## 3 Self-Configuration Management (SCM)
### Data and Radio Model  
We used sensor nodes equipped with wireless transceivers to enable continuous connectivity and integrated coverage. If the distance between two nodes is less than the communication area Ca the two nodes can communicate directly, then 1S1 & S2\(|\) < Ca. To enable data transfer without human intervention, distinctive identities were given to the sensing devices in the network. System replicas were transferred between devices with the use of the main node's services. Additionally, it chooses the main node CHi for each cluster and serves as a manager device, with the main node managing the transfer of system replicas across devices. We created the radio system with self-configuration techniques in mind. Every cluster's N-nodes are included in the communication path, starting with n1, moving on ton2, n3, n4, and so forth until it reaches Nc. It is crucial to remember that a package size is sent from S1 to SN within the time interval [0, t]. We may calculate the size of the sent software package in bits per configuration by specifying the configuration flow rate Pi. The transmission duration for each cycle can be written as \(\text{S1}\rightarrow\text{Si}+1\), and the time interval \(\text{t}_{i}\) can then be determined. \[t_{i}=^{n}/P_{i}\times R_{i} \tag{1}\]  
Let us say that \(\beta 1\) represents the energy needed by the transmitter, and \(\beta 2\) represents the energy required by the amplifier-receiver electronics. To calculate the total energy necessary for transmitting and receiving a volume of \(P_{i}\) over a distance of D, the formula for transmission energy, (\(E_{T}(P_{i},D)\)) can be used as:  
\[E_{T}(P_{i},D)=P_{i}(\alpha\beta 1+\alpha\times D) \tag{3}\]  
\(\alpha\) is a spacing coefficient that can vary from 2 to 4 [14]. In addition, we developed a model to determine how much energy was lost throughout the package exchanges. This model accounts for the energy used by the sensors in the previous transfer mode (\(E_{T}\)) as well as the radio frequency transceiver power (RF transceiver), a parameter Ci that affects transmission quality in terms of bit error rate and noise. \[\text{E}_{i}=RF-(E_{T}\times C_{i}\times t_{i}) \tag{4}\]  
Based on this, the receiving model (\(E_{R}\)) can be determined using the following equation:  
\[E_{R}(P_{i},D)=P_{i}\times\ \alpha\beta 2 \tag{5}\]  
The radio communication (\(RC\)) model was used to create a network connection between sensors within each cluster in the proposed network. The relationship between tolerance and connectivity was analyzed and then the cost of RC was calculated based on configuration energy \(\left(E_{conf. }\right)\), tolerance energy \(\left(E_{to. }\right)\), and performance. Using these calculations, the most efficient power consumption for \(\left(E_{RC}\ \right)\) was evaluated. \[\text{C(bps)}=2\text{B}\times\ log_{2}\text{M} \tag{6}\]  
\[E_{to.}=\sum_{i=1}^{m}N_{b}\times E_{rb}\ \times E_{encod.} \tag{7}\]  
\[E_{conf.}=\frac{SNR\times NR\times E_{to}\ \times\text{C(}bps)}{\text{Gant }\times Et_{b}\times\text{B}} \tag{8}\]  
\[E_{RC}\ =\sum_{i=1}^{m}(E_{R}\,E_{conf.},\ E_{to.},\ E_{T}\,E_{i}) \tag{9}\]  
The suggested technique for finding defects in a distributed system is divided into two phases and follows an established methodology. It seeks to make it possible for the system to recognize and fix any configuration problems on its own. _Phase 1: Distributed soft fault detection model (DFD)_  
In sensor networks, we assumed that all error-free sensor nodes measure the same physical value at time t. Different physical quantities are measured by WSN sensor nodes with errors (data collected at time t by sensor node Si). The data model requires that the mean value M must be the same for all healthy sensor nodes. However, the unpredictability (sometimes referred to as misconduct or misbehaving) that various sensor nodes measure may vary. The WSN literature frequently assumes that this sensor data pattern can identify faulty sensor nodes.