Abstract
This study introduces delay-aware cloudlet placement with a modified k-means (DACP-mk) algorithm to address the complexities of mobility and optimize cloudlet placement in a mobility-enhanced environment. DACP-mk enhances the traditional k-means algorithm by incorporating modified regional and global delay matrices to enable the simulation of a delay-aware cloudlet movement and placement strategy using a cluster-based approach within a smart healthcare platform. An optimized cloudlet position is generated to achieve a uniform distribution of mobile devices among placed cloudlets, maximize device coverage, and minimize network access delay within the cloudlet coverage distance. The proposed algorithm determines the optimal positions for cloudlets at coordinates (161.90, 103.24), (080.22, 139.52), and (080.29, 051.21) with an increase of 02.42%, 00.54%, 38.71%, and 49.00%, respectively, in terms of global mobile device coverage compared to modified k-means Manhattan (mkMAN), k-means, energy-efficient cloudlet placement method (ECPM) and enhanced adaptive cloudlets placement with covering algorithm (EACP-CA), and a decrease of 00.71% and 58.27% in global network access delay compared to mkMAN and k-means algorithms. The proposed algorithm offers enhanced healthcare support and cost-effective services, improving the Quality of Service (QoS) and ultimately contributing to saving lives.


























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Data availability
The dataset can be downloaded from https://drive.google.com/open?id=1pThqWafKfniPwqCcFqe-JoIZPIVrEW2r [23].
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All authors have significantly contributed to developing the algorithm and writing the paper. Lizia Sahkhar wrote the main manuscript and performed the simulation. Bunil Kumar Balabantaray helped review and analyze the algorithms. Sanjaya Kumar Panda helped code and analyze the results. All authors reviewed the manuscript thoroughly.
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Sahkhar, L., Balabantaray, B.K. & Panda, S.K. Mobility-enhanced delay-aware cloudlet movement and placement using cluster-based technique in a smart healthcare platform. Cluster Comput 27, 11803–11821 (2024). https://doi.org/10.1007/s10586-024-04569-2
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DOI: https://doi.org/10.1007/s10586-024-04569-2