CFP last date
21 October 2024
Reseach Article

Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration

by Basilis Mamalis, Sotiris Mamalis, Marios Perlitis
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 7
Year of Publication: 2021
Authors: Basilis Mamalis, Sotiris Mamalis, Marios Perlitis
10.5120/ijca2021921362

Basilis Mamalis, Sotiris Mamalis, Marios Perlitis . Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration. International Journal of Computer Applications. 183, 7 ( Jun 2021), 30-38. DOI=10.5120/ijca2021921362

@article{ 10.5120/ijca2021921362,
author = { Basilis Mamalis, Sotiris Mamalis, Marios Perlitis },
title = { Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 7 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 30-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number7/31942-2021921362/ },
doi = { 10.5120/ijca2021921362 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:09.965193+05:30
%A Basilis Mamalis
%A Sotiris Mamalis
%A Marios Perlitis
%T Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 7
%P 30-38
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Node clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper we present a novel hybrid multi-level clustering scheme that combines a traditional, appropriately modified, gradient-based clustering protocol with an evolutionary optimization method that is mainly based on the Gravitational Search Algorithm (GSA). The proposed scheme aims at improved performance over large in size networks, where classical schemes in most cases lead to non-efficient solutions. It first creates suitably balanced multi-hop clusters, in which the sensors energy gets larger as coming closer to the cluster head (CH). This scheme is then extended to generate a hierarchy of cluster-heads with the same characteristics; note that the energy savings increase with the number of levels in the hierarchy. In the last level of the proposed scheme a suitable protocol based on the GSA runs to associate sets of top-level cluster-heads to specific gateway nodes for the eventual relaying of data to the base station (BS). The fitness function was appropriately chosen considering both the distance from the cluster heads to the gateway nodes and the remaining energy of the gateway nodes, and it was further optimized in order to gain more accurate results for large instances. Extended experimental measurements demonstrate the efficiency and scalability of the presented approach over very large WSNs, as well as its superiority over other known clustering approaches of the literature, with the same objectives.

