# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 3 Related Works
The information helps each sink to make decisions toward scaling down its DODAG to address any performance issues. This is achieved by applying a maximum rank limit, which allows a sink to limit the broadcast of DIO messages to a certain topology level. In this case, only the recipients then maintain their DODAG attachments whereas other nodes detach to join other DODAGs. In [23], the dynamic split of a single DODAG topology into multiple DODAGs was proposed to overcome performance degradation. In response to any performance issue, the affected nodes send emergency requests. Once a response is received from a nearby emergency sink node deployed in the network, the split operation is initiated. The affected nodes detach from their current DODAG and join the DODAG of the responding sink nodes. As a result, a multi-DODAG topology is formed with separate sink nodes to address performance degradation. Multiple sink nodes then coexist without any coordination and thus there are no practical possibilities for topology aggregation once the situation is rectified. Partitioning the topology of a single DODAG into multiple interconnected network segments was also considered in [24]. This was proposed for addressing effective data management in large-scale IoT deployments. A different approach was adopted in [25] to enable the dynamic activation and deactivation of multiple sink nodes in a single DODAG topology. It was based on having certain non-sink RPL nodes in the topology to serve as on-demand sink nodes in the case of performance degradation. This process is managed in a dynamic way to adjust the number of sink nodes in the network as needed. It defined three decision schemes: autonomous, delegated, and centralized. The first and second ones allow each sink to make its own decisions and the decisions of the sinks in its sub-DODAG, respectively. The third scheme requires a central decision-making node. Moreover, there have been practical considerations towards introducing specific OF designs for effective routing in multi-instance and multi-DODAG setups. Examples are QAD-OF [17], OFQS [18], Multi-OF [26], and MEHOF [27]. These OFs provide RPL networks with the ability to apply different routing optimization objectives for complex RPL setups in a managed and flexible manner. It is evident that customizing OF development to address effective routing in multi-instance and multi-DODAG topologies is a practical consideration. It has also been shown that effective multi-instance RPL support can be leveraged to address efficient networking solutions for different IoT applications. Examples are a smart grid [18], smart farming [24], and e-healthcare monitoring [28]. This was also successfully adopted for other variants of IoT. These include multimedia-IoT [29] and industrial-IoT [30]. However, a different approach is considered in this work to address more dynamic topology management of RPL networks. Addressing such support in the literature was limited to either multi-instance [17; 18; 19; 20; 21] or multi-DODAG management [22; 23; 24; 25]. Most of the proposed solutions focused on dynamic DODAG partitioning to address network situations without enabling flexible DODAG aggregation [17; 18; 19; 20; 21; 22; 23; 24]. This proposed work introduces more flexibility and dynamicity to the network topology. It leverages the multi-instance RPL support to enable dynamic adjustments for single- and multi-DODAG topologies during adverse network conditions. DODAG partitioning and aggregation are both supported in a dynamic and distributed manner. It also incorporates customized OF, an improved parent selection strategy, and simple multi-DODAG inter-routing support.