## 4 Comparative Analysis of Proposed Solution with EADUC and TLPER  
Though the presented ideas related to cluster designing, cluster head selection, node affiliation to CH as CM, forwarding node selection, inter- and intra-cluster routing and cluster head rotation. These are all combined under the name Energy Aware Routing for Multilayer Cluster Designing Architecture (EAR4 MCDA), there are close similarities with some existing techniques in one functional aspect or the other. For this very reason, we have selected TLPER and EADUC for the performance evaluation comparison of our proposed technique. We have given sufficient details of the workings of both the algorithms in the literature survey section. The authors of TLPER compared its performance with LEACH on energy consumption per node, cluster head, and assistant cluster head, network utilization,  
Figure 5: Operation of threshold levels in forwarding node rotation. and load balance effect on energy consumption, where it outperforms its competitor LEACH. The performance of EADUC on network lifetime is compared with EEUC, LEACH-M, LEACH and HEED and the authors found it better in this aspect in comparison to its competitors. In this section, a comprehensive discussion of a comparative analysis of EAR4 MCDA with state-of-the-art related algorithms--TLPER and EADUC--is provided, based on the following performance metrics:  
* Energy consumption in cluster design
* Before the start of the operational phase of the network, it needs to be setup for it. We have measured the energy that is consumed in designing the clusters, _i.e._, making the network ready for the operational phase. * The current node needs to select a node among its neighbors for pushing the data ahead to the BS. That selected node is termed the forwarding node. We have measured the energy consumption that is used in information communication for the selection of forwarding nodes. * Energy consumption for transmitting one packet from source to destination
* This energy measurement is directly related to the routing part of the algorithm. We have calculated the energy that is consumed for dissemination of generated data from the far distant network node until the BS. Other aspects of energy consumption are also included in it, e.g., forwarding node selection, _etc._
* No. of Designed Clusters
* Total numbers of clusters that are designed in the network. * Average number of hops from end to end
* The nodes are deployed randomly and the network adopts the shape of a Voronoi diagram. This results in variable end to end (E2E) distances and therefore a variable E2E number of hops. We have taken the average of these E2E number of hops as part of the performance metrics list. * Number of packets produced in the first 60 s after cluster formation
* During the network operation phase, we have measured the number of data packets produced in first 60 s. This performance metric is used with the generated throughput in the first 300 s.
* Successful delivery of data packets is also taken as another performance evaluation parameter. We have measured the throughput in the first 300 s of the network operation phase. * Energy consumption for total throughput in the first 300 s
* We have calculated the energy consumption for successful reception of data packets at the BS in the first 300 s of the operational phase. * Energy consumption for cluster head rotation
* Energy consumption during CH rotation encompasses all the activities that are undertaken from the time of CH rotation until this designation is completely transferred to a new node. We have run the simulation a number of times in a network of 500 nodes in an area of 500 m \(\times\) 500 m. The succeeding results from Figures 6-15 represent the average of the number of executions. Simulation parameters are given in Table 3.  
The contribution of node energy in the data routing process starts from the cluster design process and ends with the data approaching the sink.