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Current technology on the use of fifth generation (5 G) networks relies on IPv6 routing protocol (RPL) for low-power and lossy networks (LLNs). However, the constrained-resource nature of Internet of things (IoT) devices for LLNs makes RPL limited in routing functions and in need of enhancements in its objective functions (OFs) when selecting preferred parents (PPs) among nodes for optimized routing decisions while satisfying varied IoT applications requirements. We explore the vast application areas of LLNs and advances made in supporting operating system platforms as well as RPL enhancements. We observed that recent studies focus more on routing optimization for PPs selection in LLNs and node density management under varying traffic load, targeting a diversity of IoT applications requirement. Strengths and weaknesses in metrics adopted by literature are presented with suggestions to overcoming identified challenges. Evidently, the lack of real-time data has greatly declined ground-truth verification of RPL metric(s), demanding intelligent techniques for improved performance and meaningful connectivity scale up. This work proposed an integrated machine learning (ML) framework for RPL functionalities enhancement in IoT-based networks. Findings from the review revealed that using ML techniques could facilitate the deployment of several desired parameters for significant LLNs performance improvements.

IPv6 Routing Protocol Enhancements over Low-power and Lossy Networks for IoT Applications: A Systematic Review