The Internet of Things (IoT) has been the subject of recent studies and is expected to be extremely important to the future growth of the Internet. The essential component of the IoT is the low-power and lossy networks (LLNs), which are necessary to connect hundreds or even thousands of devices. The IPv6 Routing Protocol for LLNs (RPL), the standard routing protocol for IoT, uses a variety of routing metrics to construct a destination-oriented directed acyclic graph (DODAG), which helps in selecting the Objective Function (OF). To designate the most efficient route to the destination, it must choose the best parent of the nodes. The two OFs used by the standard RPL have drawbacks as they only employ a single metric. As a result, several studies reveal RPL limitations, and just a few IoT applications gain from it. Therefore, in this paper, we will go over the drawbacks of using a single metric and the downsides of utilizing a composite metric by proposing an equilibrium optimizer-RPL (EO-RPL) protocol that selects the preferred parent using a novel optimization method known as an EO algorithm, which relies on a composite metric. The simulation results demonstrate that our novel OF outperforms its counterparts in terms of Packet Delivery Ratio (PDR), control overhead, end-to-end delay, and energy consumption. The results indicate that the average improvement ratios of the PDR of EO-RPL regarding Energy-based Path Cost-RPL (EPC-RPL), Energy Efficient RPL (E-RPL), and Energy Efficient Optimal Parent Selection-RPL (EEOPS-RPL) are 66.9%, 10.3%, and 8.3%, respectively. For control overhead, the improvement ratios of EO-RPL in comparison with EPC-RPL, E-RPL, and EEOPS-RPL are 37.7%, 22%, and 12.4%, respectively. Not only that but, the improvement ratios with regard to the end-to-end delay of EO-RPL are, in relation to EPC-RPL, E-RPL, and EEOPS-RPL, 77.06%, 45.6%, and 32.46%, correspondingly. Finally, for EPC-RPL, E-RPL, and EEOPS-RPL, the improvement ratios of EO-RPL are 16.09%, 14.1%, and 12.12% for energy consumption, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
