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Internet of things has become an essential principle of human life with the widespread acceptance of intelligent environments, where everyday objects can communicate through the internet. Mobility is the most critical factor in today's internet of things devices to apply in real-world applications. Also, proper routing protocol plays a vital role in communication and reduces devices' energy consumption. The clustering approach is one of the efficient routing techniques to improve energy consumption and enhance network life. Due to the NP-Hard nature of clustering, a krill herd optimization algorithm is proposed in this paper to select the cluster head nodes and intermediate nodes required for routing. The simulation results using NS-3 confirmed that the proposed technique performs better than particle swarm optimization and cuckoo search in terms of network lifetime. The proposed technique increases the total network lifetime by at least 11.1% compared to the current clustering methods. [ABSTRACT FROM AUTHOR]

Traditional k-means clustering algorithm is sensitive to the choice of initial cluster centers and leads to local optimal results. k-means++ is a hybrid k-means clustering algorithm which specifies the procedure to initialize the cluster centers before proceeding with the standard k-means algorithm. Inspired by nature, some contemporary optimization techniques such as Cuckoo, Bat and Krill Herd algorithms etc., are used for optimization as they mimic the swarming behaviour and allows to cooperatively move towards an optimal objective within a reasonable time. In this paper, these nature-inspired techniques are used for optimizing k-means++ clustering algorithm to enhance clustering quality and generate new hybrids of unprecedented performance. The results of the evaluation experiments on the integration of nature-inspired optimization methods with k-means++ algorithm are reported. [ABSTRACT FROM AUTHOR]