# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 5 Performance Evaluation and Discussion
### Justification of the Proposed Protocol  
The following two points demonstrate the reason UN-LEACH protocol is chosen over the other protocols. 1. From cluster head selection perspective: To control the number of generated clusters, energy and Competition Radius are the main factors considered in our proposed protocol. These two factors are also inherent in the conventional UN-LEACH protocol. 2. From energy balance perspective: Similar to the UN-LEACH protocol, our proposed protocol is based on dividing the network into a hierarchical unequal clusters to balance the energy consumption among the cluster heads and to avoid hotspot issue that can be caused by overloading cluster heads closer to the central station. However, the following two points are peculiar to our proposed technique which are not present in the UN-LEACH. 1. From topology perspective: Although the UN-LEACH protocol uses a single-hop topology for both the inter-cluster communication and intra-cluster communication, our proposed MIMO-HC protocol employs a mixed topology (i.e., a single-hop topology for the intra-cluster communication and multi-hop topology for the inter-cluster communication). 2. From scalability perspective: Our proposed technique aims to achieve improved network lifetime and better load balancing. Equally important, scalability of the proposed network model is investigated to illustrate its robustness to changes in the wireless network topology. Often, in the real-world, the network size can grow large, for example, due to a reasonable network expansion. For example, the authors of [14] have suggested K-means-based network partition algorithm to overcome multi-controller deployment issues in Software Defined Networking. Similar approaches can be used for a scalable process around the growth of wireless sensor nodes. Therefore, the proposed energy-efficient routing protocol should perform as well as at times where the network grows larger. For this purpose, the proposed technique should have the capability to support the scalability of the wireless network topology. Therefore, due to the factors mentioned above, we believe that it sounds reasonable to implement to the proposed protocol. Accordingly, the performance evaluations of our proposed methodology are benchmarked against the UN-LEACH protocol. In particular, to justify and evaluate the theoretical concepts of our proposed method several experiments have been carried out through MATLAB software tool. First, Cluster Head distribution and delay are investigated. In addition, our simulation results are compared with existing clustering algorithm UN-LEACH [11] in terms of energy consumption by the cluster heads, network lifetime, and the number of nodes alive when using the clustering protocols under the 5G environment. For our simulation environment, it is presumed that multiple wireless communication interfaces are usable on sensor device, where the sensor node can select between them according to its needs. The simulation environment is based on the system model as described in Section 3.3. We have simulated an area in which sensor nodes in the sensing area are equally distributed and the central station positioned outside the WSNs. A simulation round is defined as a time interval in which each senor node transfers a packet size of 25 bytes to the central station. The simulation result illustrates that the energy model is the same as the energy model used in [12]. The simulation parameters of the proposed MIMO-HC are shown in Table 2. We set \(\alpha\),\(\beta\) to 0.5, \(R_{c}^{0}\) have a value between 0 and 200 in (3), \(Tx\_Th\) = 130 m, and \(c1\), \(c2\) have value of 1, 50 respectively in (7). When the last node in the network dies, the simulation rounds stop counting. In addition, because of the irregular time death for the last node in the network, the number of rounds is varied. Therefore, the simulation results are recorded as an average of 30 runs for every simulation inputs/scenarios.