International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Query Aware Routing Protocol for Mobility Enabled Wireless Sensor Network

Author NameAuthor Details

M. Lingaraj, T. N. Sugumar, C. Stanly Felix , J. Ramkumar

M. Lingaraj[1]

T. N. Sugumar[2]

C. Stanly Felix [3]

J. Ramkumar[4]

[1]Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, India

[2]Department of Computing, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India

[3]Department of Computing, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India

[4]Department of Computer Science, VLB Janakiammal College of Arts and Science, Coimbatore, Tamil Nadu, India

Abstract

Mobility Enabled Wireless Sensor Network (MEWSN) plays a significant role in different fields including environmental control, traffic control and healthcare. The performance of MEWSN is dependent not only on sensing but also on routing. Multiple research works are carried out by different researchers in the domain of routing in MEWSN, but still the performance of MEWSN gets lacked. Poor routing is the root cause for the performance degradation of MEWSN. In this paper, a new routing protocol namely Query Aware Routing Protocol (QARP) is proposed to balance the load in MEWSN to prevent congestion and exhausted power utilization. Normal routing protocols either seek to match load or route, but both are considered in QARP. Also, identified routes are classified based on an enhanced relevant vector machine classification algorithm which assists in minimizing the delay and energy consumption. Using NS2, QARP is evaluated against previous routing protocols with standard performance metrics namely throughput, delay, packet delivery ratio and energy consumption. The packet delivery ratio achieved by QARP is 92.6%, where the existing routing protocols IFLIP and PARP has achieved 62.8% and 75.4% respectively.

