Skip to main content
Log in

Efficient parent selection for RPL using ACO and coverage based dynamic trickle techniques

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Routing protocol for low-power and lossy networks (RPL) plays a vital role in the architecture of the IoT. The RPL follows the trickle algorithm and the distance based parent node selection. The trickle algorithm establishes a destination oriented directed acyclic graph (DODAG) with suppressed broadcasting. The broadcast suppression lacks in handling network coverage and load imbalance issues under the nonuniform node distribution. In addition, the DODAG construction exclusively depending on a single routing metric to identify an energy efficient and reliable routing paths. This article proposes an energy efficient RPL (E-RPL) protocol that consists of ACO based multi-factor optimization for parent selection and coverage based dynamic trickle algorithm for energy efficient DODAG construction without compromising network coverage and reliable data routing. The ACO considers the expected transmission count (ETX) and rank value as pheromone factors, whereas the residual energy and children count as heuristic factors. To balance the conflicting factors of ETX, rank, delay, and energy consumption, the E-RPL exploits the parent–child relationship factor as a pheromone evaporation factor. Moreover, the weight based algorithm is utilized to combine pheromone, heuristic, and pheromone evaporation factors towards a single objective function. To build an optimal DODAG structure with a reduced routing overhead, the E-RPL introduces concentric corona based network partition and decides the value of broadcast count dynamically concerning the node density and coverage. Finally, the performance of E-RPL is evaluated using Cooja simulator. The E-RPL attains 90% of the packet delivery ratio while spending 0.532 mJ over 30 node topology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  • Bani Yassein M, Aljawarneh S, Masa’deh E, Ghaleb B, Masa’deh R (2016) A new dynamic trickle algorithm for low power and lossy networks. In: International conference on engineering and MIS (ICEMIS)

  • Barbato A, Barrano M, Capone A, Figiani N (2013) Resource oriented and energy efficient routing protocol for ipv6 wireless sensor networks. IEEE GreenCom 2013:163–168

    Google Scholar 

  • Brachman A (2013) RPL objective function impact on LLNs topology and performance. Internet Things Smart Sp Next Gener Netw 8121:340–351

    Article  Google Scholar 

  • Djamaa B, Richardson M (2015) The trickle algorithm: issues and solutions

  • Djamaa B, Yachir A, Richardson M (2017) Hybrid CoAP-based resource discovery for the Internet of Things. J Ambient Intell Human Comput 8(3):357–372

    Article  Google Scholar 

  • Evans D (2011) The internet of things: how the next evolution of the internet is changing everything CISCO white paper

  • Gaddour O, Koubaa A, Baccour N, Abid M (2014a) OF-FL: QoS-aware fuzzy logic objective function for the RPL routing protocol. In: 12th international symposium on in: modeling and optimization in mobile, ad hoc, and wireless Networks (WiOpt), pp 365–372

  • Gaddour O, Koubaa A, Rangarajan R, Cheikhrouhou O, Tovar E, Abid M (2014b) Co-RPL: RPL routing for mobile low power wireless sensor networks using corona mechanism. In: 9th IEEE international symposium on industrial embedded systems (SIES), pp 200–209

  • Gaddour O, Koubaa A, Abid M (2015) Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL. Adhoc Netw 33:233–256

    Google Scholar 

  • Ghaleb B et al (2016) Trickle-plus: elastic trickle algorithm for low-power networks and internet of things. In: IEEE wireless communications and networking conference workshops (WCNCW)

  • Gonizzi P, Monica R, Ferrari G (2013) Design and evaluation of a delay-efficient RPL routing metric. 9th international IEEE conference (IWCMC) on wireless communications and mobile computing, pp 1573–1577

  • Herberg U, Clausen T (2011) Study of multipoint-to-point and broadcast traffic performance in the “IPv6 routing protocol for low power and lossy networks”. J Ambient Intell Human Comput 2(4):293–305

    Article  Google Scholar 

  • Iova O, Theoleyre F, Noel T (2015) Using multiparent routing in {RPL} to increase the stability and the lifetime of the network. Ad Hoc Netw 29:45–62

