IoT Search Method for Entity Based on Advanced Density Clustering

Dong, Chang; Jiang, Lingyun; Wang, Ke

doi:10.1109/ictc49638.2020.9123296

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Finally, C. Dong et al [2] propose a method named A-DBSCAN which clusters devices using DBSCAN. This generates clusters with high device density.…”

Section: Related Workmentioning

confidence: 99%

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RILCE: Resource Indexing in Large Connected Environments

Achkouty,

Mansour,

Gallon

et al. 2023

New Trends in Database and Information Systems

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The purpose of this study is to present a solution to a particular challenge related to indexing resources in a connected environment. In essence, multiple factors impact significantly resource indexing in connected environments such as uneven spatial distribution of devices, their heterogeneous capacities, varying usage, and querying purposes and priorities, to mention a few. This work proposes a hybrid resource indexing approach able to cope with devices' capacities (especially their storage capacity). To validate our approach, several types of queries have been submitted to test the index's efficiency. Preliminary experiments show promising results and prove the efficiency and usefulness of the proposal.

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“…Finally, C. Dong et al [2] propose a method named A-DBSCAN which clusters devices using DBSCAN. This generates clusters with high device density.…”

Section: Related Workmentioning

confidence: 99%

“…Yes Fast Undefined Multi-index [9] No Fast Undefined A-DBSCAN [2] No Fast Covered Our approach Yes Default Both…”

Section: Bccf-tree [1]mentioning

confidence: 99%

RILCE: Resource Indexing in Large Connected Environments

Achkouty,

Mansour,

Gallon

et al. 2023

New Trends in Database and Information Systems

View full text Add to dashboard Cite

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Resource Indexing and Querying in Large Connected Environments

Achkouty,

Chbeir,

Gallon

et al. 2023

Future Internet

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The proliferation of sensor and actuator devices in Internet of things (IoT) networks has garnered significant attention in recent years. However, the increasing number of IoT devices, and the corresponding resources, has introduced various challenges, particularly in indexing and querying. In essence, resource management has become more complex due to the non-uniform distribution of related devices and their limited capacity. Additionally, the diverse demands of users have further complicated resource indexing. This paper proposes a distributed resource indexing and querying algorithm for large connected environments, specifically designed to address the challenges posed by IoT networks. The algorithm considers both the limited device capacity and the non-uniform distribution of devices, acknowledging that devices cannot store information about the entire environment. Furthermore, it places special emphasis on uncovered zones, to reduce the response time of queries related to these areas. Moreover, the algorithm introduces different types of queries, to cater to various user needs, including fast queries and urgent queries suitable for different scenarios. The effectiveness of the proposed approach was evaluated through extensive experiments covering index creation, coverage, and query execution, yielding promising and insightful results.

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IoT Search Method for Entity Based on Advanced Density Clustering

Cited by 2 publications

References 8 publications

RILCE: Resource Indexing in Large Connected Environments

RILCE: Resource Indexing in Large Connected Environments

Resource Indexing and Querying in Large Connected Environments

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