Secured location-aware mobility-enabled RPL

Although there have been many studies on the Internet of Things (IoT), there are still major challenges for IoT to become ubiquitous. So far, the mobility management challenge has not been addressed well. Routing Protocol for Low-Power and Lossy Networks (RPL), which is known as the de-facto for routing in IoT, does not support mobile nodes. Some studies have tried to address this challenge, but they either caused a very high Packet Loss Rate (PLR) or produced lots of control packets. Also, they have not considered the security aspects of their work which is crucial for real-world applications. In this study, a novel extension for the RPL called Secured Location-Aware Mobility-enabled RPL (SLM-RPL) is proposed to facilitate the mobility management of RPL while considering security precautions. From the mobility management point of view, according to extensive evaluations, SLM-RPL greatly reduces the hand-off delay and PLR compared to other mobility management schemes, even in big, dense, or highly dynamic networks. Therefore, SLM-RPL is shown to be the best option to be used in IoT applications, especially loss-sensitive ones. Also, SLM-RPL produces small numbers of control packets and has a low memory overhead. Moreover, from the security point of view, a probability-based method has been proposed and embedded in SLM-RPL, which is shown to be able to reduce the negative impacts of DODAG Information Solicitation (DIS) attacks by more than 99%. Also, a performable attack on SLM-RPL called False-Location-Injection (FLI) attack has been introduced, and a lightweight hybrid-structured Intrusion Detection System (IDS) has been provided to counter this attack as well as Sybil, Rank, Sinkhole, and impersonation attacks. The proposed IDS uses a voting-based approach which, when the ψ parameter is adjusted, can mitigate the impact of False-Reporting and collusion attacks. According to the evaluations, the proposed IDS can counter the mentioned attacks in the presence of Collusion attackers in different scenarios with Accuracy ≥0.99.