References
  1. B. Mamalis, D. Gavalas, C. Konstantopoulos, and G. Pantziou. 2009. Clustering in wireless sensor networks. Book chapter. In RFID and sensor networks: Architectures, protocols, security & integrations, New York: CRC Press, Chap. 12, pp. 324–353.
  2. P.C.S. Rao, H. Banka, and P.K. Jana. 2016. PSO-based multiple-sink placement algorithm for protracting the lifetime of wireless sensor networks. In AISC, Springer, Heidelberg, vol. 379, pp. 605–616.
  3. R. Esmat, N. Hossein, and S. Saeid. 2009. GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232–2248.
  4. X. Bao, L. Liu, S. Zhang and F. Bao. 2010. An Energy Balanced Multihop Adaptive Clustering protocol for Wireless Sensor Networks. In Proceedings of the 2nd IEEE ICSPS Conference, vol. 3, pp. 47-51.
  5. M. Sabet, and H.R. Naji. 2015. A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEU International Journal of Electronics and Communications, 69(5), 790–799.
  6. S.A. Sert, H. Bagci, and A. Yazici. 2015. MOFCA: multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing 30, 151–165.
  7. D.S. Abbasi, and J. Abouei. 2015. Toward cluster-based weighted compressive data aggregation in wireless sensor networks. Ad Hoc Networks.
  8. W.B. Heinzelman, A.P. Chandrakasan, and H. Balakrishnan. 2000. Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii International Conference on System Sciences, p. 10.
  9. S. Lindsey, and C.S. Raghavendra. 2002. PEGASIS: power efficient gathering in sensor information systems. In Proceedings of the IEEE Aerospace Conference, pp. 1125–1130.
  10. P.C.S. Rao, P.K. Jana, and H. Banka. 2016. A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, Springer (online), 1-16.
  11. P.C.S. Rao, and H. Banka. 2017. Energy efficient clustering algorithms for WSNs: novel chemical reaction optimization approach. Wireless Networks, 23(2), 433–452.
  12. P.C.S. Rao, and H. Banka. 2017. Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks. Wireless Networks, 23(3), 759–778.
  13. G. Gupta, and M. Younis. 2003. Load-balanced clustering of wireless sensor networks. In Proceedings of IEEE Intl. Conf. on Communications, ICC 2003, vol. 3, pp. 1848–1852.
  14. C.P. Low, C. Fang, J.M. Ng, and Y.H. Ang. 2008. Efficient Load-Balanced Clustering Algorithms for Wireless Sensor Networks. Computer Communications 31(4), 750–759.
  15. S. Hussain, A.W. Matin, O. Islam. 2007. Genetic Algorithm for Hierarchical Wireless Sensor Networks. Journal of Networks 2(5), 87–97.
  16. N.M.A. Latiff, C.C. Tsemenidis, and B.S. Sheriff. 2007. Energy-aware clustering for wireless sensor networks using particle swarm optimization. In Proceedings of the 18th Annual IEEE ISPIInternational Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5.
  17. P.C.S. Rao, H. Banka and P.K. Jana. 2015. Energy Efficient Clustering for Wireless Sensor Networks: A Gravitational Search Algorithm. In Proceedings of SEMCCO 2015 Conf.,, Springer, pp. 247-259.
  18. R. Krishnaprabha and A. Gopakumar. 2014. Performance of gravitational search algorithm in wireless sensor network localization, in Intl Conf. on Communication, Signal Processing and Networking (NCCSN), IEEE, pp. 1-6.
  19. Zhao Wei-Guo, Yang Shao-Pu, Li Kui and Wang Li-Ying. 2013. Gravitational Search Algorithm for Node Localization in WSN. Information Technology Journal, 12, 5806-5811.
  20. J.M. Lanza-Gutierrez and J.A. Gomez-Pulido. 2017. A Gravitational Search Algorithm for Solving the Relay Node Placement Problem in WSNs, In International Journal of Communication Systems, 30(2), 1-21.
  21. P.C.S. Rao, H. Banka and P.K. Jana. 2015. A Gravitational Search Algorithm for Energy Efficient Multi-sink Placement in Wireless Sensor Networks. In Proceedings of SEMCCO 2015 Conf., Springer, pp. 222-234.
  22. C. Konstantopoulos, B. Mamalis, G. Pantziou, and V. Thanasias 2012. Watershed-based Clustering for Energy Efficient Data Gathering in WSNs with Mobile Collector. In Proc. of Europar Conf., Springer, LNCS 7484, pp. 754-766.
  23. C. Konstantopoulos, B. Mamalis, G. Pantziou, and V. Thanasias 2015. An Image Processing Inspired Mobile Sink Solution for Energy Efficient Data Gathering in Wireless Sensor Networks. In Wireless Networks 21(1), 227-249.
  24. B. Mamalis, and M. Perlitis, “Energy Balanced Two-level Clustering for Large-Scale Wireless Sensor Networks based on the Gravitational Search Algorithm”, In IJACSA Journal, Vol. 10, No. 12, 2019.
  25. Castalia: WSNs and BANs simulator. 2007. National ICT Australia. URL: http:// castalia.npc.nicta.com.au/.
  26. Loscri, V & Morabito, G & Marano, Salvatore. 2005. A Two-levels Hierarchy for Low-Energy Adaptive Clustering Hierarchy (TL-LEACH). In Proceedings of IEEE Vehicular Technology Conference, vol. 3, pp. 1809-1813.
  27. Bandyopadhyay, Seema & Coyle, Edward. 2003. An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. In Proceedings of IEEE INFOCOM Conference, vol.3, pp. 1713-1723.
  28. Tie Qiu, Aoyang Zhao, Feng Xia, Weisheng Si, Dapeng Oliver Wu, Tie Qiu, Aoyang Zhao, Feng Xia, Weisheng Si, and Dapeng Oliver Wu. 2017. ROSE: Robustness Strategy for Scale-Free Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 25, 5, 2944-2959.
  29. M. Dong, K. Ota and A. Liu. 2016. RMER: Reliable and Energy-Efficient Data Collection for Large-Scale Wireless Sensor Networks. In IEEE Internet of Things Journal, vol. 3, no. 4, pp. 511-519.
  30. Z. Xu, L. Chen, C. Chen and X. Guan. 2016. Joint Clustering and Routing Design for Reliable and Efficient Data Collection in Large-Scale Wireless Sensor Networks. In IEEE Internet of Things Journal, vol. 3, no. 4, pp. 520-532.
  31. P Neamatollahi and M Naghibzadeh. 2018. Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic. In the Journal of Supercomputing, 74, 6, pp 2329-2352.
  32. A. Abro, D. Zhongliang, K. A. Memon, K. H. Mohammadani, N. ul Ain, S. Memon, I. Memon and M. A. Panhwar, 2019. Minimizing Energy Expenditures using Genetic Algorithm for Scalability and Longlivety of Multi hop Sensor Networks. In 9th.IEEE ICEIEC Conf., Beijing, China, 2019, pp. 183-187.
  33. C. Konstantopoulos, N. Vathis, G. Pantziou, and D. Gavalas. 2018. Employing mobile elements for delay-constrained data gathering in WSNs. Computer Networks 135: 108-131.
  34. B. Mamalis. 2014. Prolonging Network Lifetime in Wireless Sensor Networks with Path-Constrained Mobile Sink. In IJACSA Journal, Vol.5, No.10, pp.82-91.
  35. B. Mamalis and M. Perlitis. 2019. Energy Balanced Two-level Clustering for Large-Scale Wireless Sensor Networks based on the Gravitational Search Algorithm. In IJACSA Journal, Vol.10, No.12, pp.32-41.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks Gravitational Search Algorithm Node Clustering Network Lifetime Energy Balancing