## 5 Performance Evaluation and Discussion  
In this section, we evaluate the performance of our proposed clustering and routing models. We conduct our evaluation in a simulated environment with the following specifications. A total of \(N=100\) nodes are randomly distributed in a 100 m \(\times\) 100 m field. \(m=30\) nodes are scattered in the first level physical circle with normal initial energy and range 30 m around the BS, and \(n=70\) nodes place outside that circle. The range of the first level can adjust during the network initialization period depending upon the type of required information. The only condition of node deployment is that the first-level zone must contain the homogeneous nodes only. In the second level of the network, 20% nodes (b = 14 nodes) have advanced initial energy levels. The BS is located in the center of the network. During all communications, the first-order energy model is utilized, which is shown in Table 1. The nodes' initial energy is set as 0.5 Joules for the homogeneous nodes of the first-level network region, and nodes in heterogeneous parts of the network have higher energy than the nodes in the first-level network. Each node consumes its limited energy during cluster formation, sensing, aggregation, and transmission or receiving data. Once the energy resources of a specific node reach zero, then it is automatically considered as dead. The initial energy values for the second-level network are selected randomly. The size of a packet is 4000 bits. The desired percentage of cluster heads remains the same in both of the network parts. The limit of cluster formulation of the proposed protocol is based on the desired percentage value. In this paper, we define the desired percentage at 10% of the total alive nodes. In a centralized region, this desired percentage is strictly forced by the BS. Meanwhile, in the distributed region, we use a probabilistic formula (Equation (16)) to compute the threshold for the nodes to be selected as CH. This probabilistic formula uses the value of the desired percentage (\(p=0.1\)) to compute the threshold value. This threshold value is compared with the random number generated over the nodes participating in the clustering process. If the value of the random number of a node is less than the threshold value, then it is selected as CH. In this way we control the desired percentage of the CHs. This paper collects two types of information: temperature and humidity, on the same node (programmable sensor DHT11) with hard threshold and soft threshold values. The selection of information depends upon the self-generated random number by each non-cluster head in each round. We first evaluate our proposed approach's performance using the popular Network Simulator (NS) version 2.35 [45]. Our proposed model is tested by single-hop and multi-hop transmission techniques with the well-known existing clustering routing protocols, i.e., the hybrid advanced distributed and centralized clustering path planning algorithm (HADCC) [34], threshold distributed energy-efficient clustering (TDEEC) [18], threshold-sensitive stable election protocol (TSEP) [17], threshold-sensitive energy-efficient sensor network (TEEN) [21], low-energy adaptive clustering hierarchy (LEACH) [19], and LEACH-centralized (LEACH-C) [20]. We ignored the signal collision and interference effects with similar parameters for all protocols used in our evaluation to achieve more realistic and accurate results. The selected performance parameters are shown in Table 1.  
For evaluation of a real-world scenario, we deployed a 100 m\(\times\)100 m wheat farm in the spring season (March 5-25) in the Potohar plateau of Pakistan. Table 2 shows the collected information in the real-world scenario. The performance of the proposed model shows almost similar trends as observed in simulations.