A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications

Baligodugula, Vishnu Vardhan; Amsaad, Fathi; Tadepalli, Varshitha Vunnam; Radhika, Vojjala; Sanjana, Yetukuri; Shiva, Sai; Meduri, Supriya; Maabreh, Majdi; Alsaadi, Naser; Darwish, Omar; Razaque, Abdul; Tashtoush, Yahya

doi:10.1007/978-3-031-33743-7_46

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

POFFO: A Perceptual Online File Fingerprint Offloading Strategy for Effective Data Deduplication at Cloud-Edge Systems

Lu,

Deng,

Huang

2024

2024 International Conference on Networking, Architecture and Storage (NAS)

View full text Add to dashboard Cite

POFFO: A Perceptual Online File Fingerprint Offloading Strategy for Effective Data Deduplication at Cloud-Edge Systems

Lu,

Deng,

Huang

2024

2024 International Conference on Networking, Architecture and Storage (NAS)

View full text Add to dashboard Cite

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

A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications

Cited by 2 publications

References 27 publications

POFFO: A Perceptual Online File Fingerprint Offloading Strategy for Effective Data Deduplication at Cloud-Edge Systems

POFFO: A Perceptual Online File Fingerprint Offloading Strategy for Effective Data Deduplication at Cloud-Edge Systems

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

Contact Info

Product

Resources

About