Securing RPL Networks with Enhanced Routing Efficiency with Congestion Prediction and Load Balancing Strategy

Low power and Lossy Networks (LLNs) are essential components of the Internet of Things (IoT) environment. In LNNs, the Routing Protocol for LLN (RPL)-based Internet Protocol Version 6 (IPv6) routing protocol is regarded as a standardized solution. However, the existing models did not account for the issues with congestion and security when modeling the RPL. Thus, to resolve these issues, this paper proposes a novel Exponential Poisson Distribution–Fuzzy (EPD-Fuzzy) model and Kullback Leibler Divergence-based Tunicate Swarm Algorithm (KLD-TSA) for developing a reliable RPL model. The hash codes are first generated for the registered nodes at the network end in order to achieve security; the hash codes are subsequently compared via requests with the immediate nodes. Each node sends a request to its neighbors using the hash value; if the hash value matches, a path is formed. The parent nodes are then chosen and ranked using the Pearson Correlation Coefficient-Spotted Hyena Optimization Algorithm (PCC-SHOA) technique to minimize latency. To avoid congestion, the EPD-Fuzzy is employed to predict congestion; then, a genitor node is introduced in the congested scenarios. The big data and videos are split, compressed, and sent via multiple paths to reduce the losses in the RPL. Moreover, to avoid network traffic, a novel KLD-TSA load balancing is introduced at the user end. The experiential outcomes exhibited the proposed technique’s effectiveness regarding Packet delivery ratio (PDR).

@article{Raj2024,
  title     = {Securing RPL Networks with Enhanced Routing Efficiency with Congestion Prediction and Load Balancing Strategy},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Saumya Raj and Rajesh R},
  doi       = {10.14569/IJACSA.2024.0150894},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150894}
}