# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 2 Background
### IoIT Network Model  
Recently, IoUT has been envisioned as an infrastructure comprised of sensing devices designed to detect and collect ambient data alongside communication components capable of propagating signals underwater and transmitting data to on-shore stations, such as cloud servers, for further processing. Due to its practicality compared to other communication schemes, underwater devices primarily utilise acoustic communication. This method, while advantageous for underwater environments, poses significant challenges. Acoustic communication exhibits a high propagation delay, approximately 1.5 \(\times\) 10\({}^{3}\) m/s, compared to the 3 \(\times\) 10\({}^{8}\) m/s propagation speed of radio waves on land. Additionally, it suffersfrom low bandwidth and a high error rate. The aquatic environment significantly affects the performance and reliability of the network. Underwater networks are characterised as complex and dynamic, with high levels of noise, high-delay channels, and sparse and mobile node deployment [8]. Furthermore, underwater communication devices are typically battery-powered, and no efficient methods currently exist for frequently recharging or replacing these batteries. These limitations are evident in various types of underwater devices. For example, Autonomous Underwater Vehicles (AUVs) like the Bluefin-21, designed by General Dynamics, can dive to depths of 4500 m, travel at speeds of around 8.334 km/h, and have a total energy capacity of 7 kWh [9]. Similarly, the MIDAS WLR, a water level recorder developed by Valeport, features 16 MB of memory, consumes 0.3 W of power, and operates at depths of up to 600 m [10]. Existing trust mechanisms face several challenges when applied to the complex and dynamic nature of the undersea environment. The frequent changes in network topology caused by dynamic aquatic regions, influenced by factors such as water currents and surface winds, along with the movement patterns of underwater nodes, make it unreliable to rely solely on successful communication as primary observations to validate trustworthiness. Additionally, the open nature of IoUT and the high costs associated with regular monitoring and maintenance, combined with the fact that these devices are typically left unattended for long periods, make the network highly vulnerable to undetected hijacking.  
This study explores the trust establishment of the IoUT, which comprises randomly distributed nodes that carry out sensing, data collection, and data relay to the surface base station using a hop-by-hop approach communicating over acoustic channels [11]. In trust management, each node evaluates the reliability of its neighbours using its available information. Figure 1 shows the entities involved in the trust establishment process. The evaluating node is the Trustor node (_i_), depicted in green, and the evaluated node is the Trustee node (_j_), depicted in grey. Trust evidence is the data used to calculate trustworthiness, resulting in a trust score from 0 to 1, where 1 means complete trust and 0 means none. The trustor is subject to a lack of evidence to judge the trustee, and therefore, it seeks input from neighbouring nodes. These responses, called recommendations, can come from fair (blue), malicious (red), or uninformed (white) nodes.