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Local Cache-Enabled Mobile Augmented Reality in Mobile Edge Computing
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AI
DOI：10.1109/MCOM.001.2300479.png)
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Abstract
En 中文
Recently, multi-access edge computing (MEC)-empowered mobile augmented reality (MAR) has emerged as a prominent technology domain. It highlights the disruptive potential of MEC in the context of 5G, as it empowers mobile devices (MDs) with limited local processing capabilities by offering enhanced computing power, and also significantly reduces latency. Thus, this innovation has attracted considerable attention from both industry and academia. However, the design challenge of offloading management for MAR in MEC is highly complex due to the inherent heterogeneity in computing and networking capabilities between MDs and MEC servers. Furthermore, this challenge is compounded by the integration of local cache in MDs, which aims to further reduce network latency and transmission energy consumption by bypassing the offloading process for frequently repeated detection requests. In this article, we present a comprehensive overview of the overall process of local cache-enabled MAR in MEC, and provide a thorough analysis of latency and energy considerations, taking into account the influence of various system parameters. Additionally, we propose an innovative approach to MD cache control, seeking to strike a delicate balance between the operating expenses for service providers (i.e., energy consumption at the MEC server) and the cost of MDs, regarding both latency and energy consumption. Finally, we address open challenges in this field by considering cutting-edge AI technologies, such as deep reinforcement learning and super-resolution techniques, as well as standardization aspects in this field. These all represent promising avenues for future research and development.
Keywords:
Servers
Feature extraction
Energy consumption
Object detection
Object recognition
Image edge detection
Edge computing
Augmented reality
Multi-access edge computing
Journal
IF:
8.2
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6.9K
Citations:
2.2W
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