, A.H.G.R. Kan, “Optimization and approximation in deterministic sequencing and scheduling: A survey, Annals of Discrete Mathematics”, Volume 5, pp 287–326, 1979.
  • E. Gelenbe, R. Lent, “Optimising Server Energy Consumption and Response Time”, Theoretical and Applied Informatics, Volume 24, Issue, 4, pp 257-270, November 2012, ISSN: 1896-5334.
  • S. Kumar, Z. Raza, “A Priority Based Message Response Time Aware Job Scheduling Model for the Internet of Things (IoT)”, International Journal of Cyber Physical System (IJCPS), IGI Global, Volume 1, Issue 1, pp 1-14, January-June 2019, ISSN: 2577-4867, DOI: 10.4018/IJCPS.2019010101.
  • C. G. García, E. R. N. Valdez, V. G. Díaz, C. P. G.Bustelo, J. M. C. Lovelle, “A Review of Artificial Intelligence in the Internet of Things, International Journal of Interactive Multimedia and Artificial Intelligence, Volume 5, Issue 4, pp 9-20, 2019.
  • C. G. García, D. M. Llorián, J. M. C. Lovelle, “A Review about Smart Objects, Sensors and Actuators”, International Journal of Interactive Multimedia & Artificial Intelligence, Volume 4, Issue 3, 2017.
  • J. Molano, J. Lovelle, C. Montenegro, J. Granados, R. Crespo, “Metamodel for Integration of Internet of Things, Social Networks, the Cloud and Industry 4.0”, Journal of Ambient Intelligence and Humanized Computing, Volume 9, Issue 3, pp 709-723, 2018.
  • S. Kumar, Z. Raza, “Using Supply Chain Management Approach for Message Forwarding for Internet of Things (IoT)”, International Conference on Technology, Engineering and Science (IConTES), Volume 4, pp 21-27, 2018.
  • S. Kumar, Z. Raza, “A K-Means Clustering Based Message Forwarding Model for Internet of Things (IoT)”, International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, pp 604- 609, 2018.
  • V. García-Díaz et al., “TALISMAN MDE framework: an architecture for intelligent model-driven engineering”, International Work-Conference on Artificial Neural Networks (IWANN), Springer, pp. 299-306, 2009.
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