Abstract
One of the fundamental problems in the study of distributed systems is mutual exclusion. In the past, several solutions to this fundamental issue have been put up in the literature. These solutions, which consist of distributed mutual exclusion algorithms, have been applied to both static and dynamic network topologies. Unmanned aerial vehicles (UAVs) are currently popular due to strong demand and a wider area of application, and they also have a flying ad hoc network topology, in which the proposal of these algorithms is constrained by the network's adaptability. The machine learning approach gives hardware the ability to learn and comprehend based on its model and previous data. To the best of our knowledge, no literature has yet suggested integrating machine learning into the field of distributed mutual exclusion. Hence through this research article, we first propose a novel mutual exclusion solution as mutual exclusion algorithm for flying network-density based approach (MEAFN-DBA) to a UAV-assisted network through density-based spatial clustering of applications with noise clustering scheme and present various performance measures later on along with the result analysis with a comparison to existed work in the same domain with satisfying results.Kindly check the affiliatons are correctly identified.
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Parihar, A.S., Chakraborty, S.K., Sharma, A. et al. A comparative study and proposal of a novel distributed mutual exclusion in UAV assisted flying ad hoc network using density-based clustering scheme. Wireless Netw 29, 2635–2648 (2023). https://doi.org/10.1007/s11276-023-03327-3
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DOI: https://doi.org/10.1007/s11276-023-03327-3