Research Article
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Year 2020, Volume: 26 Issue: 5, 944 - 952, 23.10.2020

Abstract

References

  • [1] Xu L, Collier R, O’Hare GM. “A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios”. IEEE Internet of Things Journal, 4(5), 1229-1249, 2017.
  • [2] Afsar MM, Tayarani-N MH. “Clustering in sensor networks: A literature survey”. Journal of Network and Computer Applications, 46, 198-226, 2014.
  • [3] Liao Y, Qi H, Li W. “Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks”. IEEE Sensors Journal, 13(5), 1498-1506, 2013.
  • [4] Mahajan S, Malhotra J, Sharma S. “An energy balanced QoS based cluster head selection strategy for WSN”. Egyptian Informatics Journal, 15(3), 189-199, 2014.
  • [5] Guiloufi ABF, Nasri N, Farah MAB, Kachouri A. “MED-BS clustering algorithm for the small-scale wireless sensor networks”. Wireless Sensor Network, 5(4), 67-75, 2013.
  • [6] Elhabyan RS, Yagoub MC. “Weighted tree based routing and clustering protocol for WSN”. 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Regina, Canada, 5-8 May 2013.
  • [7] Belabed F, Bouallegue R. “An optimized weight-based clustering algorithm in wireless sensor networks”. 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 5-9 September 2016.
  • [8] Zakariayi S, Babaie S. “DEHCIC: A distributed energy-aware hexagon-based clustering algorithm to improve coverage in wireless sensor networks”. Peer-to-Peer Networking and Applications, 12, 689-704, 2018.
  • [9] Adil Mahdi O, Abdul Wahab AW, Idris MYI, Abu Znaid A, Al-Mayouf YRB, Khan S. “WDARS: A weighted data aggregation routing strategy with minimum link cost in event-driven WSNs”. Journal of Sensors, 2016(Special Issue), 1-12, 2016.
  • [10] Singh, S. “Energy efficient multilevel network model for heterogeneous WSNs”. Engineering Science and Technology, an International Journal, 20(1), 105-115, 2017.
  • [11] Zhang DG, Wang X, Song XD, Zhang T, Zhu YN. “A new clustering routing method based on PECE for WSN”. EURASIP Journal on Wireless Communications and Networking, 2015(1), 162, 2015.
  • [12] Shang F. “A multi-hop routing algorithm based on integrated metrics for wireless sensor networks”. Applied Mathematics & Information Sciences, 7(3), 1021-1034, 2013.
  • [13] Rao PS, Jana PK, Banka H. “A particle swarm optimization-based energy efficient cluster head selection algorithm for wireless sensor networks”. Wireless Networks, 23(7), 2005-2020, 2017.
  • [14] Sajwan M, Gosain D, Sharma AK. “CAMP: cluster aided multi-path routing protocol for wireless sensor networks”. Wireless Networks, 25, 2603-2620, 2019.
  • [15] Abasıkeleş-Turgut İ, Hafif OG. “NODIC: a novel distributed clustering routing protocol in WSNs by using a time-sharing approach for CH election”. Wireless Networks, 22(3), 1023-1034, 2016.
  • [16] Heinzelman WR, Chandrakasan A, Balakrishnan H. “Energy-efficient communication protocol for wireless microsensor networks”. In Proceedings of the 33rd Annual Hawaii international conference on system sciences, Maui, Hawaii, 4-7 January 2000.
  • [17] Gambhir S, Fatima N. “Op-LEACH: an optimized LEACH method for busty traffic in WSNs”. IEEE 2014 Fourth International Conference on Advanced Computing & Communication Technologies, Rohtak, India, 8-9 February 2014.
  • [18] Khan K, Sajid M, Mahmood S, Khan ZA, Qasim U, Javaid N. “(LEACH) 2: Combining LEACH with Linearly Enhanced Approach for Cluster Handling in WSNs”. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, Gwangju, Korea, 25-27 March 2015.
  • [19] Nayak P, Devulapalli A. “A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime”. IEEE Sensors Journal, 16(1), 137-144, 2016.
  • [20] Junping H, Yuhui J, Liang D. “A time-based cluster-head selection algorithm for LEACH”. 2008 IEEE Symposium on Computers and Communications, Marrakech, Morocco, 6-9 July 2008.
  • [21] Batra PK, Kant K. “LEACH-MAC: a new cluster head selection algorithm for Wireless Sensor Networks”. Wireless Networks, 22(1), 49-60, 2016.
  • [22] Jia D, Zhu H, Zou S, Hu P. “Dynamic cluster head selection method for wireless sensor network”. IEEE Sensors Journal, 16(8), 2746-2754, 2015.
  • [23] Chen DR, Chen LC, Chen MY, Hsu MY. “A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks”. Computer Communications, 137, 15-31, 2019.
  • [24] Rui L, Wang X, Zhang Y, Wang X, Qiu, X. “A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids”. Future Generation Computer Systems, 100, 35-45, 2019.
  • [25] Yu X, Liu Q, Hu M, Xiao R. “Uneven clustering routing algorithm based on glowworm swarm optimization”. Ad Hoc Networks, 93, 1-8, 2019.

