An Analysis Of The Unreliability Problems In RPL Routing Protocol

Low-Power and Lossy Networks(LLN) widely exist in scenarios related to building automation, urban sensor networks, home automation and industrial automation. The links in LLN are typically characterized by high loss rates, low data rates, and instability. And LLN routers typically operate with constraints on processing power, memory, and energy(battery power). To meet the requirement of LLN, The IETF(Internet Engineering Task Force) defined a routing protocol called RPL(The IPv6 Routing Protocol for Low-Power and Lossy Networks).In this paper, we investigate the performance of RPL. We find that there are three problems when RPL running with ND(Neighbor Discovery) and using the link metric such as ETX. First, a node cannot detect the link information to all of its neighbors. Second, loops may occur in some situation. Finally, lack of a mechanism to refresh the life time of a neighbor.To solve these problems, we come up with three mechanisms in this paper. They are Active Neighbor Detection, Loop avoidance, and Neighbor Life Time Refresh mechanism. We use a simulation tool called Cooja in Contiki operation system to do the simulation tests, in order to verify their performance.The simulation results show that Loop Avoidance mechanism and the Neighbor Life Time Refresh mechanism can enhance the performance of the entire network significantly. Especially when use these two mechanisms together, the performance of entire network is the best. These two mechanisms can effectively improve the reliability of RPL.In situations where the number of nodes is not too many and the density of nodes is not too high. Active Neighbor Detection mechanism can significantly improve network performance. But because it adds an additional protocol overhead, in a scenario where the number of nodes is larger, and the density of nodes is higher, it makes network performance degradation. However, with the Loop Avoidance and Neighbor Life Time Refresh mechanism used simultaneously under the circumstances, the negative impact of the Active Neighbor Detection mechanism to the network can be reduced.