## 3 Self-Configuration Management (SCM)
### Data and Radio Model
In terms of scalability and time to finish the algorithms, our suggested DSFD algorithm was determined to be more efficient than existing algorithms. The suggested DSFD technique needed fewer messages to be transferred among nodes than current algorithms that rely on heavy communication. The time required to finish the algorithm, on the other hand, varied, possibly due to network connections. The experimental results demonstrated that the position of the sink deployment could alter the overall network's data aggregation time. The placements of the sensor nodes, CHs, and sink, as well as the distances between them, will all result in variable data aggregation and transmission durations in a clustered network. Figure 4 demonstrates that the overall network environment's deployment strategy of CHs and sensor nodes produces the best results in terms of messages exchanged synchronously, known as the packet delivery ratio. This metric compares the number of transmitted packets to the number of received packets, as well as the node energy consumption and data transmission time. The research revealed that as the number of participating nodes increased, the amount of messages exchanged in DSFD decreased. When compared to the PAR and LRT approaches, the overall time required to execute the DSFD algorithm decreased with increasing node count, with an average reduction of 0.24 us [10; 11]. By expanding the number of sensors in the network, LRT and PAR increased exchange time/delay. DSFD began with 100 messages and gradually increased to 2000 messages for 150 nodes in terms of message exchange. The findings also reveal that when the injected error (E) is more than or equal to 0.25, the test rule achieves 100% accuracy, as shown in Figure 4a. We calculated the overall cost of communicating across the network to send m-bit messages. \[t_{comm}=\sum_{i=1}^{m}t_{s},\ (mt_{b}+t_{h})l \tag{19}\]

Figure 4 depicts two charts that demonstrate how, when a sensor network is operational, the overall traffic of exchange messages increases as the number of sensors inside the network increases. As a result, the delay time grows dramatically with the number of rounds. The study also discovered that the number of messages transferred between nodes had a quick effect on the total energy consumption of the nodes. Otherwise, the study discovered that the amount of messages exchanged between nodes falls when the number of participants' sensors diminishes. In practice, the proposed method's overall energy usage tends to be steady after 1000 rounds. It was discovered through studies that changes in the number of surviving nodes, after 1000 rounds of experiments, have no meaningful impact on how much energy is consumed. Where it is noticed that the transfer energy climbs just slightly in the proposed method, energy efficiency reaches 50% of the network's total energy. In contrast, it increased greatly when compared to other algorithms LRT [10] and PAR [11], as shown in Figure 4b. This means that, in the simulated experiment, the proposed technique can maintain a higher node survival rate. The results obtained in this section of the study demonstrate that the message exchange rates of other algorithms increased exponentially, resulting in significant energy loss. The DSFD method, on the other hand, has a fixed linear growth rate. The proposed method has much-improved performance in terms of node energy consumption and survival time, which can effectively extend the overall network's working performance. This is owing to fact that adopting a fault-tolerance technique and deploying the sensed data in a distributed manner is an adequate and well-organized mechanism for transfers and message exchange. This points out that the proposed fault detection methodology has significantly lower computational complexity and processing time than other methods. Furthermore, the study established a relationship between message exchange parameter efficiency and lower network energy consumption, which improves network performance by 96.4% and increases network lifetime.