## 3 Proposed Method
The chain code is how the user interacts with the ledger. We may develop interfaces that fulfill specified logic functions for distinct chain codes with the aid of Fabric's API. There are three sorts of chain codes in the proposed architecture. (1) The Policy Management Chaincode (PMC) adds and maintains access control policies based on the ABAC architecture in this work. Only policy managers, such as object owners, could perform it. The Environment Attribute (EA), Action Attribute (AA), Object Attribute (OA), and Subject Attribute (SA) are the four components of an access control policy. The subject, role, group, and domain are all part of the SA, representing the subject's basic identity information. SubjectID distinguishes whether the topic is a user or a user's device. As with OA, we choose two unique identifiers, ObjectID and MAC, as the object's properties. The AA represents the permissions for a subject-object pair. We utilize an integer to signify various storage permissions (4--Read, 2--Write, 1--execute). The EA reflects the policy's context condition. The item could only be accessed in a particular context by the subject. Each policy's information will be kept in PMC. Given the lack of security protection, the system is exposed to infiltration. IoT devices with insufficient processing, memory, and resource availability are vulnerable to becoming tools for thieves to start nefarious conduct. This will cause the infected IoT system to behave abnormally, including deleting, changing, injecting, and retransmitting data packets, among other things. Further, the monitoring and recording of this data, linked with the access control system, for detecting harmful conduct, and its prevention from spreading as quickly as feasible may be performed. * Suggested method TOPSIS with extended triangular functions was proposed by [PERSON] [25], in which the distance between 2 triangular functions was calculated using the vertex method. So, if the 2 triangular functions are x = (\(a_{1}\), \(b_{1}\), \(c_{1}\)) and y = (\(a_{2}\), \(b_{2}\), \(c_{2}\)), then \[\mathrm{d}(\mathrm{x},\mathrm{y})= \sqrt{\frac{1}{3}\big{[}\big{(}a_{1}-a_{2}\big{)}^{2}+\big{(}b_{1} -b_{2}\big{)}^{2}+\big{(}c_{1}-c_{2}\big{)}^{2}}\] (1)  
The framework used for this study is given in Figure 2. Further, the fuzzy TOPSIS methodology procedure has been detailed below, and a flowchart of the same is shown in Figure 3.  
1. Identify the measures and options for carrying out the analysis. 2. Assign ratings to the measures based on which alternatives would be ranked. 3. Formulate \"R\". where R = [r\({}_{\mathrm{ij}}\)], for the 'benefit criteria' \[\mathrm{r}_{\mathrm{ij}}=\left(\frac{a_{ij}}{c_{j}*},\frac{b_{ij}}{c_{j}*}, \frac{c_{ij}}{c_{j}*}\right);\ c_{j}*=\max_{i}\big{\{}\,c_{ij}\big{\}}\] (2) Furthermore, for the cost criteria, we have Equation (3). \[\mathrm{r}_{\mathrm{ij}}=\left(\frac{a_{-j}}{c_{ji}*},\frac{a_{-j}}{b_{ij}*}, \frac{a_{-j}}{a_{ij*}}\right);\ c_{j}-=\min_{i}\big{\{}\,a_{ij}\big{\}}\] (3)  
Figure 2: The framework used for the optimal node selection that indicates the evaluation criteria and the alternatives. Figure 3: Flowchart of the fuzzy TOPSIS methodology. 1. Compute \"V\", where \(\rm{v_{ij}}=\rm{r_{ij}}\) * weight of the criteria (\(\rm{w_{j}}\)). \[\rm{V}=\rm{v_{ij}}\] (4) 2. Using the following equations, calculate the Fuzzy Positive and Negative Ideal Solution (FPIS and FNIS). \[A^{*}=({v_{1}}^{*},\ {v_{2}}^{*},\ {v_{n}}^{*}),\rm{where},\ {v_{j}}^{*}=\max_{i}\{\,v_{ij }\}\] (5) \[A^{-}=({v_{1}}^{-},\ {v_{2}}^{-},\ {v_{n}}^{-}),\rm{where},\ {v_{j}}^{-}= \min_{i}\{\,v_{ij }\}\] (6)
1. Calculate the distance of every option from 'FPIS' and 'FNIS' employing Equations (7) and (8). Figure 2.0 (7) \[{d_{i}}^{-}=\sum_{j=1}^{n}d\big{(}{v_{ij}},\ {v_{j}}^{-}\big{)  
triangular membership functions for INRE, INFBS, and INDE, and their relative importance. Table 4 indicates the linguistic terms for the INSPs, their codes, and corresponding triangular fuzzy membership functions.