# Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
## 3 Green Communication in the Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
### Hybrid Intelligent Energy Optimization
#### 3.3.2 Adapted Chicken Swarm Genetic Optimization for Green Communication
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes (\(N\)) & 200 \\ Network size & \(150\times 150\) \\ Percentage of CH & 5 \\ Packet size & 4000 bit with 100 bit header \\ Initial Energy & 1 J \\ \(E_{uy}\) & 5 nJ/bit/message \\ \(E_{T}\) & 50 nJ/bit \\ \(\partial_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(\partial_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \(\vartheta\) & 0.3 \\ \(\tau\) & 0.006 \\ Cycle time & 60 \(\upmu\)s \\ Crossover rate & 0.7 \\ Mutation rate & 0.1 \\ Population size & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. functions: inter-cluster, intra-cluster distance and energy consumption. This reveals that the presented algorithm is able to balance the energy consumption and average distance properly. Thus, the best fitness value of the proposed algorithm HIOA is obtained through setting up the parameters \(pop_{size}=60\), \(N_{r}=0.3\), \(N_{h}=0.6\) and \(\theta=10\). In addition, the number of chicks can be evaluated as \(N_{c}=1-0.3-0.6=0.1\). In the next upcoming simulation, the above parameters are set with their values. ### Network Parameter Analysis

In this section, a comparison of the HIOA with LEACH-FL [27], CRGA [29] and EC-PSO [28] algorithms has been analyzed to show the effectiveness in terms of network lifetime, average energy consumed per node at each round and the total average energy consumed per round. #### 4.2.1 Comparison of Active Nodes over Rounds

In Figure 5, the number of active IoT nodes is plotted per round to show the effectiveness of the proposed algorithms compared to state-of-the-art algorithms. It is evident from the result for the starting phase of all the algorithms, up to 300 rounds around 98% IoT nodes are alive/active except in LEACH-FL only 87%. Further, on enhancement in rounds, the proposed algorithm shows the best performance compared to EC-PSO, CRGA and LEACH-FL. This is due to the fact that each chicken in the proposed algorithm acts as an agent, which updates its position in the network areas to become CH or not. In addition, this agent (chicken) has information about the whole group (cluster) and fairly balances the energy consumption load using GA crossover and mutation operator. Further, the EC-PSO algorithm does not have the ability to multi-swarm optimization and lacks adaptation to changes in the network. Whereas, CRGA do not has agent so that keep information's of other chromosomes in a population. It is also worth noting that the number of active nodes in LEACH-FL is only 20 after crossing 800 rounds and further increments in the number of rounds in turn all the nodes die. This result confirms that LEACH-FL is not able to achieve fairness in network load balance and residual energy of IoT nodes. Thus, overall,

Figure 4: Optimization performance of HIOA over iterations (**a**) \(pop_{size}\) (**b**) \(N_{r}\) (**c**) \(N_{h}\) (**d**) \(\theta\). the proposed algorithm HIOA outperforms EC-PSO, CRGA and LEACH-FL by 12%, 19% and 29%, respectively, in terms of the number of active nodes remaining in the 900 rounds. #### 4.2.2 Comparison of Network Lifetime over Rounds

A comparison of the network lifetime of the proposed algorithm and state-of-the-art algorithms is shown in Figure 6. The network lifetime is the time at which the first node dies (FND) or half of the nodes die (HND) and the last node dies (LND). It can be clearly observed from the result the proposed algorithm HIOA achieves better lifetime in terms of FND is 11%, 18% and 48% from EC-PSO, CRGA and LEACH-FL, respectively. Further, HND is 27%, 35% and 58% compared with EC-PSO, CRGA and LEACH-FL, respectively. Furthermore, in terms of LND is 35%, 46% and 81% compared to EC-PSO, CRGA and LEACH-FL, respectively. This can be attributed to the reason that the proposed algorithm uses both fuzzy logic optimization with a chicken swarm genetic algorithm that helps in building a more feasible solution rather than only using particle swarm optimization in EC-PSO or a genetic algorithm in CRGCA.