Resource allocation and trust computing for blockchain-enabled edge computing system

被引:102
|
作者
Zhang, Lejun [1 ,2 ]
Zou, Yanfei [1 ]
Wang, Weizheng [3 ]
Jin, Zilong [4 ]
Su, Yansen [5 ]
Chen, Huiling [6 ]
机构
[1] Yangzhou Univ, Coll Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China
[2] Quanzhou Normal Univ, Sch Math & Comp Sci, Quanzhou 362000, Peoples R China
[3] Univ Aizu, Aizu Wakamatsu, Fukushima 9658580, Japan
[4] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 21004, Peoples R China
[5] Anhui Univ, Sch Comp Sci & Technol, Key Lab Intelligent Comp & Signal Proc, Minist Educ, Hefei 230601, Peoples R China
[6] Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China
基金
黑龙江省自然科学基金;
关键词
Edge computing; Blockchain; Trust computing; Resource allocation; Content model; CLOUD;
D O I
10.1016/j.cose.2021.102249
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to meet various needs of people, different Internet of Things (IoT) devices have been developed and applied successfully in recent years. However, the consequent challenges in terms of search efficiency, reliable requirements, and resource allocation appear followed, which attract attention from both academia and industry. Facing this circumstance, it is nec-essary to establish a new scheme to realize data processing and sharing better. Therefore, a reliable and efficient system based on edge computing and blockchain is proposed in this pa -per. First, a new group-agent strategy with trust computing is designed to ensure the reliabil-ity of edge devices during interactions and improve transmission efficiency. Second, we in-troduce a stacked task sorting and ranking mechanism which improves resource allocation in each edge device. Third, this paper creates a new content model that uses Zipf distribution to predict context popularity of keywords and encrypt hot data with symmetric searchable encryption (SSE) technology. Finally, simulation results show that the proposed scheme has better computational efficiency and higher reliability compared with existing methods.& nbsp; (c) 2021 Elsevier Ltd. ABrights reserved.
引用
收藏
页数:13
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