# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 5 Discussion
#### Answers to Research Questions  
Next, we provide answers to the RQs:  
Figure 10: Overview of results for the different frameworks: (**a**) Network lifetime, (**b**) Average residual energy, (**c**) Control message overhead, (**d**) Attack detection rate, (**e**) Attack detection time, (**f**) End-to-end delay.  
* How can security in RPL be improved to mitigate internal attacks while maintaining the mobility of the network?
* In resource-constrained IoT devices, RPL plays an important role in communication and message passing. Nevertheless, it needs to be improved to provide security against potential internal attacks. Furthermore, in delay-sensitive IoT infrastructures, the timely and accurate detection of internal attacks is challenging. Therefore, MMTM-RPL enhances the efficiency and effectiveness of internal attack detection to improve the security of RPL-based frameworks, as shown in Figure 10a,d. The overall attack detection time is shorter compared to state-of-the-art approaches, as shown in Figure 10e. This is achieved by shipping complex computations to the fog layer to conserve the limited memory, energy, and computational resources. Although the end-to-end delay is slightly higher, the increase is negligible, as shown in Figure 10f.
* How can we ensure that data are gathered securely to protect RPL-based networks against internal attacks?
* The literature review indicated that most state-of-the-art approaches do not consider network mobility, which is an essential feature of LLNs with IoT devices. Apart from minimizing the message, energy, and computational overheads, it is equally important to ensure data are gathered securely by the RPL infrastructure. Most LLN-based infrastructures use wireless communications for exchanging data, which are vulnerable to certain types of breaches [18]. In a trust-based mechanism, trust-related data (trust parameters) are important and must be secured. The integrity of trust is very much based upon the integrity of the trust parameters. Therefore, by authenticating the MAs sent to collect trust parameters, MMTM-RPL ensures that data are gathered securely so that trust values are effective and reliable. Therefore, it meets the mobile and dynamic requirements of RPL-based networks while securing them against internal attacks. This is achieved by generating new itineraries and initiating new MAs whenever a topology change occurs. Thus, the trust is calculated after each change as well as at fixed time intervals.
* In support of RQ1, can we minimize network congestion (overhead due to the number of messages exchanged) to improve network lifetime and minimize energy, memory, and computational overheads at the device level in RPL-based infrastructures?
* In RPL-based IoT infrastructures, the data packets are sent in a multi-hop fashion. Due to the limited buffer size and bandwidth, exchanging massive amounts of data may increase network congestion and data-packet loss. To inoculate the network against internal attacks, apart from the data generated and exchanged by nodes, trust parameters are exchanged for trust assessment using the same channels. This increases the underlying channel load and also the data-packet loss rate and latency. However, the scope of this study is to minimize trust-related data congestion to support RPL only. To the best of our knowledge, in RPL-based trust mechanisms, trust-related data are either exchanged among nodes or routed toward the central entity in a multi-hop fashion, increasing the message overhead. Therefore, there is a dire need to minimize the amount of multi-hop trust-related data that is gathered, which MMTM-RPL achieves with MAs. These collect trust-related data from each node listed in their itineraries and give the data to the control layer. In this way, compared to state-of-the-art frameworks, the message overhead is reduced, as depicted in Figure 10c, and the energy is conserved at the node level, as shown in Figure 10b.