A holistic methodology for improved RFID network lifetime by advanced cluster head selection using dragonfly algorithm

  1. Pramod Singh Rathore 1
  2. Abhishek Kumar 2
  3. Vicente García-Díaz 3
  1. 1 University MDS Ajmer
  2. 2 Chitkara University
    info

    Chitkara University

    Rājpura, India

    ROR https://ror.org/057d6z539

  3. 3 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Mostrar afiliaciones +
Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2020

Volumen: 6

Número: 2

Páginas: 48-55

Tipo: Artículo

DOI: 10.9781/IJIMAI.2020.05.003 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Objetivos de desarrollo sostenible

Resumen

Radio Frequency Identification (RFID) networks usually require many tags along with readers and computation facilities. Those networks have limitations with respect to computing power and energy consumption. Thus, for saving energy and to make the best use of the resources, networks should operate and be able to recover in an efficient way. This will also reduce the energy expenditure of RFID readers. In this work, the RFID network life span will be enlarged through an energy-efficient cluster-based protocol used together with the Dragonfly algorithm. There are two stages in the processing of the clustering system: the cluster formation from the whole structure and the election of a cluster leader. After completing those procedures, the cluster leader controls the other nodes that are not leaders. The system works with a large energy node that provides an amount of energy while transmitting aggregated data near a base station.

Referencias bibliográficas

  • R. Koh, E. W. Schuster, I. Chackrabarti, and A. Bellman, “Securing the Pharmaceutical Supply Chain,” White Paper MIT-AUTO ID-WH- 021, Auto-Id Center MIT, Cambridge, MA 02139-4307, USA, 2003, Available at http://www.mitdatacenter.org
  • J. Kim, D. Choi, I. Kim, and Kim H., “Product authentication service of consumer’s mobile RFID device,” IEEE 10th International Symposium on Consumer Electronics, pp. 1-6, 2006.
  • Kim, J. and Kim, H., “A wireless service for product authentication in mobile RFID environment”, 1st International Symposium on Wireless Pervasive Computing, pp. 1-5, 2006.
  • C. –L. Chen, Y. –Y. Chen, T. –F. Shih, T. –M.Kuo, “An RFID Authentication and Anti-counterfeit Transaction Protocol, ” International Symposium on Computer, Consumer and Control, 2012, pp. 419-422.
  • S. Pahuja, S. Negi, A. Verma, P. Rathi, N. Narang, R. Chawla, “An Authentication Protocol for secure tag-reader communication,” IEEE Students’ Conference on Electrical, Electronics and Computer Science, 2012.
  • W. Alsalih, K. Ali, and H. Hassanein, “Optimal distance-based clustering for tag anti-collision in RFID systems,” 2008 33rd IEEE Conference on Local Computer Networks (LCN), Montreal, Que, 2008, pp. 266-273, doi: 10.1109/LCN.2008.4664179.
  • Birari S.M., Iyer S., “PULSE: A MAC Protocol for RFID Networks” in Enokido T., Yan L., Xiao B., Kim D., Dai Y., Yang L.T. (eds) Embedded and Ubiquitous Computing – EUC 2005 Workshops. EUC 2005. Lecture Notes in Computer Science, vol 3823. Springer, Berlin, Heidelberg.
  • P.D’Arco and A. De Santis, “On ultralightweight RFID Authentication Protocols,” IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 4, July/August 2011.
  • J. Yang, K. Ren and K. Kim, “Security and Privacy on Authentication Protocol for Low-Cost Radio”, Proc. 2005 Symp. Cryptography and Information Security, 2005.
  • J. Wang, D.Wang, Y.Zhao, and T. Korhonen, “A Novel Anti-Collision Protocol with Collision based Dynamic Clustering in Multiple-Reader RFID Systems,” International Conference on Applied Informatics and Communications, 2008, pp. 417-422.
  • M.V. Bueno Delgado, J. Vales Alonso, F.J. Gonzalez Castaño, “Analysis of DFSA Anti-collision Protocols in passive RFID environment” 2009 35th Annual Conference of IEEE Industrial Electronics, Porto, 2009, pp. 2610- 2617, doi: 10.1109/IECON.2009.5415261.
  • M.Shuang, Y. Xiao-long, “An Efficient Authentication Protocol for Low-Cost RFID System in the Presence of Malicious Readers” 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery(FSKD 2012), pp. 2111-2114.
  • S. Pahuja, S. Negi, A. Verma, P. Rathi, N. Narang, R. Chawla, “An Authentication Protocol for secure tag-reader communication,” 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science, Bhopal, 2012, pp. 1-4, doi: 10.1109/SCEECS.2012.6184757.
  • C. –L. Chen, Y. –Y. Chen, T. –F. Shih, T. –M.Kuo, “An RFID Authentication and Anti-counterfeit Transaction Protocol” 2012 International Symposium on Computer, Consumer and Control, 2012, pp. 419-422.
  • M.Shuang, Y. Xiao-long, “An Efficient Authentication Protocol for Low-Cost RFID System in the Presence of Malicious Readers” 2012 9 International Conference on Fuzzy System and Knowledge Discovery (FSKD 2012), pp. 2111-2114.
  • L. F.Bittencourt, E. R. Madeira, F.Cierre, and L.Buzato, “A path clustering heuristic for scheduling task graphs onto a grid,” in 3rd International Workshop on Middleware for Grid Computing (MGC05), 2005.
  • P. S. Mann, and S. Singh, “Energy-efficient hierarchical routing for wireless sensor networks: a swarm intelligence approach.” Wireless Personal Communications, vol. 92, no. 2, 2017, pp. 785-805.
  • M. Mirzaie, and S. M. Mazinani, “MCFL: An energy-efficient multi- - 55 - Regular Issue clustering algorithm using fuzzy logic in the wireless sensor networks.” Wireless Networks, vol. 24, no. 6, pp. 2251-2266, 2018.
  • M. Elshrkawey, S. M. Elsherif, and M. E. Wahed, “An enhancement approach for reducing energy consumption in wireless sensor networks.” Journal of King Saud University-Computer and Information Sciences, vol. 30, no. 2, pp. 259-267, 2018.
  • P. Lalwani, H. Banka, and C. Kumar, “BERA: a biogeography-based energy-saving routing architecture for wireless sensor networks,” Soft Computing, vol. 22, no. 5, pp. 1651-1667, 2018.
  • S.-C. Wang, “Artificial Neural Network,” in Interdisciplinary Computing in Java Programming, Boston, MA: Springer US, 2003, pp. 81–100.
  • V. Estivill-Castro, “Why so many clustering algorithms,” ACM SIGKDD Explore. News., vol. 4, no. 1, pp. 65–75, Jun. 2002.
  • B. S. Harish, B. S. Kumar, “Anomaly-based Intrusion Detection using Modified Fuzzy Clustering,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 6, pp. 54-59, 2017.
  • García CG, Núñez-Valdez ER, García-Díaz V, Pelayo G-Bustelo C, Cueva-Lovelle JM. “A Review of Artificial Intelligence in the Internet of Things.” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 4, pp. 9-20, 2019.
  • Bahuguna, Y., D. Punetha, and P. Verma. “An analytic Study of the Key Factors Influencing the Design and Routing Techniques of a Wireless Sensor Network.” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 3, pp. 11-15, 2017.
Fuente de los datos: Dialnet