# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Column Mobility Model (CMM)
#### 6.1.2 End-to-End Delay  
Figure 3 provides a comprehensive analysis of E2 ED in a mobile environment using the CMM, comparing scenarios with and without the Hello Flood attack. The \(x\)-axis depicts the number of nodes, ranging from 0 to 60, while the \(y\)-axis represents E2 ED in milliseconds (ms), spanning from 0 to 2 ms. Distinct lines, color-coded for clarity and  
Figure 2: PDR without and with attack using CMM.  
labeled with corresponding legends, illustrate different percentages (10%, 20%, 30%, 40%) of mobile nodes under two conditions: the mobile environment without the Hello Flood attack and with the attack. The higher E2 ED shown by lines representing \"attacker nodes\" compared to those representing \"mobile nodes\" at similar percentages of malicious nodes is a consistent finding. This shows that the Hello Flood attack has a negative effect on network performance, especially when it comes to delay. Notably, as the number of nodes increases, there is a noticeable trend of escalating E2E Delay, particularly pronounced under the \"attacker\" condition. In scenarios devoid of malicious nodes, E2 ED escalates with an increasing number of nodes, peaking at its highest value with 60 nodes, where 40% of nodes are mobile. This finding underscores the disruptive influence of the attack on data transmission efficiency. Overall, the graph provides valuable insights into the interplay between the CMM mobility model and the Hello Flood attack on E2 ED in mobile networks, emphasizing the critical consideration of both mobility patterns and security threats in evaluating network performance.  
Through a variety of mechanisms, the CMM exerts a notable influence on E2 ED within network environments. Structured mobility patterns introduced by the CMM, such as nodes moving in columns or groups, can lead to congestion in specific network areas where nodes cluster or compete for communication channels. This congestion prolongs the transmission time for data packets, contributing to increased E2 ED. Furthermore, the constant change that comes with the CMM causes frequent changes in the structure of the network, which requires constant route recalculations and makes routing paths less stable. Consequently, this routing instability elongates the time taken for data packets to reach their destinations, further elevating E2 ED. Additionally, as nodes converge within columns, resource utilization escalates, affecting network bandwidth and processing power. Heightened resource consumption lengthens queuing times for data packets and amplifies delays in packet transmission, augmenting E2 ED. Moreover, the boundary handling mechanisms employed by the CMM, where nodes reverse direction or wrap around simulation area boundaries, introduce additional delays in packet transmission, particularly as nodes navigate boundary transitions. In conjunction with the CMM, the Hello Flood attack intensifies E2 ED by flooding the network with excessive traffic, saturating communication channels, and depleting network resources. This inundation amplifies congestion and contention for resources, prolonging packet transmission times and increasing E2 ED. Additionally, the attack disrupts routing protocols and introduces instability, further imp  
Figure 3: E2 ED without and with attack using CMM.