    Article  Google Scholar 

  • Kadri B, Feham M, Mhammed A (2014) Efficient and secured ant routing algorithm for wireless sensor networks. Int J Netw Secur 16(2):149–156

    Google Scholar 

  • Kamgueu PO, Nataf E, Djotio T, Festor O (2013) Energy based routing metric for RPL. Research Rzport, p 14, hal-00779519, [Online]. http://hal.inria.fr/hal-00779519

  • Karkazis P, Leligou HC, Trakadas P, Sarakis L, Velivassaki TH, Capsalis C (2012) Design of primary and composite routing metrics for RPL-compliant wireless sensor networks. In: Telecommunications and multimedia (TEMU)

  • Karkazis P et al (2013) Evaluating routing metric composition approaches for QoS differentiation in low power and lossy networks. Wirel Netw 19(6):1269–1284

    Article  Google Scholar 

  • Lewis P, Clausen TH, Hui J et al (2011) The trickle algorithm. IETF RFC 6206. http://www.rfc-editor.org/ rfc/rfc6206.txt

  • Ma D, Ma J, Cheng L, Xu P (2014) An adaptive virtual area partition clustering routing protocol using ant colony optimization for wireless sensor networks. In: Advances in wireless sensor networks. Springer, Berlin, pp 23–30

    Chapter  Google Scholar 

  • Negash B, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2016) LISA 2.0: lightweight internet of things service bus architecture using node-centric networking. J Ambient Intell Human Comput 7(3):305–319

    Article  Google Scholar 

  • Olfa G, Koubâa A (2012) RPL in a nutshell: a survey. Comput Netw 56(14):3163–3178,

    Article  Google Scholar 

  • Ortiz AM, Royo F, Olivares T, Castillo JC, Orozco-Barbosa L, Marron PJ (2013) Fuzzy-logic based routing for dense wireless sensor networks. Telecommun Syst 52(4):2687–2697

    Article  Google Scholar 

  • Sahani SK, Kumar K (2013) Multi-routing in wireless sensor networks using an ant colony optimization (ACO). Int J Comput Netwo Wirel Mob Commun 3(3):87–98

    MathSciNet  Google Scholar 

  • Said O (2017) Analysis, design and simulation of internet of thing routing algorithm based on ant colony optimization. Int J Commun Syst 30:8

    Article  Google Scholar 

  • Tan L, Wang N (2010) Future internet: the Internet of Things. In: 3rd international conference on advanced computer theory and engineering (ICACTE), Chengdu, China, pp 376–380

  • Vučinić M, Tourancheau B, Duda A (2013) Performance comparison of the RPL and LOADng routing protocols in a home automation scenario. IEEE conference on wireless communications and networking (WCNC), pp 1974–1979

  • Yang X, Guo J, Orlik P, Parsons K, Ishibashi K (2014) Stability metric based routing protocol for lo4-power and lossy networks. IEEE ICC, pp 3688–3693

  • Yassein MB, Aljawarneh S (2017) A new elastic trickle timer algorithm for Internet of Things. J Netw Comput Appl 89:38–47

    Article  Google Scholar 

  • Zhang T, Li X (2014) Evaluating and analyzing the performance of RPL in Contiki. ACM proceedings of the first international workshop on mobile sensing, computing and communication, pp 19–24

  • Zhao M, Chong PHJ, Chan HCB (2017) An energy-efficient and cluster-parent based RPL with power-level refinement for low-power and lossy networks. Comput Commun 104:17–33

    Article  Google Scholar 

  • Zungeru AM, Ang LM, Seng KP (2012) Classical and swarm intelligence based routing protocols for wireless sensor networks: a survey and comparison. J Netw Comput Appl 35(5):1508–1536

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Kumar.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Preeth, S.K.S.L., Dhanalakshmi, R., Kumar, R. et al. Efficient parent selection for RPL using ACO and coverage based dynamic trickle techniques. J Ambient Intell Human Comput 11, 4377–4391 (2020). https://doi.org/10.1007/s12652-019-01181-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01181-w

Keywords

Navigation