# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 3 Background
### Overview of Mobility Models  
This study investigates the performance of proposed scenarios in a mobile environment with and without the Hello Flood attack using different group mobile models. In this context, mobility models play a crucial role, particularly in networks characterized by frequent topology changes, where they are applied in conjunction with the RPL protocol. This subsection provides a brief overview of the mobility models used in the research, emphasizing their impact on routing protocol performance through modeling and analysis. The group models include CMM, RPGM, Nomadic, and Pursue, providing a comprehensive exploration of various mobility scenarios and their effects on the RPL protocols. The Column Mobility Model (CMM) was initially developed to assess routing protocols in ad hoc network simulations. The CMM simulates a group of nodes moving within a column structure. Notably, in this model, the column itself can move forward. Implementing CMM requires an initial reference grid to determine the nodes' locations within each column. Each node is assigned a reference point (RP) along the column and can move randomly within a predefined maximum distance threshold (\(r_{max}\)) around this RP, adhering to a predefined mobility model. When the reference grid moves according to an Advanced Vector (AV), the mobile nodes move with it. Once the reference point reaches its destination, the mobile nodes resume roaming around their respective RPs. When the mobile nodes reach the boundaries of the simulation area, they reverse their direction by \(180^{\circ}\) and head back towards the center. The CMM finds application in specific search tasks like mine clearance or modeling organized military marches, where nodes (forming a column) move collectively towards a designated target [7]. On the other hand, the Reference Point Group Mobility Model (RPGM) simulates the collective movements of nodes within a terrain, akin to troop behavior in a battlefield scenario. In the RPGM, each node is assigned a reference point, and its movement is constrained within a maximum allowable distance deviation from this reference point, denoted as \(r_{max}\). The reference points of the nodes track the group's motion, following the movements of the logical center. However, each node can only move independently within a radius around its reference point. The groups' logical centers move along predefined paths using the RDM mobility model, which is limited by a specific velocity. These paths in the RPGM comprise multiple checkpoints, with the group's logical center transitioning between checkpoints within a designated time interval (\(\Delta\)t) [8]. The Nomadic Community Mobility Model (NCM) is a group-oriented model designed to replicate the movement patterns observed in a collective of nodes traveling together from one location to another, much like the migratory behavior seen in ancient nomadic societies transitioning between different environments throughout the year. This model shares similarities with the RPGM mobility model, where each member of the group moves towards a randomly selected destination within a circular range defined by a radius value denoted as \(r_{max}\). As a result, the NCM model is regarded as a specialized version of the RPGM model. In this model, each group is assigned a reference point, which all group members follow as they move randomly within a certain radius around it. Group members' mobility is independent and can be based on various entity mobility models chosen before the simulation begins, such as the Random Walk Model. The NCM model finds application in scenarios where a group of nodes needs to execute coordinated tasks across large areas comprising multiple subareas, such as scanning and search operations. It has practical utility in domains like agricultural robotics and various military operations [9]. Meanwhile, the Pursue Mobility Model (PMM) emulates the coordinated movement of multiple nodes as they track a designated target node.