## 2 Literature Survey
### Trust-Aware Data Gathering  
The literature on trust-aware data collection refers to the process of data collection from WSNs, which includes varying degrees of sensor trust. Considering other data-gathering factors, namely, energy consumption, journey time, and cost of the network, necessitates a nonlinear data-gathering mechanism that prioritizes the trajectory of mobile sinks with greater closeness to the trust nodes. There are many different algorithms that have been used for trust-aware data collection in the literature. A model for gathering reliable data was presented [6] based on edge computing (EC) and the Internet of Things (IoT) The sensor nodes in this model are assessed over a wide range of attributes to obtain accurately measured trust levels. Additionally, by virtually mapping a node's trust value, the optimum mobility route with high trust was built for mobile data collection. Furthermore, a portable edge data collector was used to access and gather reliable data from sensors with quantifiable degrees of trust [19]. The potential for sliding into local minima when the total amount of virtual forces reaches zero is a limitation of this approach. The model used also only supports one mobile collector. Additionally, the total trust makes the erroneous assumption that there is a linear relationship between several components. In one study [20], it was recommended to use a probabilistic graphical model to completely evaluate the dependability of the sensor nodes utilizing a mobile edge-computing-based intelligent trust-assessment technique. The suggested approach analyzes data collection and communication behavior to assess the reliability of sensor nodes. The proposed mechanism evaluates trust based on data collection and communication behavior and plans the edge nodes' movement path to increase the likelihood of direct trust evaluation while minimizing travel distance. However, this method ignores the various dimensions of trust and operates with only one mobile collector. Authors [21] also looked at the use of unmanned aerial vehicles (UAVs) as mobile sinks. The problem was recast as a multiobjective joint optimization problem as a result, with a large number of constraints, leading to a typical K-center problem. Then, a lightweight authority authentication model using certain stages was examined to overcome the major security issues with UAV-aided trustworthy data collecting. This architecture needs reliable sensors and UAVs with a tolerable authentication latency to carry out the data-gathering session. Smart data collecting with cloud services (S-SDC), a secure mobile sensor network-based protocol, was suggested in a study using mobile sensors to enable information dissemination in dynamic networks with the least loss and power consumption [22]. Additionally, the method supports authoritative routing and offers network consistency and availability for the collected data with cloud enterprises. The fitness-based fuzzy C-means (Fit-FCM) algorithm, which gathers nodes' residual energy and utilizes the energy to pick new CHs while discarding nodes with the lowest energy, was proposed as a UAV-based CH selection (CHS) technique for usage in WSNs [23]. The created Fit-FCM algorithm, which takes into consideration fitness functions including energy, distance, and trust, is used to conduct CHS after simulating UAV-based WSN nodes. An IoT-based trust evaluation was carried out [24] to determine whether the target object can consistently carry out defined actions. The target object's direct interaction history or other recommenders' suggested data are both considered in the valid time domain. Two types of trust--active trust and recommended trust--were offered to characterize the trust evaluation process for Internet of Things devices. A temperature-aware trusted routing scheme (TTRS), a multifactor routing strategy based on the remaining energy, the hop count, and the trust value of sensor nodes, was proposed [25] to find the shortest and most reliable route.