## 3 Self-Configuration Management (SCM)
### Data and Radio Model
The primary (leading) node transmits the heartbeat status report M1 and a software delivery request (replicas) M2 to the receiving node (Sink node); two message exchange (M1 + M2). 3. The sink node accepts the sent service request and then sends replicas in a prepared message to each other, including the primary nodes (cluster head); and message exchange (M1 \(\times\) M2). 4. The primary nodes forward all replicas and direct commit messages M3 to each faulty node; message exchange ((M1 \(\times\) M2) + M3). 5. All nodes send a reply message M4 to their cluster head node to confirm their configuration of them. The head sends a memo to the sink after diffusion and fixes rounds periodically for message exchange (M4 + 1). We completed the request under the condition that each cluster head returns a \"k + 1\" answer, confirming that system sick nodes are at least one-third of the system's nodes, where k is the maximum allowable number of unhealthy nodes. \[Total\ \mathrm{message\ exchange}=\mathrm{M1}+(\mathrm{M1}+\mathrm{M2})+( \mathrm{M1}\times\mathrm{M2})+((\mathrm{M1}\times\mathrm{M2})+\mathrm{M3})+( \mathrm{M4}+1) \tag{17}\]

In practice, it was testing the aforementioned condition, and the software package transfer process began to execute the required configuration for the detected system fault sensors, as indicated in the previous step. At this point, we attempted to provide the reader with a computation of the elapsed configuration period, which includes the emitter of the software, removal, and change times, as well as periods of initial configuration and installation of the software package, via epochs. Table 5 summarizes the head's memo in one of the duty cycles and the time spent (in seconds) in the configuration process, which evaluates the complementation of the fault tolerance technique to configure the discovered Byzantine sensors in the previously mentioned round. Table 5 shows how successful each cluster's head is at covering the amount of detected defective nodes and reconfiguring during each duty cycle in a short time. When the head received \"k + 1\" replies from faulty nodes in the network, it satisfied the CH request. Similarly, we discovered that it survived numerous defective nodes over the service cycle. The head node is changed at each cycle of the practical implementation. If a preset period has passed without the head node broadcasting the memo message, it can be replaced with another, as seen in Figure 2. If necessary, a majority of clustering nodes can vote on the legitimacy of the current leading node and replace it with the cluster's next leading node. During that phase, we attempted to calculate an efficient fault tolerance ratio in the operational round by estimating the response haste of each configured sensor Si in the message exchange in all models, based on the number of sensor nodes N present in the network and the number of times a sensor node needs to broadcast the message. \(RT_{i}=\frac{S_{fi}}{N}\cdot(S_{f}\sum\limits_{i=1}^{Sf}Cp_{d}\) ). In the proposed configuration model, we achieved efficient fault tolerance, which was estimated at 93.2%. Figure 2 demonstrated that the results of experimental trials of detection and configuration operations were equalizing. This demonstrates that the progress timeline is parallel. During the defect detection phase, we counted the number of false alarm messages generated by the implementation. Detection accuracy (DA) was calculated during deployments by dividing the total number of messages sent by the average degree of malfunctioning sensors that emitted the fraudulent data (Xi). Throughout numerous deployments, we achieved a detection accuracy (DA) of 98.8%. DAi was given by,