# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 4 Energy Efficient GA-Based Multipath Routing
### Multipath Formation Using Genetic Algorithm
#### 4.2.4 Mutation
* _Energy Consumption_**:** This parameter provided node's energy dissipation in the event region per round, and m is the number of nodes in the cluster: \[E_{cons}(i)=\sum_{i=1}^{m}E(i)-E_{TX}\left(i\right)\] (20)
* _Standard deviation (SD)_**:** This parameter showed the variation between the individual node's energy consumption versus the mean energy consumption of the nodes in the network per round. Small SD values signify better load distribution in the network. \[SD=\sqrt{\frac{\sum_{i=1}^{n}\left(E_{res}(i)-E_{avg}\right)^{2}}{n}}\] (21)
* _Lifetime of Network_**:** This parameter provided the duration for which the network would continue to serve the purpose it wasmadefor, without failing. There are various definitions given for network lifetime by researchers [50; 51]. Some researchers define network lifetime as the duration until a certain percentage of nodes die or remain alive: first node die (FND), half node die (HND), and last node die (LND). Similarly, we defined network lifetime as the time the network took for the first node to die (FND). ### Discussion of Results

We evaluated the efficiency of the four algorithms in terms of mean residual energy of nodes in the network, energy of consumption of nodes in the event area, the standard deviation of the residual energy of nodes against the mean energy dissipation of the network, and the network lifetime, using FND. We examined the performance of the protocols both under the homogeneous network and under the heterogeneous network. By homogeneous network, we mean the entire sensor nodes had the same initial energy and other characteristics, whereas the heterogeneous network means the nodes had different initial energy. #### 5.2.1 Homogenous Network

In scenario 1, we evaluated and compared the algorithms in a homogeneous sensor network of 100 to 500 nodes, as depicted in Figure 5. The simulation results are shown for the four metrics. EMRGA outperformed the other existing algorithms in all the parameters as illustrated from Figures 5-8. Figure 5 illustrates the mean energy of sensor nodes by applying the four algorithms. EMRGA is observed as having higher average energy than the other protocols. The simulation result revealed that the proposed algorithm consumed less energy for data communication. Though the performance was much higher than the other algorithms in terms of average residual energy, EMRGA showed minor fluctuations when the number of nodes was 300. In Figure 6, it is observed that in the denser network, the energy consumption of EMRGA also linearly increased. Higher traffic was also observed due to the higher number of nodes in the network. Though MRP and MP both had better energy consumption, the proposed EMRGA still outperformed all the other algorithms for total energy consumption in the network. TEEN uses a dynamic clustering algorithm, but it consumed more energy than EMRGA. In the case of MACS, it had a better energy consumption rate than TEEN. Even though MP is not a hierarchical routing technique (flat), its primary path utilized a small amount of energy in contrast to MACS and TEEN. According to Figure 7, the standard deviation of energy consumption of each node in the network from the mean energy utilization of the network is lower in the case of the proposed protocol, EMRGA, when compared to other algorithms for all numbers of nodes in the scenario. This provides assurance that it is efficient enough in energy consumption and load balancing across the sensor nodes regardless of the number of nodes in the network. When the number of nodes in the network linearly increased, the SD fluctuated up and down. However, when the network size was greater than 400, the SD

Figure 5: Average residual energy. Figure 6: Energy consumption. decreased continuously ensuring that the energy consumption in the network was maintained at a low level.