Advertisement

A Modified Partitioning Around Medoids Clustering-Based Cluster Head Selection Scheme for Data Offload in Mobile Cloud Sensor Network

  • S. Jeen SheneEmail author
  • W. R. Sam Emmanuel
Conference paper
  • 29 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 656)

Abstract

Mobile cloud-deployed mobile sensing networks are a growing area of technology where attaining energy utilization is a challenging task during data transmission from mobile sensor devices to the cellular base station. Data offload can address drawbacks like network delay, poor performance, and high-energy consumption. Such a setup requires an efficient scheme that focuses on energy efficiency in a better way reducing the faster death of nodes. In this paper, an energy-aware approach named modified partitioning around medoids with cluster head selection (MPAM-CHS) is proposed, that aims for better clustering of mobile devices and the fairer selection of group head to minimize the energy utilization of the nodes. The proposed scheme consists of four phases like initialization, clustering, cluster head formation, and transmission phase. Initially, the nodes are randomly deployed in the network field and then clustering is performed on them using a modified PAM algorithm to determine the actual cluster points for partitioning the nodes into small groups. Next, the cluster head (CH) or the group head is determined based on the criteria such as residual energy, signal-to-noise ratio (SNR), path loss, and average path loss between the sensor and the sink. Finally, the sensed information collected from the nodes is offloaded to the group head, aggregated, and then sent to the sink. The experimental analysis shows that the proposed algorithm has a significant gain in energy consumption in terms of network utilization and lifetime metrics.

Keywords

Energy efficiency Mobile cloud Mobile sensing Node clustering Cluster head Network lifetime Threshold distance 

References

  1. 1.
    Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A (2010) EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM international conference on ubiquitous computing. ACM, pp 281–290.  https://doi.org/10.1145/1864349.1864393
  2. 2.
    Zhang X, Yang Z, Sun W, Liu Y, Tang S, Xing K, Mao X (2015) Incentives for mobile crowd sensing: a survey. IEEE Commun Surv Tutorials 18(1):54–67.  https://doi.org/10.1109/COMST.2015.2415528CrossRefGoogle Scholar
  3. 3.
    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150CrossRefGoogle Scholar
  4. 4.
    Khan WZ, Xiang Y, Aalsalem MY, Arshad Q (2012) Mobile phone sensing systems: a survey. IEEE Commun Surv Tutorials 15(1):402–427.  https://doi.org/10.1109/SURV.2012.031412.00077CrossRefGoogle Scholar
  5. 5.
    Kumar K, Lu YH (2010) Cloud computing for mobile users: can offloading computation save energy? Computer 43:51–56.  https://doi.org/10.1109/MC.2010.98CrossRefGoogle Scholar
  6. 6.
    Haghighi V, Moayedian NS (2018) An offloading strategy in mobile cloud computing considering energy and delay constraints. IEEE Access 6:11849–11861.  https://doi.org/10.1109/ACCESS.2018.2808411CrossRefGoogle Scholar
  7. 7.
    Othman M, Madani SA, Khan SU (2013) A survey of mobile cloud computing application models. IEEE Commun Surv Tutorials 16(1):393–413.  https://doi.org/10.1109/SURV.2013.062613.00160CrossRefGoogle Scholar
  8. 8.
    Liu X, Yang Q, Luo J, Ding B, Zhang S (2018) An energy-aware offloading framework for edge-augmented mobile RFID systems. IEEE Internet Things J.  https://doi.org/10.1109/jiot.2018.2881295
  9. 9.
    Chidean MI, Morgado E, Sanromán-Junquera M, Ramiro-Bargueno J, Ramos J, Caamano AJ (2016) Energy efficiency and quality of data reconstruction through data-coupled clustering for self-organized large-scale WSNs. IEEE Sens J 16(12):5010–5020.  https://doi.org/10.1109/JSEN.2016.2551466CrossRefGoogle Scholar
  10. 10.
    Souza ÉL, Pazzi RW, Nakamura EF (2015) A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks. Wirel Netw 21(7):2263–2278.  https://doi.org/10.1007/s11276-015-0914-3CrossRefGoogle Scholar
  11. 11.
    Khan BM, Bilal R, Young R (2018) Fuzzy-TOPSIS based cluster head selection in mobile wireless sensor networks. J Electr Syst Inf Technol 5(3):928–943.  https://doi.org/10.1016/j.jesit.2016.12.004CrossRefGoogle Scholar
  12. 12.
    Bhatti D, Saeed N, Nam H (2016) Fuzzy c-means clustering and energy efficient cluster head selection for cooperative sensor network. Sensors 16(9):1459.  https://doi.org/10.3390/s16091459CrossRefGoogle Scholar
  13. 13.
    Loomba R, de Frein R, Jennings B (2010) Selecting energy efficient cluster-head trajectories for collaborative mobile sensing. In: 2015 IEEE global communications conference (GLOBECOM). IEEE, pp 1–7.  https://doi.org/10.1109/glocom.2015.7417727
  14. 14.
    Sarkar A, Murugan TS (2019) Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wirel Netw 25(1):303–320.  https://doi.org/10.1007/s11276-017-1558-2CrossRefGoogle Scholar
  15. 15.
    Darabkh KA, Wala’a S, Al-Zubi RT, Alnabelsi SH (2017) C-DTB-CHR: centralized density-and threshold-based cluster head replacement protocols for wireless sensor networks. J Supercomputing 73(12):5332–5353.  https://doi.org/10.1007/s11227-017-2089-4CrossRefGoogle Scholar
  16. 16.
    Kaswan A, Singh V, Jana PK (2018) A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks. Pervasive Mobile Comput 46:122–136.  https://doi.org/10.1016/j.pmcj.2018.02.003CrossRefGoogle Scholar
  17. 17.
    Darabkh KA, Wala’a S, Hawa M, Saifan R (2018) MT-CHR: a modified threshold-based cluster head replacement protocol for wireless sensor networks. Comput Electr Engg 72:926–938.  https://doi.org/10.1016/j.compeleceng.2018.01.032CrossRefGoogle Scholar
  18. 18.
    Hong J, Kook J, Lee S, Kwon D, Yi S (2009) T-LEACH: the method of threshold-based cluster head replacement for wireless sensor networks. Inf Syst Front 11(5):513.  https://doi.org/10.1007/s10796-008-9121-4CrossRefGoogle Scholar
  19. 19.
    Darabkh KA, Odetallah SM, Al-qudah Z, Ala’F K, Shurman MM (2019) Energy-aware and density-based clustering and relaying protocol (EA-DB-CRP) for gathering data in wireless sensor networks. Appl Soft Comput 80:154–166CrossRefGoogle Scholar
  20. 20.
    Zafar S, Bashir A, Chaudhry SA (2019) Mobility-aware hierarchical clustering in mobile wireless sensor networks. IEEE Access 1(7):20394–20403CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and Research CentreNesamony Memorial Christian College, Marthandam, Affiliated to Manonmaniam Sundaranar UniversityTirunelveliIndia

Personalised recommendations