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

Enhanced Objective Function ETX Metric in Routing Protocol for Low-Power and Lossy Networks (RPL)

Author NameAuthor Details

Poorana Senthilkumar S, Subramani B

Poorana Senthilkumar S[1]

Subramani B[2]

[1]Department of Computer Applications, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India

[2]SNMV College of Arts and Science, Coimbatore, Tamil Nadu, India

Abstract

An Internet of Things (IoT) is a technique capable of real-time object connections on the Internet and accessing them anywhere at any time. The low-power wireless network is the primary component for Internet of Things applications. In this scenario, the major processes of the wireless sensor network (WSN) applications are considered routing, deploying low-power nodes, load balancing, and controlling remotely. The Internet Engineering Task Force (IETF) is systematized to enable communication over Low-Power and Lossy Networks (LLNs) using Internet Protocol Version 6 (IPv6) routing. The Routing Over Low-power and Lossy Networks (ROLL) working group has determined a new routing protocol for LLNs named Routing Protocol for Low-Power and Lossy Networks (RPL). Moreover, two Objective Functions (OF) are designed for implementing the routing technique that applies the metrics. In the network routing process, hop count is a core metric in Objective Function Zero (OF0), and expected transmission count (ETX) is a metric in Minimum Rank Hysteresis Objective Function (MRHOF). However, the network becomes high-density, and nodes are increased. Adapting the existing objective function single metric generates long hops and bottlenecks affecting network performance. To solve the stated problem, the objective function requires optimized enhanced metrics. This paper aims to provide an optimization path selection in RPL by implementing a new routing objective function metric. It is called Variance Expected Transmission Count (VETX), in which the optimized best route is computed by the method of modified variance calculated in ETX values. This implementation is simulated using the Cooja simulator, and the obtained result of the enhanced ETX metric (VETX) ensures an average of 2.6% outperformance in OF0 and MRHOF in the subjects of packet delivery ratio, latency, energy consumption, overhead, and goodput.

