RAARPL: End‐to‐end Reliability‐Aware Adaptive RPL routing protocol for Internet of things

Summary The RPL protocol has been accepted by the Internet Engineering Task Force as the only routing standard for the Internet of things (IoT). Although RPL has been significantly used in IoT routing, it still has extensive challenges. One of the most basic challenges is related to the reliability of routing. The severe limitations of resources, wireless communication, and variable topology, along with other limitations of IoT, have caused reliability in these networks to be a challenging topic. However, in RPL, no measures are provided to support the reliability of routing. To improve this issue, in this article, RPL was improved based on reliability requirements, and a new protocol was proposed called the Reliability‐Aware Adaptive RPL routing protocol (RAARPL). RAARPL selects the parents based on the evaluation of various criteria related to reliability and forms the network topology. In addition, the conditions of the paths were also considered in the decision‐making so that the reliability depends on the paths and parents. In order to maintain stability, RAARPL had controlled the parent selection and children assignment in a way that will prevent errors as much as possible. The simulation results by Cooja and compared with CLRPL and RPL protocols in various scenarios indicated the high efficiency of RAARPL in improving the reliability of data exchange and successful delivery ratio, reducing the instability of topology and the network throughput. The contributions of RAARPL protocol to improve RPL are as follows: Proposing the objective function of EERAOF to improve the selection of parents with the following characteristics: (a) evaluating by focusing on reliability of node, link, and rank; (b) considering path and parent conditions; and (c) anticipating measures to prevent momentary effects on the evaluation of the objective function (based on the coefficient proportional to the distance). Introducing a mechanism to predict and prevent errors based on traffic.