# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 8 Conclusions  
This study presents a thorough evaluation of the RPL protocol's performance across diverse scenarios, highlighting its challenges in adapting to dynamic network topologies and mobility constraints. Specifically, we focused on assessing RPL's behavior in mobile environments, utilizing four group mobility models: the Column Mobility Model (CMM), Reference Point Group Mobility Model (RPGM), Nomadic Community Mobility Model (NCM), and Pursue Mobility Model (PMM). We conducted our analysis under two conditions: with and without the Hello Flood attack, to understand its impact on RPL's performance. Our investigation involved varying node quantities and the percentage of malicious nodes in the network, evaluated through key metrics such as Average End-to-End Delay (AE2 ED), throughput, the Packet Delivery Ratio (PDR), Expected Transmission Count (ETX), and Average Power Consumption (APC). Without the attack, the CMM demonstrates robust data delivery and transmission rates, although it may experience increased delays and transmission counts in complex scenarios. The RPGM offers a balanced realism but may be vulnerable to attacks, while the NCM reflects migratory behavior effectively but faces scalability issues. The PMM is suitable for tracking moving targets but may introduce overhead. In the presence of a Hello Flood attack across various group mobility models, notable shifts occur in network performance metrics. Firstly, the Packet Delivery Ratio (PDR) diminishes, indicating heightened packet loss or delivery failures. Expected Transmission Count (ETX) values rise, necessitating increased packet retransmissions and routing hops due to attack-induced disruptions. End-to-End Delay (E2 ED) increases, introducing delays in routing decisions and data transmission times. Throughput declines as the attack disrupts data flow, leading to more packet loss and retransmissions. Moreover, Average Power Consumption escalates due to increased energy usage on packet transmissions, particularly over extended paths. These findings underscore the disruptive nature of Hello Flood attacks and highlight the urgent need for robust security measures in dynamic and mobile networks to mitigate such disruptions effectively. In summary, this study provides crucial insights into the challenges faced by RPL-based IoT networks and the tangible impact of Hello Flood attacks, essential for devising protective measures and ensuring network resilience against evolving threats. Our future objective is to leverage these findings to develop an anomaly intrusion detection system tailored for mobile environments. This system will continuously monitor specific performance parameters sensitive to internal attacks, enhancing network security and resilience.  
The authors confirm contributions to the paper as follows: study concept, data analysis, and writing: A.H.; writing--review and editing: S.A., O.B.B. and M.K.; draft manuscript preparation: H.F. and M.M. All authors have read and agreed to the published version of the manuscript.  
The authors received no specific funding for this study.  
All authors confirm that accepted principles of ethical and professional conduct have been followed. Additionally, this article does not contain any studies with human participants or animals performed by any of the authors.  
All data generated or analyzed during this study are included in this article and are available from the corresponding authors upon reasonable request.  
The authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University, for funding this work.  
The authors declare no conflict of interest.