Title: Accurate close contact identification: a solution based on P-RAN, fog computing and blockchain

Authors: Meiling Dai; Yutong Wang; Zheng Zhang; Xiaohou Shi; Shaojie Yang

Addresses: Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

Abstract: Epidemic like COVID-19 has spread extensively, disrupting people's daily lives worldwide. Accurate close contact identification (ACCI) emerges as a crucial measure to mitigate the spread of these epidemics. The low accuracy, insufficient user privacy protection ability, and limited effectiveness of ACCI become important issues to be solved. This paper proposes a solution that emphasises collecting reliable ACCI data and proving the trusted behaviour supervision of all the participating entities based on proximity network awareness. A three-layer architecture consisting of a P-RAN layer, hierarchical blockchain layer, and application layer is provided. Based on the architecture, a well-structured management platform for the blockchain network deployment and a comprehensive process for ACCI are also designed. Furthermore, this paper has conducted a thorough analysis of the potential for implementation and outlined the future challenges. Finally, we design simulations to validate the efficiency of the proposed solution in specific scenarios.

Keywords: close contact identification; fog computing; blockchain; proximity radio access network.

DOI: 10.1504/IJSNET.2024.137336

International Journal of Sensor Networks, 2024 Vol.44 No.3, pp.144 - 156

Received: 25 Nov 2023
Accepted: 16 Dec 2023
Published online: 12 Mar 2024 *

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