## 3 Self-Configuration Management (SCM)
### Data and Radio Model
The suggested SCM was put through its paces on a variety of system executions with an infinite number of network problems. During testing, the proposed DSFD reported dropped messages, increased Byzantine process problems, and omitted and damaged messages. The average degree of sensor nodes in the network influenced detection accuracy, while the total number of messages required varied based on the methods used. The overall number of messages exchanged by all of the proposed methods was influenced by the network's number of sensor nodes as well as the energy needed by the sensors to send messages. To improve the system's efficiency, DSFD uses a feedback mechanism and tasks supervisory nodes (CHs) within each cluster to ensure the reliability of the primary node. ## 5 Conclusions

In this study, we proposed self-configuration management for the correction of system faults that are influencing the effectiveness of performance in a WSN's operating system platform. To account for poor detector faults, we suggested a self-sensible distributed fault identification technique. It put its abilities to the test by injecting random spots into the chosen sensor networks in order to detect the aforementioned aberrant circumstances. In each round, the cluster head collects data from the clustering sensors and then diagnoses

Figure 4: Comparison of total time and message exchange by varying average degrees for the DSFD, LART, PAR, and MC algorithms. (**a**) Total time; (**b**) energy consumed by transfer. its functional condition using the suggested DSFD algorithm. The research work in this phase resulted in practice characteristics such as detection accuracy and false alarm rate. The suggested DSFD technique detected the malfunctioning software sensors with over 98.8% detection accuracy, converging the proportion of passing-alarm message accuracy, while maintaining a minimal (at most 2%) false alarm rate. There was a 2% improvement in detection accuracy. Following that, we created configuration policies in response to the system answers. These policies were used to configure recognized defective nodes that are part of the planned network. The IoT service was used to transfer all software replicas by primary nodes and direct commit messages M3 to each defective node for error configuration. The reaction assessments observed efficiently and automatically respond to a wide range of system problems on the network over brief epochs. The fault configuration management achieved a 93.2% efficiency in correcting the software within the limited time frame estimated in seconds. To achieve optimal network setup and improve WSN performance, configuration parameters were created, to refine the proposed network performance and increase indications of WSN lifespan prolongation. Based on the above parameters, the actual network lifetime was calculated by subtracting the entire configuration length from the operational communication duration. The overall network lifetime is estimated by adding the period after delivering coverage messages to the continuous running time of the network prior to the warning message export, which indicates the data delivery ratio going below its predefined threshold. To ensure the success of the trials in this part, we set both criteria at 90. Conceptualization W.M.E. and E.E.-s.; methodology, M.E. ; validation, M.K.H. ; formal analysis, W.M.E. ; writing--original draft preparation, E.E.-s.; writing--review and editing, M.E. and W.M.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data sharing is not applicable. The authors declare no conflict of interest.