Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks

Omeke, Kenechi G.; Mollel, Michael S.; Shah, Syed Tariq; Arshad, Kamran; Zhang, Lei; Abbasi, Qammer H.; Imran, Muhammad

doi:10.1109/cicn56167.2022.10008249

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To securely transmit healthcare data, active sensors are selected by the Archimedes algorithm and the shortest path is selected by an attribute-centered binary scheme [29]. Similarly, clustering and data aggregation is performed for underwater WSNs [30,31]. The first layer contains medical sensors to capture the patient's condition and data is transferred to a fog server.…”

Section: Clustering-based Data Aggregation Schemesmentioning

confidence: 99%

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Khan,

Cao,

Tang

et al. 2024

Future Internet

View full text Add to dashboard Cite

The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost.

show abstract

Section: Clustering-based Data Aggregation Schemesmentioning

confidence: 99%

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Khan,

Cao,

Tang

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks

Abstract: There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 1 publication

References 18 publications

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Contact Info

Product

Resources

About