# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 2 Related Work
[26] presented a trust framework that uses a consistent approach for determining trustworthiness in terms of interaction but uses a decentralized implementation of the model to facilitate a structured P2P network. Consequently, in [27], BTRM-WSN, a bio-inspired trust and reputation model for WSNs, is provided to maintain the most trusted route to the most reputable node in a WSN. This methodology is built on an ant colony's bio-inspired approach. The authors of [28] have proposed an encryption and trust evaluation model based on a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. [PERSON] et al. [29] have presented a trust-based mechanism for enhanced security integrated with an energy utility and reusability model with software-defined networking (SDN) to maximize energy utilization. They presented a framework with SDN for the service station in a WSN. In [30], the authors proposed a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used to calculate the trust value, while LSTM is used to identify changes in the behavior based on the trust threshold. A Temperature-Aware Trusted Routing Scheme (TTRS) is proposed in [31]. TTRS is a hybrid trust model and a multifactor, depending on the trust value of sensor nodes, remaining energy, hop count, and routing strategy. The multifactor strategy selects trustworthy nodes to forward data and reduce energy utilization due to secure shorter routing paths. TTRS incorporates an efficient multifactor hotspot node detection algorithm (HNDA), route discovery, and a route maintenance mechanism to detect malicious relay nodes for consistent data delivery in an unattended environment. The authors of [32] have proposed a dynamic network security mechanism. Firstly, the direct trust value of the node is established based on its behavior in the regional information interaction. Then, the comprehensive trust value is calculated according to the trust recommendation value and energy evaluation value of other high-trust nodes. Finally, node reliability and management nodes are updated periodically. Malicious nodes are detected and isolated according to the credibility to ensure the network's dynamic, safe, and reliable operation. [PERSON] et al. [33] have proposed a cross-layer security-based fuzzy trust calculation mechanism (CLS-FTCM) and the least overhead monitoring for WSNs by means of memory and energy demands to detect the faults happening at various places at the same time. The fault monitoring system is carried out using an enhanced convolutional neural network (ECNN) classifier to identify malicious nodes on the network, and the confidential value is determined for the trust values. The authors of [34] focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A grid organizer was selected based on the sailfish optimization algorithm in the grid-based model. Subsequently, a fuzzy expert system selects the relay node to reach the shortest path for data transmission. The authors of [35] have presented a well-organized trust estimation-based routing scheme (ETERS) that consists multi-trust (communication trust, energy trust, data trust) approach to alleviate several internal attacks, such as badmouthing, Sybil, selective forwarding, on-off, black hole, and gray-hole attacks for clustered WSN.