Dynamic coefficient-based cluster head election in wireless sensor networks

Year 2020, Volume: 26 Issue: 5, 944 - 952, 23.10.2020

Abstract

Owing to the fact that the selection of cluster heads has a significant effect on the lifetime of the network, many researches have proposed various cluster head election methodologies for cluster-based WSNs. Although recent studies have focused on adaptive approaches, in which different parameters are assembled under a function, the effect of these parameters on cluster head election is not investigated in detail. In this paper, initially, a small-scale dataset is constructed by evaluating the death of the first, the half and the last node in a cluster-based WSN using three popular cluster head parameters, including the remaining energy of the nodes, the intra-cluster communication cost and the number of neighbours. In consideration of the results, a dynamically changeable coefficient based adaptive cluster head election, DCoCH, is proposed. The coefficients of the cluster head election parameters show alteration within three different periods of the lifetime of the network. DCoCH is compared with two recent adaptive based cluster head election methodologies for various WSN parameters and the results show that DCoCH outperforms equivalent approaches for different values of the location of the base station, the size of the network, the number of the nodes, the initial batteries of the nodes and the distribution of the nodes.

References

  • [1] Xu L, Collier R, O’Hare GM. “A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios”. IEEE Internet of Things Journal, 4(5), 1229-1249, 2017.
  • [2] Afsar MM, Tayarani-N MH. “Clustering in sensor networks: A literature survey”. Journal of Network and Computer Applications, 46, 198-226, 2014.
  • [3] Liao Y, Qi H, Li W. “Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks”. IEEE Sensors Journal, 13(5), 1498-1506, 2013.
  • [4] Mahajan S, Malhotra J, Sharma S. “An energy balanced QoS based cluster head selection strategy for WSN”. Egyptian Informatics Journal, 15(3), 189-199, 2014.
  • [5] Guiloufi ABF, Nasri N, Farah MAB, Kachouri A. “MED-BS clustering algorithm for the small-scale wireless sensor networks”. Wireless Sensor Network, 5(4), 67-75, 2013.
  • [6] Elhabyan RS, Yagoub MC. “Weighted tree based routing and clustering protocol for WSN”. 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Regina, Canada, 5-8 May 2013.
  • [7] Belabed F, Bouallegue R. “An optimized weight-based clustering algorithm in wireless sensor networks”. 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 5-9 September 2016.
  • [8] Zakariayi S, Babaie S. “DEHCIC: A distributed energy-aware hexagon-based clustering algorithm to improve coverage in wireless sensor networks”. Peer-to-Peer Networking and Applications, 12, 689-704, 2018.
  • [9] Adil Mahdi O, Abdul Wahab AW, Idris MYI, Abu Znaid A, Al-Mayouf YRB, Khan S. “WDARS: A weighted data aggregation routing strategy with minimum link cost in event-driven WSNs”. Journal of Sensors, 2016(Special Issue), 1-12, 2016.
  • [10] Singh, S. “Energy efficient multilevel network model for heterogeneous WSNs”. Engineering Science and Technology, an International Journal, 20(1), 105-115, 2017.
  • [11] Zhang DG, Wang X, Song XD, Zhang T, Zhu YN. “A new clustering routing method based on PECE for WSN”. EURASIP Journal on Wireless Communications and Networking, 2015(1), 162, 2015.
  • [12] Shang F. “A multi-hop routing algorithm based on integrated metrics for wireless sensor networks”. Applied Mathematics & Information Sciences, 7(3), 1021-1034, 2013.
  • [13] Rao PS, Jana PK, Banka H. “A particle swarm optimization-based energy efficient cluster head selection algorithm for wireless sensor networks”. Wireless Networks, 23(7), 2005-2020, 2017.
  • [14] Sajwan M, Gosain D, Sharma AK. “CAMP: cluster aided multi-path routing protocol for wireless sensor networks”. Wireless Networks, 25, 2603-2620, 2019.
