# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 2 Related Work
To enforce node cooperation in Mobile Ad hoc Networks, the authors of [14] have suggested the CORE (COllaborative REputation) method. This method is developed and modified over time as a result of direct observations and information given by other members of the sensing community. Nodes exclusively share information about their positive reputation with one another. Confirmation of Nodes-Fairness in Dynamic Ad-hoc NeTworks (CONFIDANT) [15] is a distributed, symmetric reputation technique that relies on first-hand observation and second-hand suggestions from non-neighboring sensors. [PERSON] et al. [16] presented a reputation-based architecture for sensor networks. It employs a watchdog to build trust. However, the watchdog cannot capture every event due to its fault or network error. [PERSON] et al. [17] have developed a trust-based and energy-aware MANET routing model. Using the reliability parameter, the pathfinding process determines which node has the highest level of trust, and energy is used as a router. The node accepts a Route Request (RR) packet from the origin node if its trust value is high; else, the RR packet is ignored. As a result, by incorporating re-evaluation and reputation fading, the Bayesian approach-based model [18] may facilitate recovery while also preventing the sudden exploitation of a positive reputation that has been established over time. Aside from that, the authors of [19] have suggested a Hermes method, which is a conceptual framework for trust-building in the context of dependable packet delivery in the presence of potential hostile nodes. This method uses both first-hand and second-hand information to alter the values of trust. The authors of [20] offer another distributed trust-based architecture, along with the election method for the trustworthy cluster heads. Each node maintains a trust table for all of the nodes in its surroundings; the contents of this table are only transmitted to the cluster head upon request. To rank sensors through correlation using exploring Markov Chains in the WSN, the authors in [21] improved a scheme called SensorRank. A Trust Voting system was presented to discover incorrect node readings, and if the sensor is misbehaving, the sensor will not share in the voting. According to [PERSON] et al. [22], reputation-based trust is derived from the concepts of probability, statistics, and mathematical analysis. As a new concept in trust, the authors propose that \"certainty\" can be adopted in building trust. WSNs, according to the authors, are not capable of making decisions based on the \"trust\" output of a single incident. A reputation space and trust spaces were used in WSNs, and they can define the transition from reputation space to trust space. Moreover, using a trust-based LEACH protocol to enable safe routing, the authors of [23] have suggested integrating a trust management module with a trust-based routing module. The trust management module is responsible for establishing trust relationships among nodes using innovative methods to increase building trust and offer efficient monitoring and trust exchange. A modified version of the trust-based routing protocol uses the same head-selection algorithm and working phases as the original protocol, making decisions based on trust. Several researchers, including [24], have suggested a multi-angle trust mechanism for nodes in Wireless Sensor Networks. Moreover, it considers the sensing data, communication trust, and node's energy when assessing the trust and communication. Moreover, to provide a trust framework in Wireless Sensor Networks, the authors of [25] have proposed the TTSN protocol. There are various trust rankings for each sensor based on the task. The proposed method employs a watchdog strategy to monitor the behavior throughout these sensors' various events and broadcast their trust values. Furthermore, [PERSON] et al.