Index Terms

IoT

WSN

RPL

Objective Functions

Routing metric

VETX

Reference

  1. 1.
    J. Santosa and G. Kurniawan, “IOT Based Low-Cost Platform for Smart Ornamental Plant Monitoring System,” J. Theor. Appl. Inf. Technol., vol. 99, no. 24, pp. 6102–6117, 2021.
  2. 2.
    W. Xiao, J. Liu, N. Jiang, and H. Shi, “An optimization of the object function for routing protocol of low-power and Lossy networks,” in The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014), Nov. 2014, no. Icsai, pp. 515–519, doi: 10.1109/ICSAI.2014.7009341.
  3. 3.
    P. L. Gnawali, O, “The Minimum Rank with Hysteresis Objective Function,” Internet Eng. Task Force, vol. 1, pp. 1–13, 2012.
  4. 4.
    H. S. Kim, H. Cho, H. Kim, and S. Bahk, “DT-RPL: Diverse bidirectional traffic delivery through RPL routing protocol in low power and lossy networks,” Comput. Networks, vol. 126, no. October, pp. 150–161, 2017, doi: 10.1016/j.comnet.2017.07.001.
  5. 5.
    A. Ouhab, T. Abreu, H. Slimani, and A. Mellouk, “Energy-efficient clustering and routing algorithm for large-scale SDN-based IoT monitoring,” in ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Jun. 2020, vol. 2020-June, pp. 1–6, doi: 10.1109/ICC40277.2020.9148659.
  6. 6.
    J. Nassar, M. Berthomé, J. Dubrulle, N. Gouvy, N. Mitton, and B. Quoitin, “Multiple instances QoS routing in RPL: Application to smart grids,” Sensors (Switzerland), vol. 18, no. 8, pp. 1–16, 2018, doi: 10.3390/s18082472.
  7. 7.
    S. B. Gopal, C. Poongodi, M. Joseph Auxilius Jude, S. Umasri, D. Sumithra, and P. Tharani, “Minimum energy consumption objective function for RPL in internet of things,” Int. J. Sci. Technol. Res., vol. 9, no. 1, pp. 3395–3402, 2020.
  8. 8.
    S. Y. Shahdad, M. Khan, H. Sultana, M. A. Hussain, and S. M. Bilfaqih, “Routing protocols for constraint devices in an internet of things network,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 114–117, 2019, doi: 10.1109/ICCSP.2019.8697933.
  9. 9.
    K. S. Bhandari, I.-H. Ra, and G. Cho, “Multi-Topology Based QoS-Differentiation in RPL for Internet of Things Applications,” IEEE Access, vol. 8, pp. 96686–96705, 2020, doi: 10.1109/ACCESS.2020.2995794.
  10. 10.
    J. Vasseur, M. Kim, K. Pister, N. Dejean, and D. Barthel, “Routing metrics used for path calculation in low power and lossy networks,” Internet Engineering Task Force (IETF). Internet Engineering Task Force (IETF), pp. 1–30, 2012.
  11. 11.
    M. da Silva, E. L. F. Senne, and N. L. Vijaykumar, “AN OPTIMIZATION MODEL TO MINIMIZE THE EXPECTED END-TO-END TRANSMISSION TIME IN WIRELESS MESH NETWORKS,” Pesqui. Operacional, vol. 37, no. 2, pp. 209–227, Aug. 2017, doi: 10.1590/0101-7438.2017.037.02.0209.
  12. 12.
    M. Barcelo, A. Correa, J. L. Vicario, A. Morell, and X. Vilajosana, “Addressing Mobility in RPL With Position Assisted Metrics,” IEEE Sens. J., vol. 16, no. 7, pp. 2151–2161, Apr. 2016, doi: 10.1109/JSEN.2015.2500916.
  13. 13.
    S. Sennan and S. Palanisamy, “Composite metric based energy efficient routing protocol for internet of things,” Int. J. Intell. Eng. Syst., vol. 10, no. 5, pp. 278–286, 2017, doi: 10.22266/ijies2017.1031.30.
  14. 14.
    P. O. Kamgueu et al., “Energy-based routing metric for RPL,” [Research Report], no. RR-8208, INRIA. ffhal-00779519, p. 14, 2013.
  15. 15.
    N. Sousa, J. V. V. Sobral, J. J. P. C. Rodrigues, R. A. L. Rabêlo, and P. Solic, “ERAOF: A new RPL protocol objective function for Internet of Things applications,” 2017 2nd Int. Multidiscip. Conf. Comput. Energy Sci. Split. 2017, no. Figure 1, pp. 1–5, 2017.
  16. 16.
    S. Hoghooghi and R. Javidan, “Proposing a new method for improving RPL to support mobility in the Internet of things,” IET J. Institiue Eng. Technol., vol. 9, no. 2, pp. 48–55, 2020, doi: 10.1049/iet-net.2019.0152.
  17. 17.
    H. Fotouhi, D. Moreira, and M. Alves, “MRPL: Boosting mobility in the Internet of Things,” Ad Hoc Networks, vol. 26, no. November, pp. 17–35, 2015, doi: 10.1016/j.adhoc.2014.10.009.
  18. 18.
    Y. Ben Aissa, H. Grichi, M. Khalgui, A. Koubaa, and A. Bachir, “QCOF: New RPL Extension for QoS and Congestion-Aware in Low Power and Lossy Network,” in Proceedings of the 14th International Conference on Software Technologies, 2019, no. Icsoft, pp. 560–569, doi: 10.5220/0007978805600569.
  19. 19.
    H. Pereira, G. L. Moritz, R. D. Souza, A. Munaretto, and M. Fonseca, “Increased Network Lifetime and Load Balancing Based on Network Interface Average Power Metric for RPL,” IEEE Access, vol. 8, pp. 48686–48696, 2020, doi: 10.1109/ACCESS.2020.2979834.
  20. 20.
    K. S. Bhandari and G. H. Cho, “Resource oriented topology construction to ensure high reliability in IoT based smart city networks,” Int. J. Syst. Assur. Eng. Manag., vol. 11, no. 4, pp. 798–805, 2020, doi: 10.1007/s13198-019-00861-2.
  21. 21.
    S. S. Solapure and H. H. Kenchannavar, “Design and analysis of RPL objective functions using variant routing metrics for IoT applications,” Wirel. Networks, vol. 26, no. 6, pp. 4637–4656, 2020, doi: 10.1007/s11276-020-02348-6.
  22. 22.
    H. S. Kim, J. Paek, D. E. Culler, and S. Bahk, “PC-RPL: Joint Control of Routing Topology and Transmission Power in Real Low-Power and Lossy Networks,” ACM Trans. Sens. Networks, vol. 16, no. 2, 2020, doi: 10.1145/3372026.
  23. 23.
    P. Singh and Y.-C. Chen, “RPL Enhancement for a Parent Selection Mechanism and an Efficient Objective Function,” IEEE Sens. J., vol. 19, no. 21, pp. 10054–10066, Nov. 2019, doi: 10.1109/JSEN.2019.2927498.
  24. 24.
    M. Pushpalatha, T. Anusha, T. Rama Rao, and R. Venkataraman, “L-RPL: RPL powered by laplacian energy for stable path selection during link failures in an Internet of Things network,” Comput. Networks, vol. 184, p. 107697, Jan. 2021, doi: 10.1016/j.comnet.2020.107697.
  25. 25.
    A. E. Hassani, A. Sahel, and A. Badri, “A new objective function based on additive combination of node and link metrics as a mechanism path selection for RPL protocol,” Int. J. Commun. Networks Inf. Secur., vol. 12, no. 1, pp. 63–68, 2020.
  26. 26.
    A. S. Kassab, K. G. Seddik, A. Elezabi, and A. Soltan, “Realistic Wireless Smart-Meter Network Optimization Using Composite RPL Metric,” 8th IEEE Int. Conf. Smart Grid, pp. 109–114, 2020.
  27. 27.
    J. Ramkumar, C. Kumuthini, B. Narasimhan, and S. Boopalan, “Energy Consumption Minimization in Cognitive Radio Mobile Ad-Hoc Networks using Enriched Ad-hoc On-demand Distance Vector Protocol,” 2022 Int. Conf. Adv. Comput. Technol. Appl., pp. 1–6, Mar. 2022, doi: 10.1109/ICACTA54488.2022.9752899.
  28. 28.
    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.
  29. 29.
    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.
  30. 30.
    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.
  31. 31.
    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.
  32. 32.
    J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2020, doi: http://dx.doi.org/10.12785/ijcds/100196.
  33. 33.
    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.
  34. 34.
    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.
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