Resource allocation and trust computing for blockchain-enabled edge computing system. Issue 105 (June 2021)

• Record Type: Journal Article
• Authors: Zhang, Lejun; Zou, Yanfei; Wang, Weizheng; Jin, Zilong; Su, Yansen; Chen, Huiling
• Is Part Of: Computers & security. Issue 105(2021)
• Publication Date: 2021-06
• Subjects: Edge computing -- Blockchain -- Trust computing -- Resource allocation -- Content model; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2021.102249
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: We design a group-agent strategy with trust computing. We propose a stacked task sorting and ranking mechanism. We adopt a secure and efficient content model. Simulation results show that our scheme has better computational efficiency and higher reliability. Abstract: 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 necessary 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 paper. First, a new group-agent strategy with trust computing is designed to ensure the reliability of edge devices during interactions and improve transmission efficiency. Second, we introduce 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.