Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

Manoj Kumar, Sushil Kumar, Pankaj Kumar Kashyap, Geetika Aggarwal, Rajkumar Singh Rathore, Omprakash Kaiwartya, Jaime Lloret

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Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algo-rithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter-and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters popsize = 60, number of rooster Nr = 0.3, number of hen’s Nh = 0.6 and swarm updat-ing frequencyθ = 10. Further, comparative results proved that HIOA is more effective than tradi-tional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

Original languageEnglish
Article number3910
Number of pages17
Issue number10
Publication statusPublished - 21 May 2022
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgments: The research work is scientifically supported by Nottingham Trent University, UK.

Funding Information:
Funding: This research was funded by Jawaharlal Nehru University, New Delhi, India, and partially supported by Cardiff Metropolitan University, UK.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


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