Index Terms

WSN

MEWSN

Routing

Query

Load

Congestion

Reference

  1. 1.
    S. Gudla and N. R. Kuda, “Learning automata based energy efficient and reliable data delivery routing mechanism in wireless sensor networks,” J. King Saud Univ. - Comput. Inf. Sci., 2021, doi: https://doi.org/10.1016/j.jksuci.2021.04.006.
  2. 2.
    Y. Yigit, V. K. Akram, and O. Dagdeviren, “Breadth-first search tree integrated vertex cover algorithms for link monitoring and routing in wireless sensor networks,” Comput. Networks, vol. 194, p. 108144, 2021, doi: https://doi.org/10.1016/j.comnet.2021.108144.
  3. 3.
    Y. Hong, D. Li, and Z. Chen, “Constructing virtual backbone with guaranteed routing cost in Wireless Sensor Networks,” Ad Hoc Networks, vol. 116, p. 102500, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102500.
  4. 4.
    L. Jia, “Distributed energy balance routing algorithm for wireless sensor network based on multi-attribute decision-making,” Sustain. Energy Technol. Assessments, vol. 45, p. 101192, 2021, doi: https://doi.org/10.1016/j.seta.2021.101192.
  5. 5.
    D. L. Reddy, P. C., and H. N. Suresh, “Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in Wireless Sensor Network,” Pervasive Mob. Comput., vol. 71, p. 101338, 2021, doi: https://doi.org/10.1016/j.pmcj.2021.101338.
  6. 6.
    G. Thahniyath and M. Jayaprasad, “Secure and load balanced routing model for wireless sensor networks,” J. King Saud Univ. - Comput. Inf. Sci., 2020, doi: https://doi.org/10.1016/j.jksuci.2020.10.012.
  7. 7.
    M. K. Singh, S. I. Amin, and A. Choudhary, “Genetic algorithm based sink mobility for energy efficient data routing in wireless sensor networks,” AEU - Int. J. Electron. Commun., vol. 131, p. 153605, 2021, doi: https://doi.org/10.1016/j.aeue.2021.153605.
  8. 8.
    B. M. Sahoo, H. M. Pandey, and T. Amgoth, “GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network,” Swarm Evol. Comput., vol. 60, p. 100772, 2021, doi: https://doi.org/10.1016/j.swevo.2020.100772.
  9. 9.
    Y. U. Xiu-wu, Y. U. Hao, L. Yong, and X. Ren-rong, “A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks,” Comput. Networks, vol. 167, p. 106994, 2020, doi: https://doi.org/10.1016/j.comnet.2019.106994.
  10. 10.
    D. Mehta and S. Saxena, “MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks,” Sustain. Comput. Informatics Syst., vol. 28, p. 100406, 2020, doi: https://doi.org/10.1016/j.suscom.2020.100406.
  11. 11.
    S. Prithi and S. Sumathi, “LD2FA-PSO: A novel Learning Dynamic Deterministic Finite Automata with PSO algorithm for secured energy efficient routing in Wireless Sensor Network,” Ad Hoc Networks, vol. 97, p. 102024, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102024.
  12. 12.
    V. Mythili, A. Suresh, M. M. Devasagayam, and R. Dhanasekaran, “SEAT-DSR: Spatial and energy aware trusted dynamic distance source routing algorithm for secure data communications in wireless sensor networks,” Cogn. Syst. Res., vol. 58, pp. 143–155, 2019, doi: https://doi.org/10.1016/j.cogsys.2019.02.005.
  13. 13.
    D. B.D. and F. Al-Turjman, “A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks,” Ad Hoc Networks, vol. 97, p. 102022, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102022.
  14. 14.
    M. Naghibi and H. Barati, “EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks,” Sustain. Comput. Informatics Syst., vol. 25, p. 100377, 2020, doi: https://doi.org/10.1016/j.suscom.2020.100377.
  15. 15.
    A. Mazinani, S. M. Mazinani, and M. Mirzaie, “FMCR-CT: An energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network,” Alexandria Eng. J., vol. 58, no. 1, pp. 127–141, 2019, doi: https://doi.org/10.1016/j.aej.2018.12.004.
  16. 16.
    B. Abbache et al., “Dissimulation-based and load-balance-aware routing protocol for request and event oriented mobile wireless sensor networks,” AEU - Int. J. Electron. Commun., vol. 99, pp. 264–283, 2019, doi: https://doi.org/10.1016/j.aeue.2018.12.003.
  17. 17.
    M. K, C. K, and S. C, “An energy efficient clustering scheme using multilevel routing for wireless sensor network,” Comput. Electr. Eng., vol. 69, pp. 642–652, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.10.007.
  18. 18.
    C. Lin, Y. Sun, K. Wang, Z. Chen, B. Xu, and G. Wu, “Double warning thresholds for preemptive charging scheduling in Wireless Rechargeable Sensor Networks,” Comput. Networks, vol. 148, pp. 72–87, Jan. 2019, doi: 10.1016/j.comnet.2018.10.023.
  19. 19.
    F. H. Awad, “Optimization of relay node deployment for multisource multipath routing in Wireless Multimedia Sensor Networks using Gaussian distribution,” Comput. Networks, vol. 145, pp. 96–106, 2018, doi: https://doi.org/10.1016/j.comnet.2018.08.021.
  20. 20.
    L. Han, M. Zhou, W. Jia, Z. Dalil, and X. Xu, “Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model,” Inf. Sci. (Ny)., vol. 476, pp. 491–504, Feb. 2019, doi: 10.1016/j.ins.2018.06.017.
  21. 21.
    H. Huang, A. V. Savkin, M. Ding, and C. Huang, “Mobile robots in wireless sensor networks: A survey on tasks,” Comput. Networks, vol. 148, pp. 1–19, Jan. 2019, doi: 10.1016/j.comnet.2018.10.018.
  22. 22.
    N. Ramluckun and V. Bassoo, “Energy-efficient chain-cluster based intelligent routing technique for Wireless Sensor Networks,” Appl. Comput. Informatics, 2020, doi: 10.1016/j.aci.2018.02.004.
  23. 23.
    S. V. Manisekaran and R. Venkatesan, “An analysis of software-defined routing approach for wireless sensor networks,” Comput. Electr. Eng., vol. 56, pp. 456–467, Nov. 2016, doi: 10.1016/j.compeleceng.2016.06.017.
  24. 24.
    A. Agrawal, V. Singh, S. Jain, and R. K. Gupta, “GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink,” AEU - Int. J. Electron. Commun., vol. 94, pp. 1–11, 2018, doi: https://doi.org/10.1016/j.aeue.2018.06.036.
  25. 25.
    R. Simon Carbajo, E. Simon Carbajo, B. Basu, and C. Mc Goldrick, “Routing in wireless sensor networks for wind turbine monitoring,” Pervasive Mob. Comput., vol. 39, pp. 1–35, Aug. 2017, doi: 10.1016/j.pmcj.2017.04.007.
  26. 26.
    A. E. Zonouz, L. Xing, V. M. Vokkarane, and Y. L. Sun, “Reliability-oriented single-path routing protocols in wireless sensor networks,” IEEE Sens. J., vol. 14, no. 11, pp. 4059–4068, Nov. 2014, doi: 10.1109/JSEN.2014.2332296.
  27. 27.
    M. Zhao, J. Li, and Y. Yang, “A framework of joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks,” IEEE Trans. Mob. Comput., vol. 13, no. 12, pp. 2689–2705, Dec. 2014, doi: 10.1109/TMC.2014.2307335.
  28. 28.
    D. Sharma and A. P. Bhondekar, “Traffic and Energy Aware Routing for Heterogeneous Wireless Sensor Networks,” IEEE Commun. Lett., vol. 22, no. 8, pp. 1608–1611, Aug. 2018, doi: 10.1109/LCOMM.2018.2841911.
  29. 29.
    Z. Sun, M. Wei, Z. Zhang, and G. Qu, “Secure Routing Protocol based on Multi-objective Ant-colony-optimization for wireless sensor networks,” Appl. Soft Comput. J., vol. 77, pp. 366–375, Apr. 2019, doi: 10.1016/j.asoc.2019.01.034.
  30. 30.
    S. Al-Sodairi and R. Ouni, “Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks,” Sustain. Comput. Informatics Syst., vol. 20, pp. 1–13, Dec. 2018, doi: 10.1016/j.suscom.2018.08.007.
  31. 31.
    J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
  32. 32.
    Lingaraj M and Prakash A, “Power Aware Routing Protocol (PARP) to Reduce Energy Consumption in Wireless Sensor Networks,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 380–385, 2019.
  33. 33.
    J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
  34. 34.
    J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.
  35. 35.
    R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.
  36. 36.
    J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.
  37. 37.
    J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, doi: 10.30534/ijeter/2020/82882020.
  38. 38.
    J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
  39. 39.
    T. N. Sugumar and N. R. Ramasamy, “mDesk: a scalable and reliable hypervisor framework for effective provisioning of resource and downtime reduction,” J. Supercomput., vol. 76, no. 2, pp. 1277–1292, Feb. 2020, doi: 10.1007/s11227-018-2662-5.
SCOPUS
SCImago Journal & Country Rank