@Article{iasc.2023.036079,
AUTHOR = {Ghawar Said, Ata Ullah, Anwar Ghani, Muhammad Azeem, Khalid Yahya, Muhammad Bilal, Sayed Chhattan Shah},
TITLE = {Hash Table Assisted Efficient File Level De-Duplication Scheme in SD-IoV Assisted Sensing Devices},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {38},
YEAR = {2023},
NUMBER = {1},
PAGES = {83--99},
URL = {http://www.techscience.com/iasc/v38n1/55287},
ISSN = {2326-005X},
ABSTRACT = {The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead. Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows. This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table with key-value pair for each block of the file. Further, an algorithm for hash table-based duplicate block identification and storage (HDBIS) is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index. Hash tables normally have a consistent time complexity for lookup, generating, and deleting stored data regardless of the input size. The experiential results show that the proposed EFDS scheme performs better compared to its counterparts.},
DOI = {10.32604/iasc.2023.036079}
}