# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 2 Related Work  
This section is dedicated to the most relevant literature on the proposed approaches in this paper. An innovative method, game-based trust, is proposed in [7], which utilizes game theory to synthesize direct and indirect trust for decision-making and improve networks' robustness and security. Game theory based on indirect information is used to calculate indirect trust value. The authors of [8] also propose a data-driven trust mechanism based on blockchain (DDTMB) technology, which is a decentralized and energy-efficient solution for detecting inside attacks in the Internet of Things (IoT) powered Sensor Networks. The DDTMB utilizes data collected from internet of things devices to demonstrate the resulting communication. Similarly, a trust management-based and energy-efficient hierarchical routing protocol were also suggested in [9]. To reduce node energy consumption, LEACH-TM restricted the cluster size. Numerous metrics were utilized, including the number of dynamic decision cluster head sensors, sensors residual energy, and the number of neighbors. Furthermore, the trust-management model was integrated into LEACH-TM to overcome internal threats. Moreover, in [10], the author suggested a trust method for dynamic optimization depending on the entropy technique. The entropy weight model chooses the sensor weights in every set. After that, the Standard Deviation (SD) value for local group evaluation is computed to obtain the general expectations of all sensors within the set, likewise the SD of Local Evaluation. In [11], the authors presented a Time Series Trust method depending on the Toeplitz matrix and trust-based Auto-Regressive strategy. The effect of the suggested method on data forecasting the gathering and rebuilding of compressed sensing evaluated via many performance factors and various attack methods. Another proposal was stated by the authors in [12], where they proposed a protocol Layer trust-Based Intrusion Detection System to protect the WSN through discovering the threats at various layers. The sensor's trust value was determined using the variation of trust indicators for every layer considering the threats. Additionally, the authors consider the truthfulness of trust in the PHY, MAC, and network layers. Lastly, the total node's trust value is calculated via summation of the trust values for the three layers. Based on the sensor's trust value, it is considered a trusted or attacker. To evaluate the trust and reputation of sensors in wireless sensor networks, the \begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Definition** \\ \hline TTSN & Task-based Trust framework for Sensor Network. \\ P2P & Peer-to-Peer \\ ID & identifier \\ IOP & Information of Packet \\ TCP & Transmission Control Protocol \\ RREQ & Route REQuest \\ RREP & Route REPly \\ ACK\({}_{\text{DLL}}\) & Data Link-Layer ACKnowledgement \\ ACK\({}_{\text{TCP}}\) & TCP Layer ACKnowledgement \\ MEM & Malicious Exploration Message \\ MDM & Malicious Discovery Message \\ RTT & Round-Trip Time \\ TRMSim & Trust and Reputation Models Simulator \\ MDR & Malicious Detection Ratio \\ APL & Average Path Length \\ AEC & Average Energy Consumption \\ MTE & Multi-Threshold Energy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._authors in [13] proposed an Exponential-based Trust and Reputation Evaluation System to monitor sensors' activities and assess sensors' trust and reputation. The exponential distribution was used to compute the allocation of the sensors' trust. The sensor's trust was applied to find trusted sensors to transfer data and decrease malicious threats in the network. Moreover, the entropy notion was applied to get the uncertainty values of the first-hand trust. The second-hand trust was proposed to support interaction information from non-neighboring nodes when the suspicion of the first-hand trust is large enough. Moreover, the authors used the confidence factor to regulate the value of the sensor's trust to debilitate the hurtful impacts of the malicious sensors.