## 6 Outcome Analysis  
According to the research, the proposed CRS protocol was evaluated compared to prevailing algorithms, including recent LEACH-C [43] and standard LEACH [15]. As a result, the CRS protocol is more effective than any of those compared protocols, particularly when considering network lifespan, and it is ideal for IoT-based smart infrastructures. Initially, the experiment is examined for two scenarios that vary in the count of deployed nodes. Scenario-1 comprises 2:6:150 propositions of nodes (i.e., 2 BS, 6 clustering regions, and 150 sensing nodes). Similarly, scenario-2 comprises 4:8:200. We used inter-cluster (between the clusters) and intra-cluster (within the cluster) routing strategies and loaded balancing conceptual paradigms to examine the CRS algorithm's energy efficiency and other QoS metrics. All the generalized processes, such as slot allocation per frame, CH selection, cluster management, and data transmission for all three services, are conducted at experimental rounds. In the standard LEACH procedure [15], cluster segmentation and CH selection are common evidence for maximum homogeneous network energy source utilization. In contrast, the proposed CRS accommodates all three essential services common in smart cities and renders an efficient transmission process across the heterogeneous network. One of the prominent procedures of CRS is clustering that is processed based on service type. The choice of the CH selection for each cluster is based on the weight-age factors such as service priority, ranking strategy, and residual energy. Next, every member node of each cluster communicates according to the DD-TDMA scheduling. CRS primarily uses energy sources to transmit sensed data rather than cluster formation, control overheads, etc. There are significant differences between CRS and S-LEACH, especially at clustering stages. The S-LEACH employs a dynamic cluster that is refreshed after each round, with CH selection happening between rounds. The cluster creation mechanism causes reliable connectivity and essential information exchange that use enormous energy. In contrast,  
Figure 11: FT routing during inter-cluster transmission. Figure 10: _Case\({}_{3}\)_ of intra-cluster routing. CRS is based on nodes' service type and transmission period at inter and intra-level clusters using optimal energy sources. When they are formed, clustered regions stay unchanged throughout the network's operations, but enhancements are allowed only for newly updated nodes. As you can see in Figures 12 and 13, the proposed CRS saves a significant proportion of node energy. Furthermore, the CH selection method relies on weight-age factors accumulated over time, enabling each node a potential CH. CRS protocols' efficiency in energy consumption is much superior to that of S-LEACH and LEACH-C protocols. CRS and LEACH-C pose a similar set of stages, including cluster formation, selection of CH, and data routing. CRS consumes much less energy than LEACH-C and S-LEACH during the cluster formation and CH selection stages. In Figure 14, the average energy used for the cluster formation process is shown for scenarios 1 and 2 with transmission ranges of 50 and 100 meters, respectively. Similarly, Figure 15 depicts the average energy used for the CH selection process. The second evaluation metric is based on the total count of packets generated by various sensing nodes and delivered to the BS via corresponding CH. CRS seems to have a greater percentage of packet reachabi  
Figure 12: Overall energy utilization vs. experimental rounds (intra-cluster level). Figure 13: Overall energy utilization vs. experimental rounds (inter-cluster level). either LEACH-C or S-LEACH, which have similar network lifespans. The CRS manages its communication hops to minimize packet losses due to collision and increases the packet reachability ratio to the CH/BS.