## 3 Self-Configuration Management (SCM)
### Data and Radio Model
Each CH in the planned network receives welcoming joined messages and monitoring information from the elements of its cluster regularly. Following that, CH begins analyzing the incoming data packets and is capable of detecting the state of each sensor within its cluster. CH can establish the status table (STi) for the cluster individuals through analyses, and then declare STI to base station (BS) to prepare the maintenance process in which faulty deployed nodes are configured by automatic installation procedures to obtain the basic configuration for system operation. In our simulation, we used relational datasets from IoT devices to collect temperature values throughout data radio transfer. In our simulation, sensor nodes are randomly inserted and distributed over a square area of 220 m \(\times\) 220 m. Table 2 illustrates the measures of concluded parameters from this simulation. At the beginning of each round, the CH collects heartbeat messages from all nodes of the cluster to confirm joining its cluster. The table illustrates the estimated deployment costs from our constructed WSN using the proposed SCM approach. Through experiments, we eventually incorporated 100 s to 1000 s of autonomous sensor nodes that were spatially distributed through our planned WSN. It used an NS3 simulator to plan twenty clusters. Temperatures can be detected, treated, and sent via an RF emitter on the network by a sensor node within each cluster. The sensor readings chosen for implementation, as well as the temperatures recorded in various rooms of residences in one of Germany's geographical districts. Due to a lack of additional power, the sensors relied on the limited battery and approximated the peak voltage by 2 joules. The functioning radio, the status of the sensor, the distance between the nodes, and the elapsed diffusion time all have a substantial impact on the energy consumption of the sensor node as a result of the implementation. During communication, the most power is consumed. It is worth noting that the Tiny operating system (OS) automates the proposed network pieces. Tiny OS is a prominent platform for sensor network research due to its ability to successfully address system difficulties. It is used by over a hundred organizations throughout the world and supports a wide range of research topics and applications. Tiny OS was chosen because it is a versatile, application-specific operating system for sensor networks. It also allows for the execution of concurrent programs with reduced memory needs. In practice, we considered Tiny OS to be a tiny system with fewer than 400 bytes that fits within 16 KB of memory space. Our fundamental operating system is under 400 bytes in size, making it highly efficient and low-power in all duty cycles. Our experiences with Tiny OS have been positive, and we have optimized sensor network apps and offered advances in coverage speed and repair. In our implementation, the Tiny OS component method worked well, and we reflect on our practical experiences. Table 3 records the selected samples from STI responses of various infected clusters with system faults throughout numerous deployment rounds. Table 3 shows the two types of system errors that are taken into account. The first is the fault-stop (in which the node fails and stops running) induced in the previous rounds to return a result. S4 demonstrated this in R15. During deployments, we noted that S4 did not react with the result from the previous round R14, and this occurred again in R15. Another sort of failure is an arbitrary-node failure, which includes a variety of failure types. S4 in R10 made one response with an incorrect outcome. The second option is for S2 in R15 to respond with a deliberately deceptive response. Another option is to respond differently to distinct members of the cluster, as seen in S4 in R5. In R10, S2 is afflicted by a failure to synchronously respond. Two approaches can be used to detect a system problem caused in the transmitted data during transmission.