## 2 Literature Survey
### Trust-Aware Data Gathering
The scheme includes a hotspot node detection algorithm to detect malicious relay nodes and a route discovery and management mechanism. In a study [26], a belief-based trust evaluation mechanism was developed that uses a Bayesian estimation approach to distinguish hostile nodes from trustworthy nodes and protect against attacks such as bad-mouthing, on-off, and denial of service. In the work of [27], an aggregate signature-based trust routing (ASTR) scheme was developed to ensure safe data collection in WSNs. The scheme uses aggregate signatures to maintain data integrity and employs a detour routing scheme to verify the safe delivery of data to the sink. The trustworthiness of a path is determined by its success rate. In [28], a trust-based data gathering approach is proposed that focuses on collecting data in a sensor-driven environment through data aggregation and reconstruction. The concept of multidimensional trust was introduced, incorporating communication, data, resource, and forwarding trust. However, the approach has limitations as the aggregation method used is based on the weighted average rule, which may lead to suboptimality. In [29], the secure and energy aware data gathering technique (SEEDGT) was proposed as a secure and energy-efficient data gathering technique that combines trust, public key algorithms, and compressive sensing (CS) methods to achieve security while maintaining a fair energy load balance in IoT-WSN. The SEEDGT technique is divided into three phases: cluster formation, network operation, and reconfiguration. During the cluster formation phase, energy-efficient and trust-based methods are used to form clusters and select cluster heads. The network operation phase focuses on securing network data during the data-collection process using a public key algorithm. Additionally, the CS strategy is employed to reduce the size of the original data, thus reducing energy consumption. Finally, the reconfiguration phase considers any changes that may occur during network operations. In [30], for data aggregation, the cluster head is selected using the emperor penguin optimization (EPO) algorithm, and STELR is used to ensure routing security. Secure communication is achieved by determining a node's trustworthiness. In [31], a fog-based hierarchical trust mechanism is proposed, consisting of two components: trust in the underlying structure and trust between cloud service providers (CSPs) and sensor service providers (SSPs). To enhance the trust in the underlying structure, a behavior-monitoring component was established and implemented in wireless sensor networks (WSNs), while the more fine-grained and complex data analysis component was moved to the fog layer. The fog layer focuses on the real-time comparisons of service parameters, gathering exception information in WSNs, performing targeted quantitative evaluations of entities, and performing other aspects to establish trust between CSPs and SSPs. In [32], a trust evaluation model based on trust transitivity along a chain is proposed, which relies on the relatively strong computing and storage capabilities of mobile edge nodes. The authors first designed calculation methods for various trust chains to determine their trust levels. Then, they proposed an improved version of [PERSON]'s algorithm for collecting trust information from sensor nodes by the mobile edge nodes. In [33], a method is proposed for encoding returned verification messages in order to reduce the cost of obtaining verification messages for IoT devices. Additionally, the authors applied an adaptive active trust detection technique that considers the varying energy consumption of IoT devices in order to obtain reliable device trust while preserving network lifetime. The cloud distributes tasks to all available mobile edge units (MEUs). The publication includes the topology of the sensor network and the location of each sensor node. Upon receiving the publication, interested MEUs report their evaluation quality, coverage area, and expected price to the cloud.