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

  • S. K. Sathya Lakshmi Preeth
  • R. Dhanalakshmi
  • R. KumarEmail author
  • Sangar Si
Original Research


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.


IoT Energy efficiency Reliability ACO Dynamic trickle algorithm RPL DODAG structure 



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • S. K. Sathya Lakshmi Preeth
    • 1
  • R. Dhanalakshmi
    • 1
  • R. Kumar
    • 1
    Email author
  • Sangar Si
    • 2
  1. 1.National Institute of TechnologyNagalandIndia
  2. 2.PSG College of TechnologyCoimbatoreIndia

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