# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 3 Related Work
However, the effectiveness of the KNN algorithm can be limited by its sensitivity to the choice of k and its performance in high-dimensional spaces, which might not adequately reflect the dynamic and complex networks. Within the same context, two trust evaluation models presented in [25; 26] introduced several metrics to weight recommendations, defined as Familiarity (how well a one-hop neighbour is acquainted with the targeted vehicle), Similarity (degree of similar content),and Timeliness (the freshness of any reputation segment). Similarly, [PERSON] et al. [27] present a recommendation-based trust model designed to address the challenges of maintaining reliable packet delivery through multi-hop intermediate nodes in mobile ad hoc networks (MANETs). The proposed model introduces a defence scheme that employs a clustering technique to dynamically filter out dishonest recommendations from nodes, which may attempt to deceive the trust management system through bad-mouthing, ballot stuffing, and collusion. The core of the model is evaluating the honesty of recommending nodes based on three key factors: confidence based on the interactions, the compatibility of information (assessed through deviation tests), and the physical closeness between nodes. [PERSON] et al. [28] introduce a belief function within the CTRUST model to assess the credibility of recommendations in collaborative IoT applications. This function is not designed to diminish trust in the recommendations but rather to modulate the influence of their recommendations based on temporal and relational dynamics. The belief function is mathematically derived from several components: the decay of trust over time, the existing trust scores between the recommender and the trustor, and the magnitude of change proposed by new recommendations compared to existing trust levels. The operational principle of this belief function is that it assigns minimal weight to recommendations when there are recent and direct observations that contradict these recommendations, thereby prioritizing empirical experience over hearsay. However, a potential issue arises when the calculated belief value becomes negative due to a large discrepancy between the trust scores provided by the recommender and the existing trust scores of the trustor. This situation is not explicitly addressed in the paper. Few attempts have been proposed to address the issue of dishonest recommendations in underwater networks. For instance, the work in [16] introduces a trust model named CATM for IoUT. In this model, they propose a mechanism to evaluate recommendations based on factors like link stability and node reliability. These measurements failed to capture the dishonest recommendation attacks like bad-mouthing or ballot stuffing. In [29], a cluster-based trust model is introduced where recommendations are computed by the cluster head. Each sensor sends sensory data to the cluster head, which computes trust based on the assumption that data follow a normal distribution. Trust assessments then undergo median filtering to remove outliers and employ collaborative filtering to compute recommendations. This model, while robust, can be susceptible to inaccuracies in node importance assessment and deceptive behaviours among neighbouring nodes, potentially undermining the reliability of trust recommendations in dynamic underwater environments. Another study [30] introduced a trust model that utilizes both collaborative filtering and a variable weight fuzzy algorithm to exclude untrustworthy recommendations and pinpoint dishonest nodes effectively. This model applied various filtering methods informed by divination tests and the precision of link quality, and it used a preset threshold for each criterion. Based on assessments from recommenders, the link quality filter is vulnerable to manipulation by malicious entities who might falsify these data to evade detection in the model.