Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Neelamegam, G.; Marikkannu, P.

doi:10.32604/iasc.2023.031283

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Their BEDD-O and BEDD-A algorithms significantly outperformed and have shown good computational advantages results. Neelamegam et al [23] stated that they handled the data duplication of medical healthcare by their proposed window size chunking algorithm. Firstly, the data is stored in the cloud-fog layer, and the duplicate data are eliminated.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Architecture for Optimized IoT Data Transmission from Edge to Cloud

Zeba,

Mashira,

Mim

et al. 2024

Preprint

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Edge Computing and the Internet of Things (IoT) have recently experienced significant growth and transformed how data is processed and analyzed. Edge computing improves efficiency and reduces latency by processing data locally. However, transmitting data efficiently while conserving energy is still a major issue today, especially considering the volume and redundancy of data. The computational capacity and memory of edge gateways in the network's edge layers are limited, making it challenging to process data effectively. As a result, data transmission often becomes inefficient. To address this issue, our research introduces an energy-efficient architecture for edge gateways in the edge layer. This architecture leverages data deduplication and compression techniques for IoT data transmission from edge to cloud. The research's unique deduplication algorithm eliminates duplicate data, while the Lempel Ziv 4 compression algorithm compresses large data sets effectively. This method not only reduces energy consumption but also minimizes memory usage, facilitating quicker and more efficient data transmission. Consequently, this approach significantly alleviates energy consumption challenges and limited data processing capabilities in the edge layer.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Architecture for Optimized IoT Data Transmission from Edge to Cloud

Zeba,

Mashira,

Mim

et al. 2024

Preprint

View full text Add to dashboard Cite

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Deduplication-Aware Healthcare Data Distribution in IoMT

Altowaijri

2024

Mathematics

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As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization.

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Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Cited by 2 publications

References 15 publications

Energy-Efficient Architecture for Optimized IoT Data Transmission from Edge to Cloud

Energy-Efficient Architecture for Optimized IoT Data Transmission from Edge to Cloud

Deduplication-Aware Healthcare Data Distribution in IoMT

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