@article{balakrishna_d-acsm_2022,
	title = {D-{ACSM}: a technique for dynamically assigning and adjusting cluster patterns for {IoT} data analysis},
	issn = {1573-0484},
	shorttitle = {D-{ACSM}},
	url = {https://doi.org/10.1007/s11227-022-04427-1},
	doi = {10.1007/s11227-022-04427-1},
	abstract = {With rapid advancements in wireless communications and sensor technologies, the Internet of Things (IoT) has advanced dramatically in past years. In IoT, the data created by a large number of sensors are extremely intricate, diverse, and enormous, and it is unprocessed. These may have underlying patterns that are not visible that must be discovered to do large-scale data analysis. Several clustering algorithms have been developed and proved effective in data analysis in recent decades; however, they are intentionally designed for dealing with static data and infeasible for processing huge data in IoT environments. As a result, this research proposes a Density-based Adaptive Cluster Split and Merge (D-ACSM) technique for dynamically assigning and changing cluster patterns for IoT data processing to solve this challenge. For successful cluster analysis, the local density and minimum distance between dynamic data objects were first measured. In addition, the D-ACSM technique used Cluster Splitting and Merging (CSM) to alter cluster patterns between surrounding dynamic data objects. In addition, the suggested D-ACSM technique’s results were evaluated using four IoT benchmarked datasets that varied in the number of arriving data objects. Finally, the proposed D-ACSM technique improves the results of the performance metrics by 4\%, 5\%, 3\%, and 6\% on the BWS-AS dataset, CRAWDAD dataset, Minute\_Weather dataset, and LinkedSensorData dataset, respectively, when compared to the AC-ICSM, IMMFC, and IAPNA techniques used for cluster analysis in all data chunks.},
	language = {en},
	urldate = {2022-03-17},
	journal = {The Journal of Supercomputing},
	author = {Balakrishna, Sivadi},
	month = mar,
	year = {2022},
}

{"_id":"MtXPLfS9XSps3axtd","bibbaseid":"balakrishna-dacsmatechniquefordynamicallyassigningandadjustingclusterpatternsforiotdataanalysis-2022","author_short":["Balakrishna, S."],"bibdata":{"bibtype":"article","type":"article","title":"D-ACSM: a technique for dynamically assigning and adjusting cluster patterns for IoT data analysis","issn":"1573-0484","shorttitle":"D-ACSM","url":"https://doi.org/10.1007/s11227-022-04427-1","doi":"10.1007/s11227-022-04427-1","abstract":"With rapid advancements in wireless communications and sensor technologies, the Internet of Things (IoT) has advanced dramatically in past years. In IoT, the data created by a large number of sensors are extremely intricate, diverse, and enormous, and it is unprocessed. These may have underlying patterns that are not visible that must be discovered to do large-scale data analysis. Several clustering algorithms have been developed and proved effective in data analysis in recent decades; however, they are intentionally designed for dealing with static data and infeasible for processing huge data in IoT environments. As a result, this research proposes a Density-based Adaptive Cluster Split and Merge (D-ACSM) technique for dynamically assigning and changing cluster patterns for IoT data processing to solve this challenge. For successful cluster analysis, the local density and minimum distance between dynamic data objects were first measured. In addition, the D-ACSM technique used Cluster Splitting and Merging (CSM) to alter cluster patterns between surrounding dynamic data objects. In addition, the suggested D-ACSM technique’s results were evaluated using four IoT benchmarked datasets that varied in the number of arriving data objects. Finally, the proposed D-ACSM technique improves the results of the performance metrics by 4%, 5%, 3%, and 6% on the BWS-AS dataset, CRAWDAD dataset, Minute_Weather dataset, and LinkedSensorData dataset, respectively, when compared to the AC-ICSM, IMMFC, and IAPNA techniques used for cluster analysis in all data chunks.","language":"en","urldate":"2022-03-17","journal":"The Journal of Supercomputing","author":[{"propositions":[],"lastnames":["Balakrishna"],"firstnames":["Sivadi"],"suffixes":[]}],"month":"March","year":"2022","bibtex":"@article{balakrishna_d-acsm_2022,\n\ttitle = {D-{ACSM}: a technique for dynamically assigning and adjusting cluster patterns for {IoT} data analysis},\n\tissn = {1573-0484},\n\tshorttitle = {D-{ACSM}},\n\turl = {https://doi.org/10.1007/s11227-022-04427-1},\n\tdoi = {10.1007/s11227-022-04427-1},\n\tabstract = {With rapid advancements in wireless communications and sensor technologies, the Internet of Things (IoT) has advanced dramatically in past years. In IoT, the data created by a large number of sensors are extremely intricate, diverse, and enormous, and it is unprocessed. These may have underlying patterns that are not visible that must be discovered to do large-scale data analysis. Several clustering algorithms have been developed and proved effective in data analysis in recent decades; however, they are intentionally designed for dealing with static data and infeasible for processing huge data in IoT environments. As a result, this research proposes a Density-based Adaptive Cluster Split and Merge (D-ACSM) technique for dynamically assigning and changing cluster patterns for IoT data processing to solve this challenge. For successful cluster analysis, the local density and minimum distance between dynamic data objects were first measured. In addition, the D-ACSM technique used Cluster Splitting and Merging (CSM) to alter cluster patterns between surrounding dynamic data objects. In addition, the suggested D-ACSM technique’s results were evaluated using four IoT benchmarked datasets that varied in the number of arriving data objects. Finally, the proposed D-ACSM technique improves the results of the performance metrics by 4\\%, 5\\%, 3\\%, and 6\\% on the BWS-AS dataset, CRAWDAD dataset, Minute\\_Weather dataset, and LinkedSensorData dataset, respectively, when compared to the AC-ICSM, IMMFC, and IAPNA techniques used for cluster analysis in all data chunks.},\n\tlanguage = {en},\n\turldate = {2022-03-17},\n\tjournal = {The Journal of Supercomputing},\n\tauthor = {Balakrishna, Sivadi},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n\n\n","author_short":["Balakrishna, S."],"key":"balakrishna_d-acsm_2022","id":"balakrishna_d-acsm_2022","bibbaseid":"balakrishna-dacsmatechniquefordynamicallyassigningandadjustingclusterpatternsforiotdataanalysis-2022","role":"author","urls":{"Paper":"https://doi.org/10.1007/s11227-022-04427-1"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":[],"search_terms":["acsm","technique","dynamically","assigning","adjusting","cluster","patterns","iot","data","analysis","balakrishna"],"title":"D-ACSM: a technique for dynamically assigning and adjusting cluster patterns for IoT data analysis","year":2022}