# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 2 Related Work
### Critical Analysis and Research Gaps  
Table 1 is a summary of different state-of-the-art approaches. It shows whether the mobility of the network has been catered for. A \(\check{\mathsf{\mathsf{\mathsf{V}}}}\) represents the corresponding feature is present or high (e.g., a \(\check{\mathsf{\mathsf{\mathsf{V}}}}\) for message overhead means it is high and a \(\check{\mathsf{\mathsf{\mathsf{V}}}}\) for mobility means it is addressed), and a \(\times\) represents a low or the absence of a feature (e.g., a \(\times\) for message overhead means it is low and a \(\times\) for mobility means it is not addressed). Similarly, it compares energy, message, and computational overheads. It also compares network performance and network lifetime with those achieved by the proposed approach. Only [27; 28] considered node and environment mobility. References [25; 27; 28] proposed an energy-efficient trust mechanism in which most of the calculations are done by the root node. However, the authors in [25] used homomorphic encryption, which increased the computational overhead on nodes during decryption. The message overhead is also higher because no effective transmission method was used from the root to a node or vice versa. Similarly, the authors in [27; 28] calculated the trust at the node level, but there was no mechanism for exchanging messages, resulting in unwanted message and computational overheads. These state-of-the-art approaches do not minimize the message overhead while detecting and isolating internal attacks and performing trust-related calculations by device layers. The studies in [25; 27; 28] ensured that the network lifetime was long by improving energy efficiency. This analysis shows that most state-of-the-art approaches aim to reduce energy consumption and increase network lifetime. However, they ignore traffic congestion (i.e., number of messages exchanged) and computational overheads, which are the main causes of low network performance. Mobility is also a crucial feature of LLN-based IoT networks [31], but only a few methods cater for it. For a network, its lifetime is considered to end at the death of the first node. To increase network lifetime, energy must be conserved by nodes. For LLNs, therefore, there is a need to design a mechanism that minimizes energy consumption at the node level. Moreover, as well as messages containing sensed data, in a trust-based mechanism, trust-related data are also exchanged between nodes or sent to a central entity in a multi-hop fashion, causing unwanted traffic and leading to congestion. This increases the message overhead, which must be reduced to improve network performance and reduce delays and the packet loss ratio. To overcome these issues and limitations, this research introduces MMTM-RPL, which aims to improve the security of RPL-based LLNs. It maximizes network lifetime by conserving energy at the node level and improves network performance by minimizing network congestion at both node and channel levels. All the complex trust-related computations and storage are done at the fog or control layer to conserve resources. To reduce network congestion caused by exchanging trust parameters, MMTM-RPL uses MAs, which were recently employed in wireless sensor networks to conserve energy and improve data collection [32]. The basic purpose of data aggregation is to capture, gather,  
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