# 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
This is also observed from the result that LEACH-FL shows the worst performance in terms of lifetime, where FND, HND and LND occur at 100, 336 and 982 rounds. It is due to the reason LEACH-FL selects the CH based only on residual energy and node distance, where node density is left out. There is no optimization, such as PSO, or GA is used to enhance performance. #### 4.2.3 Comparison of Average Energy Consumed over Rounds

A comparison of average consumed energy over rounds of the proposed algorithm HIOA and state-of-the-art algorithms is shown in Figure 7. Initially, all the state-of-the-art algorithms except LEACH-FL [27], up to 300 rounds, consume almost similar amounts of energy. Thereafter, on the enhancement of the number of rounds, the proposed algorithm

Figure 5: Number of active nodes over round. Figure 6: Network lifetime over round. HIOA consumes much less energy than other state-of-the-art algorithms and goes up to above 1200 rounds with a consumption of 0.75 Joule. In addition, it can be also observed that CRGA [29] and EC-PSO [28] consume almost 0.95 Joule and 0.85 Joule of energy within 972 rounds and 1030 rounds, respectively. This is due to the fact presented HIOA algorithm inherit CSGO that designated to innate multi-swarm optimization group with crossover and mutation technique of genetic algorithm, which enhances the efficient use of energy in the IoT network. EC-PSO is a single group swarm optimization technique that is lacking in the generation of feasible solutions of CHs throughout the IoT network. In addition, the fitness function of CRGA only includes residual energy parameter and left the inter-cluster distance and intra-cluster distance parameter, so that selection of CHs is not optimal. Thus, overall, the proposed algorithm HIOA outperforms EC-PSO, CRGA and LEACH-FL by 17%, 26% and 38%, respectively. #### 4.2.4 Comparison of Average Residual Energy over Rounds

A comparison of the average residual energy per IoT node with respect to the round between the proposed algorithms and state-of-the-art algorithms is shown in Figure 8. The average residual energy is calculated as dividing the overall residual energy per round by the total number of IoT nodes. It is obvious that the residual energy of IoT nodes that uses the presented HIOA has more residual energy (0.12 Joule) after completion of 1200 rounds, whereas CRGA [29] and EC-PSO [28] algorithms consume more energy in data communication and IoT nodes have only 0.05 Joule and 0.02 Joule of energy art 1000 rounds. This is due to the fact that the proposed algorithm HIOA optimally selects the CHs at each round and balances the energy consumption load among all the nodes within a cluster. This can be attributed to the proper division of IoT nodes in rooster, hens and chicks, where the mother-child (hens & chicks) relationship is also balanced. In addition, crossover and mutation operators also help in enhancing the selection of optimum set of CHs. It is also worth noting that initially the LEACH-FL [27] algorithm consumes less energy and IoT nodes have a handsome amount of residual energy left out. However, in the increment of rounds, the residual energy of IoT nodes declined sharply and almost zero within 892 rounds. This is because LEACH-FL ignores the inter-cluster and intra-cluster communication costs in the selection of CHs that consume extra energy. Thus, overall, the proposed algorithm HIOA has more residual energy compared to EC-PSO, CRGA and LEACH-FL by 11%, 16% and 21%, respectively. Figure 7: Average energy consumed over round. #### 4.2.5 Comparison of Standard Deviation over Rounds

A comparison of the standard deviation of residual energy and energy load balance by the CHs between the proposed algorithm and state-of-the-art algorithms is shown in Figure 9a and b, respectively. The standard deviation of residual energy measures the variability and consistency of the residual energy of the IoT nodes in the population.