# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 5 Proposal of SADFIR
### Network Forwarding Phase (NFP) of SADFIR
### Heterogeneity-Awareness and Network Scalability in Different Network Environment

In order to check the heterogeneity-awareness and scalability of the proposed SADFIR, we deploy higher numbers of sensor nodes in different scales of network areas. In particular, we deploy 200, 300 and 500 sensor nodes in the network area of 200 m \(\times\) 200 m, 300 m \(\times\) 300 m, 500 m \(\times\) 500 m and runs the network for 5000, 7000 and 10,000 communication rounds respectively. In Figure 9, we present the heterogeneity-awareness of SADFIR in order to understand the network life-time and scalability of the network. In all the graphs of Figure 9, it is demonstrated that SADFIR outperforms the existing routing protocols and show better scalability for more large and dense IoT network scenarios. In particular, in Figure 9a, the sensor nodes in SADFIR are still alive until the end of network time, while the sensor nodes in other routing protocols are dead far before SADFIR. Similarly, in Figure 9b, EESRA and EBRP perform significantly, but still, SADFIR outperforms all of them and remain active until the end of network time. In Figure 9c, SADFIR outperform the EBRP by 810 communication rounds, whereas all the other routing protocols exhaust their total energy before EBRP routing protocol. The reason behind this efficient performance of SADFIR is because the multiple distributed SDN controllers avoid the reconfiguration time and forward the packets with a minimum rate of communication energy. In addition, the SDN controller sends only critical data, therefore higher number of sensor nodes favour the SADFIR and subsequently enhance the communication efficiency. These results indicate the better heterogeneity-awareness and scalability once the density and network area is increased. Figure 8: Energy Efficiency in terms of network lifetime in different scales of network area. ### Residual Energy of Sensor Nodes

The dispersed sensor nodes are supposed to transmit the critical data continuously to the BS, therefore higher levels of the initial energy of sensor nodes can prolong the network lifetime. This initial energy initializes the network and helps in selecting the CHs in the first epoch. These CHs consume the energy to communicate with the BS. Hence, the residual energy of sensor nodes is to be considered to select the CHs in the next epoch. Whereas, the higher energy require the bigger size of the battery, and due to the matter of the fact that the sensor nodes are supposed to be deployed in the critical network, therefore we cannot set the initial energy to the higher numbers. To this end, in this paper, we consider the real-time applications of WSNs and set considerable amounts of initial energy to the sensor nodes. In Figure 10, we test the network lifetime of the proposed model in different scales, where we set the initial energy to \(0.5,1.0,1.5,2.0,2.5\) and \(3.0\) and disperse the \(100\) sensor nodes in the \(100\) m \(\times\)\(100\) m, \(200\) m \(\times\)\(200\) m, \(300\) m \(\times\)\(300\) m, \(600\) m \(\times\)\(600\) m, \(800\) m \(\times\)\(800\) m, and \(1000\) m \(\times\)\(1000\) m network areas, respectively and run the model for \(5000\) communication rounds. In this Figure 10, ACEEC performs worst because of the single-hop communication and central control of route selection whereas, EBRP tried to compete the proposed SADFIR in the first two graphs but failed to prolong on the bigger scale of networks. Figure 10 proves that the proposed model SADFIR produces the optimal results not only in conventional settings of WSNs but also in the bigger scale of networks. This is due to the matter of fact that in the proposed SADFIR, the multiple distributed SDN controllers help the sensor nodes to adopt reprogrammable configurations with an understanding of residual energy levels. Figure 10: _Cont._

Figure 9: Energy Efficiency in terms of network lifetime with different numbers of sensor nodes in larger scales of network area.