@Article{iasc.2021.019201,
AUTHOR = {Ye Yang, Xiaofang Li, Dongjie Zhu, Hao Hu, Haiwen Du, Yundong Sun, Weiguo Tian, Yansong Wang, Ning Cao, Gregory M.P. O’Hare},
TITLE = {A Resource-constrained Edge IoT Device Data-deduplication Method with Dynamic Asymmetric Maximum},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {30},
YEAR = {2021},
NUMBER = {2},
PAGES = {481--494},
URL = {http://www.techscience.com/iasc/v30n2/44040},
ISSN = {2326-005X},
ABSTRACT = {Smart vehicles use sophisticated sensors to capture real-time data. Due to the weak communication capabilities of wireless sensors, these data need to upload to the cloud for processing. Sensor clouds can resolve these drawbacks. However, there is a large amount of redundant data in the sensor cloud, occupying a large amount of storage space and network bandwidth. Deduplication can yield cost savings by storing one data copy. Chunking is essential because it can determine the performance of deduplication. Content-Defined Chunking (CDC) can effectively solve the problem of chunk boundaries shifted, but it occupies a lot of computing resources and has become a bottleneck in deduplication technology. This paper proposes a Dynamic Asymmetric Maximum algorithm (DAM), which uses the maximum value as the chunk boundaries and reducing the impact of the low-entropy string. It also uses the perfect hash algorithm to optimize the chunk search. Experiments show that our solution can effectively detect low-entropy strings in redundant data, save storage resources, and improve sensor clouds system throughput.},
DOI = {10.32604/iasc.2021.019201}
}