# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 2 Related Work
Simulation experiments are conducted, validating the effectiveness of the proposed mechanism in enhancing the information security in the network, and in defending against the internal attacks that originate from those nodes that are compromised. The reliance on beta distribution modeling requires extensive monitoring and trust calculations, which can lead to increased computational and communication overhead. Additionally, the approach may struggle in dynamic or highly mobile OppNet environments where node behaviors change rapidly, potentially affecting the accuracy of trust evaluations. In [8], [PERSON] et al. proposed a routing method for OppNets, which also secures the network against dropping and Sybil attacks. Its design consists of a trust structure for ensuring the confidentiality of messages, a shared public-key approach for data integrity and privacy protection purposes, and a cooperative mechanism to resist selfish nodes. Through experiments, the proposed scheme is shown to protect against selective dropping attacks and Sybil attacks while yielding a better performance in terms of message delivery ratio and average latency compared to that yielded by selected benchmark schemes. The reliance on a shared public-key approach and cooperative mechanisms may introduce computational overhead and increase energy consumption, making it less suitable for resource-constrained devices. Additionally, the method's performance in highly dynamic or large-scale OppNet environments may require further evaluation to ensure scalability and adaptability. In [PERSON] et al. (2019), [PERSON] et al. proposed a dynamic trust framework for opportunistic mobile social networks, which enable a given node to determine the trust of another node by relying on the behavior of the latter. Such a trust model is designed using a two-hop feedback method based on three parameters, namely fitness, connectedness, and satisfaction, which help quantify the honesty of a node. Through simulations, the proposed trust architecture is shown to effectively identify abnormal nodes, as well as nodes that have launched conspiracy attacks. The reliance on indirect feedback and predefined parameters may reduce its accuracy in rapidly changing network conditions or environments with sparse connectivity. Additionally, the computational complexity of the two-hop feedback mechanism could be a limitation for nodes with constrained resources. In [PERSON] et al. (2019), [PERSON] et al. proposed a trust-based secure routing algorithm for OppNets, in which a trust value for each node is calculated by gathering the message's delay to the destination node and a packet-forwarding evidence chain. The values are maintained locally by each node in a vector table. This vector table is then broadcasted periodically, along with a signature and timestamp, leading to the construction of a trust routing table that is hosted at each node, which then helps in determining a suitable next-hop relay node to forward the message toward its intended destination node. Experiments are conducted, showing the effectiveness of the proposed algorithm in terms of delivery probability ratio. The periodic broadcasting of vector tables with signatures and timestamps increases communication overhead, potentially leading to higher energy consumption and network congestion in resource-constrained environments. Furthermore, the reliance on locally maintained trust values may limit the algorithm's adaptability in highly dynamic or large-scale OppNet scenarios. In [PERSON] et al. (2019), [PERSON] et al. proposed a trust-based routing scheme for OppNets, where the trust value of a node is calculated by combining Direct and Indirect Trust. Low-trust nodes are then filtered and excluded from participation in the routing process. The considered trust model employs pruning and filtering processes to eliminate harmful proposals. Additionally, by merging the node trust values with the expected transmission count data, an opportunistic routing strategy based on the aforementioned trust model is proposed.