期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 24, 页码 17382-17394出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3080709
关键词
Contracts; Device-to-device communication; Servers; Edge computing; Computer architecture; Internet of Things; Cloud computing; Augmented reality (AR); computational caching; contract theory
资金
- National Research Foundation of Korea (NRF) - Korea Government (MSIT) [2020R1A4A1018607]
- National Research Foundation of Korea [2020R1A4A1018607] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The article discusses the growing demand for augmented reality applications and proposes an incentive mechanism for utilizing user equipment resources for local caching to address the challenge of insufficient MEC server resources.
Recently, we have witnessed an increasing demand in augmented reality (AR)-based fifth-generation (5G) and beyond applications, such as smart gaming, smart navigation, smart military wearable, and smart industries. These AR-based applications require on-demand computational and caching resources with low latency that can be provided via multiaccess edge computing (MEC) server. However, due to the massive growth of AR-enabled devices, the MEC server resources might be insufficient. To overcome this challenge, we can utilize the computational and caching resources of user equipment (UE) to serve the other UEs in its close vicinity. Successfully enabling such interaction among devices requires an attractive incentive mechanism. Therefore, we propose a contract theory-based incentive mechanism for enabling on-device caching for AR-based applications. In our approach, the MEC offers a reward to the UE for providing its resources (i.e., storage capacity, power, etc.). Furthermore, under the information asymmetry problem, we derive an optimal mechanism via the contract theory for enabling on-device caching subject to the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to validate the effectiveness of our proposed scheme.
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