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In 5G and beyond, the adequate interaction between densely deployed Internet-of-Things (IoT) devices and cellular users will generate a massive cyber-physical information stream in a real-time manner. How to capture insights underneath these data in a smart city context is gaining great attention nowadays. In this article, we introduce a highly function-differentiated metropolitan scenario, which is covered by multiple unmanned aerial vehicles (UAVs) serving as cache-enabled edge computing nodes. With the help of wireless backhaul technology, coverage capability of UAV can be dynamically configured through smart 3-D placement, where trajectories of two types of UAVs are optimized. A social augmented reality (AR)-based use case is proposed and discussed in the proposed scenario, from which we derive the fundamental mechanisms and strategies to maintain a green and sustainable edge/fog computing framework. We sequentially establish two nonconvex programming problems and optimize delay and energy performance during AR data acquisition and AR content downloading, respectively. Two convex approximation skills are applied to transform the original problems into tractable form. The experimental results show that our proposed edge computing framework can help provide energy-efficient AR service to cellular users, catering to pretty tight delay constraints.
Tan et al. (Tue,) studied this question.
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