  • [15] Abasıkeleş-Turgut İ, Hafif OG. “NODIC: a novel distributed clustering routing protocol in WSNs by using a time-sharing approach for CH election”. Wireless Networks, 22(3), 1023-1034, 2016.
  • [16] Heinzelman WR, Chandrakasan A, Balakrishnan H. “Energy-efficient communication protocol for wireless microsensor networks”. In Proceedings of the 33rd Annual Hawaii international conference on system sciences, Maui, Hawaii, 4-7 January 2000.
  • [17] Gambhir S, Fatima N. “Op-LEACH: an optimized LEACH method for busty traffic in WSNs”. IEEE 2014 Fourth International Conference on Advanced Computing & Communication Technologies, Rohtak, India, 8-9 February 2014.
  • [18] Khan K, Sajid M, Mahmood S, Khan ZA, Qasim U, Javaid N. “(LEACH) 2: Combining LEACH with Linearly Enhanced Approach for Cluster Handling in WSNs”. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, Gwangju, Korea, 25-27 March 2015.
  • [19] Nayak P, Devulapalli A. “A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime”. IEEE Sensors Journal, 16(1), 137-144, 2016.
  • [20] Junping H, Yuhui J, Liang D. “A time-based cluster-head selection algorithm for LEACH”. 2008 IEEE Symposium on Computers and Communications, Marrakech, Morocco, 6-9 July 2008.
  • [21] Batra PK, Kant K. “LEACH-MAC: a new cluster head selection algorithm for Wireless Sensor Networks”. Wireless Networks, 22(1), 49-60, 2016.
  • [22] Jia D, Zhu H, Zou S, Hu P. “Dynamic cluster head selection method for wireless sensor network”. IEEE Sensors Journal, 16(8), 2746-2754, 2015.
  • [23] Chen DR, Chen LC, Chen MY, Hsu MY. “A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks”. Computer Communications, 137, 15-31, 2019.
  • [24] Rui L, Wang X, Zhang Y, Wang X, Qiu, X. “A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids”. Future Generation Computer Systems, 100, 35-45, 2019.
  • [25] Yu X, Liu Q, Hu M, Xiao R. “Uneven clustering routing algorithm based on glowworm swarm optimization”. Ad Hoc Networks, 93, 1-8, 2019.
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

İpek Abasıkeleş Turgut This is me

Publication Date October 23, 2020
Published in Issue Year 2020 Volume: 26 Issue: 5

Cite

APA Abasıkeleş Turgut, İ. (2020). Dynamic coefficient-based cluster head election in wireless sensor networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(5), 944-952.
AMA Abasıkeleş Turgut İ. Dynamic coefficient-based cluster head election in wireless sensor networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. October 2020;26(5):944-952.
Chicago Abasıkeleş Turgut, İpek. “Dynamic Coefficient-Based Cluster Head Election in Wireless Sensor Networks”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26, no. 5 (October 2020): 944-52.
EndNote Abasıkeleş Turgut İ (October 1, 2020) Dynamic coefficient-based cluster head election in wireless sensor networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26 5 944–952.
IEEE İ. Abasıkeleş Turgut, “Dynamic coefficient-based cluster head election in wireless sensor networks”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 26, no. 5, pp. 944–952, 2020.
ISNAD Abasıkeleş Turgut, İpek. “Dynamic Coefficient-Based Cluster Head Election in Wireless Sensor Networks”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26/5 (October 2020), 944-952.
JAMA Abasıkeleş Turgut İ. Dynamic coefficient-based cluster head election in wireless sensor networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2020;26:944–952.
MLA Abasıkeleş Turgut, İpek. “Dynamic Coefficient-Based Cluster Head Election in Wireless Sensor Networks”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 26, no. 5, 2020, pp. 944-52.
Vancouver Abasıkeleş Turgut İ. Dynamic coefficient-based cluster head election in wireless sensor networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2020;26(5):944-52.

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