# Study of Mobility Enhancements for RPL in Convergecast Scenarios
## 3 Related Work  
[PERSON] et al. [6] proposed a mobility-enabled RPL protocol to enhance the hand-off process, optimize control messages and improve best parent selection scheme. In their context, they considered that mobile nodes and static nodes co-exist within the network. In order to make reliable transmissions, they use different timers to manage the Neighbourhood Discovery (ND) process and apply the average received signal strength indicator (RSSI) value to identify link quality. Within a certain duration, if a robust link is found by a mobile node, the best transmission route will be constructed, otherwise the ND process will be initiated. When mobile nodes detect no packet received within a certain duration, a DIS will be broadcast to call responses of neighbour nodes. Based on the average RSSI value of received DIOs, mobile nodes detect a best route, otherwise the ND process will be initiated. In order to avoid loops, they set the rank of mobile nodes as infinity rank in order to solve the rank problem when nodes move far away from the sink. However, infinity rank means this mobile cannot be selected by other nodes as a next-hop, which will cause network congestion, packet loss and disconnected nodes when there are many mobile nodes in the network. [PERSON] et al. [7] proposed an adaptive timer algorithm in RPL called modified RPL (mod-RPL) to enhance the trickle timer to fit with mobility requirements. It takes into consideration the random trajectory of mobile nodes, pause time and the velocity of nodes. Each mobile node knows its own position and based on the position information, the minimum time to leave (TL), which represents the minimum time to move out the radio range of its preferred parent, can be computed. Based on TL, if an inconsistency is detected, mobile nodes will reset the trickle timer. If a mobile node is suspected to disconnect, an immediate DIS will be broadcast. However, position information is not accessible without a GPS (Global Positioning System) system and the authors did not mention the way how they obtained position information. Moreover, if the preferred parent of the mobile node is mobile as well, this algorithm cannot calculate an accurate TL due to the unknown movement direction of the mobile preferred parent. [PERSON] et al. [8] proposed the Bayesian model Mobility Prediction RPL (BMP-RPL) aiming to adapt native RPL to mobile scenarios. They used a Bayesian model for mobility prediction, which is proposedin [9], to estimate the nodes connecting period according to a velocity probability distributions. This model estimates a link duration between two nodes and introduces it as a new link quality metric. They proposed a new control message Hello protocol to broadcast link information. However, the degree of accuracy of prediction depends on the velocity distribution. Being able to predict the movement of all nodes in the network is something very rare and only few applications would be able to provide this information. In addition, BMP does not consider path loss, which is another influencing factor on connecting duration, in its model. Furthermore, it is challenging to implement a Bayesian model for mobility prediction in LLNs considering sensor node constraints in terms of CPU (Central Processing Unit) and energy. [PERSON] et al. [10] proposed Co-RPL based on the Corona mechanism to support mobility in RPL. In order to be aware of the movement of the nodes in real-time, they proposed that the DAG root send DIOs periodically. They divided the network into different circular areas and each area is called a Corona. The Corona ID (C_ID) is contained in DIOs in order to identify different Coronas. Each node selects the best parent that has the minimum C_ID and updates its own C_ID by incrementing the minimum C_ID. However, the C_ID updating method is incomplete due to directly discarding DIOs from higher C_ID values, since there is a possibility that a node with lower C_ID will move into a section in which all the nodes have higher C_ID.