# 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 evident from the result that the standard deviation of residual energy and energy load of the selected CHs for the proposed HIOA algorithms is more balanced compared to EC-PSO [28], CRGA [29] and LEACH-FL [27]. This can be attributed to the reason that it's minimizing the energy consumption during the selection of the fitness function of CHs by considering inter-cluster and intra-cluster distance. In addition, GA operators (crossover and mutation) help in reducing the convergence time in selecting the global optimum set of CHs, which increases population diversity and not to select local optimum CH sets. EC-PSO forms the clusters according to swarm particle optimization, and normal nodes join the cluster according to the distance from the edge node and left behind the number of neighboring IoT nodes. This can be attributed to unbalanced energy consumption in the cluster. It can also be noticed that CRGA and LEACH-FL show variations in the standard deviation of the IoT node's residual energy and load balancing by CHs. It is due to the fact both CRGA and LEACH-FL algorithms choose the CHs randomly, in turn, unevenly distributing the load on the CHs and reducing residual energy. The worst performance is shown by LEACH-FL because probabilistic selection generates some isolated CHs that consume much more energy in the transmission of data to edge nodes. Thus, the overall result proved that the standard deviation of residual energy of the proposed algorithm

Figure 8: Average residual energy over rounds. Figure 9: Standard deviation over rounds: (**a**) residual energy and (**b**) CH load. HIOA was lower than the EC-PSO, CRGA and LEACH-FL by 18%, 26% and 42%, respectively. The load balancing of the proposed algorithm is also 23%, 32% and 48% lower than state-of-the-art algorithms. ## 5 Conclusions and Future Scope

In this paper, we proposed a new hybrid algorithm for clustering in the IoT network based on FL and CSO employing a GA to minimize energy dissipation. To this end, tentative CHs are selected using FL by considering essential parameters. Further, enhanced CSO with GA utilizes the concept of hierarchal order (rooster \(\rightarrow\)hen \(\rightarrow\)chicks) to divide the population and their fitness values are evaluated in order to find the best of nodes as CHs using crossover and mutation operation of GA. Simulations conclude that our proposed algorithm HIOA is robust to different CSO parameters and finds a near-optimal solution with low time complexity and higher convergence speed over state-of-the-art algorithms. The particle implementation of the simulate model can be used for load balancing or self-organizing structure in IoT nodes (electric vehicles) for the energy buying market. The presented HIOA model effectively captures the rapid and frequently changing traffic patterns due to the inherent feature of GA and the dynamic classification property of CSO with a lower rate of latency for convergence. As the proposed model has no prediction ability for a sustainable network where electric vehicles are used as nodes, to add these features in the future, we included a neural network or deep learning approach for the formation of clusters based on past experience [34]. The nearby models include training and testing and deployment as well in different network scenarios. Conceptualization, M.K. ; Normal analysis, M.K. ; Investigation, M.K. ; Methodology, P.K.K. ; Resources, M.K., G.A. and R.S.R. ; Supervision, S.K., O.K. and J.L. ; Validation, R.S.R. ; Writing, M.K. ; Review & Editing, P.K.K., O.K. and R.S.R. All authors have read and agreed to the published version of the manuscript. This research was funded by Jawaharlal Nehru University, New Delhi, India, and partially supported by Cardiff Metropolitan University, UK. The research work is scientifically supported by Nottingham Trent University, UK. The authors declare no conflict of interest. ## References

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