Towards Skewness and Balancing of RPL Trees for the Internet of Things

TL;DR

This paper evaluates the skewness of RPL routing trees in IoT networks and introduces a new protocol to improve tree balance, addressing a key challenge in large-scale low-power networks.

Contribution

The paper provides the first comprehensive analysis of RPL tree skewness and proposes a novel routing protocol to enhance balance in IoT networks.

Findings

RPL trees exhibit severe skewness across various metrics.

Existing routing metrics do not prevent tree imbalance.

The proposed protocol significantly improves tree balance.

Abstract

In many application areas such as large-scale disaster detection, IoT networks connote the characteristics of LLN (Low power and Lossy Network). With few exceptions, prior work on RPL(Routing Protocol for LLN), a standard routing protocol standardized in the IETF, has focused on the evaluation of various aspects of routing performances and control overheads. In this paper, we address the problem of DODAG (Destination Oriented Directed Acyclic Graph) created by the direct application of RPL. We first evaluate the skewness of DODAG both via numerical simulations and via actual large-scale testbed. RPL secures its flexibility and wide applicability by allowing the adoption of implementer-specific rank definitions and parent selection criteria. In addition to the metrics used in ContikiRPL and TinyRPL, the two most widely used open source implementations, we evaluated the skewness of RPL trees generated by applying various routing metrics. Performance analysis results show that RPL trees suffer from severe skewness regardless of routing metrics in both randomly generated networks and in real-world networks. We propose a novel routing protocol that may improve the balance of RPL trees. Rigorous performance analysis based on computer simulations shows that our algorithm improves the tree balance significantly.