## 6 Outcome Analysis
The experiments were conducted by varying the network size based on two scenarios, where scenario-1 constitutes 2:6:150 [BS: CH: mn] propositions of nodes and scenario-2 includes 4:8:200 [BS: CH: mn]. Furthermore, Figures 16 and 17 demonstrate that, throughout the case of CRS, the sensing node survival rate is much higher than that of S-LEACH and LEACH-C. Thus, our suggested approach is successful for massive network deployments. Figure 18 illustrates the throughput in homogeneous routing at the intra-cluster level and heterogeneous routing at the inter-cluster level. When compared to S-LEACH and LEACH-C, CRS is also shown to provide better throughput. This is because each cluster is segregated based on service type and process specific to similar data types. Moreover, unless the transmission data packets of the nodes exceed the time slot, it is presumed to transmit its sensor data to the corresponding CH directly. Thus, the overall delivery of packets with minimal loss is an application through the proposed protocol. Residual energy and connection level are primary factors in attaining an optimum throughout. The CH is randomly chosen among active nodes in S-LEACH with no parameter/criteria. Another  
Figure 14: Average energy utilization for cluster formation. Figure 15: Average energy utilization for CH selectioon. significant issue is that all nodes are assumed to prefer only zero-hop communication (direct communication) in LEACH. Thus, it is not realistic and suitable for large-scale infrastructure. Figure 19 compares the number of stable connections among active nodes throughout the network operations. The results exhibit the stable connectivity for the proposed CRS, almost 100% in every round. Whereas, for LEACH-C and S-LEACH, it is observed as 96% and 88%, respectively. Because the transmission limit for each cluster is constant, it ensures the data reachability of every node falls under the reception coverage of other nodes. LEACH-C aims to avoid isolated nodes, certain nodes that do not interconnect well because of gateway nodes, which may shorten the network's lifespan. Due to its dynamic cluster formation strategies, it guarantees a high level of connection. CRS service-oriented clustering and CH selection based on ranking strategy enable routing topology creation more efficiently. For LEACH-C, the size of the area varies, leading to the increased count of hop utilization even with higher transmission capacity. These data show  
Figure 16: Active sensor nodes based on service types vs. experimental rounds (scenario-1). Figure 17: Active sensor nodes based on service types vs. experimental rounds (scenario-2). that the transmission route developed by CRS is better than that of S-LEACH and LEACH-C. Further, the gap might be more significant in situations for which the transmission capabilities are expanded. Another way to quantify how well the mn and CHs are distributed concerns the mean hop count between CH and BS, which is determined as the ratio of total hop-count between the CH and BS to the total count of CHs in the deployed region. For all three protocols, the mean hop-count from mn to the sink is compared. Figure 20 shows that the CRS protocol's mean hop count decreased by 1%, whereas LEACH-C and S-LEACH increased by 2% throughout the experimental rounds. This recorded observation is due to the adaptation of CRS protocol to the DD-TDMA's slot adjustments based on the generation of data packets and thus forms an optimized clustering environment. Figure 19: Average number of connected nodes (in 3-service types). Figure 18: Throughput vs. experimental rounds. In addition to processing several comparative analyses, CRS, S-LEACH, LEACH-C also vary in the packet reachability ratio from specified clusters to the BS, using various energy levels of the nodes with varying experimental rounds. The packet reachability ratio of the CRS protocol is much higher than for the other two protocols.