## 2 Related Works  
Many studies have been conducted to address authentication in WSNs. These studies can be divided into blockchain-based and non-blockchain studies. In non-blockchain studies [2; 9; 10; 11], there is low security [12] due to central controls, client-server architecture, and exposure to many attacks. Some of these studies are as follows:  
In the study [13], a secure user authentication model is proposed for wireless sensor networks used in the healthcare area. The authentication model that provides security and confidentiality comes into play when medical personnel want to see or approve patients' information. The study consists of three phases: model registration, login, and authentication. Medical personnel can safely process the health conditions of patients with the proposed study. Smart cards and bilinear pairing-based encryption are used for data verification. It is stated that the proposed model is resistant to the following attacks: impersonation attacks, replay attacks, online and offline password guessing attacks, and stolen verifier attacks. In another study [14], an authentication model was proposed by making use of users' biometric data. In order to keep the network secure, only authenticated users were able access the network. Fingerprint data were used with the help of users' PDAs or smart phones without the need for any additional equipment. It consisted of two phases, enrollment and authentication. The validity of the model was tested with the Burrows-Abadi-Needham (BAN) logic. In another study [15], an authentication center was created using RSA, AES, SHA-1. Sensor nodes were able to join the network after receiving approval from the relevant central unit. The central unit contained a microcontroller Unit and a WiFi module. In another study [16], a data authentication model is proposed for data integrity and availability. In the model, all sensor nodes go through the validation process. It consists of three stages: preparing a signature packet, data storage and authentication, and data verification and collection. The MAC value is generated for the data and the signed packet is obtained in the model. In another study [17], an authentication model based on a digital watermark was proposed. The model consists of three stages: watermark generation, embedding, and detection. In these processes, techniques such as XOR, insertion, and extraction of bits are used. In another study [18], a secure authentication and integrity technique was proposed. Digital signature and public key encryption were used in the model. In another study [19], an authentication model was proposed. The model consists of four phases: pre-deployment, registration, login and authentication, and password-change. The hash function and XOR computation are used in the model. The model is resistant to the following attacks: replay attack, stolen smart card attack, and off-line password guessing attack. In most of the non-blockchain studies, the authentication method has been developed by using known techniques. For example, in a study [20], a secure authentication protocol was developed for wireless sensor networks using machine learning techniques. The behavior of the nodes is monitored. Trust value is calculated according to these behaviors. In the verification process, if the trust value is lower than the specified threshold, the node cannot join the network. The threshold value is calculated by the Support Vector Machine (SVM) method using the collected traffic data. While calculating these values, Packet Delivery Ratio, Delay, Residual Energy, and Computational Overhead values are used. While there are many machine learning methods, SVM is preferred because it requires less computation. In another study [21], an adaptive trust-based authentication model was proposed. Authentication is performed using the trust score. The trust score is calculated by considering the location information of the nodes. The timestamp is also taken into account when calculating the trust score.