Energy Efficient Deployment and Task Offloading for UAV-Assisted Mobile Edge Computing

doi:10.1007/978-3-030-95388-1_28

2022-02-23

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DOI：10.1007/978-3-030-95388-1_28

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摘要

En 中文

With the popularization of mobile wireless networks and Internet of Things (IoT) technologies, energy-hungry and delay-intensive applications continue to surge. Due to the limited computing power and battery capacity, mobile terminals rarely satisfy the increasing demands of application services. Mobile Edge Computing (MEC) deploys communication and computing resources near the network edge closing to the user side, which effectively reduces devices' energy consumption and enhances system performance. However, the application of MEC needs infrastructures that can deploy edge services, and is limited by the geographical environment. UAV-assisted MEC has better flexibility and communication Line-of-Sight (LoS), which expands service scope while improving the versatility of MEC. Meanwhile, the dynamic task arrival rate, channel condition, and environmental factors pose challenges for task offloading and resources allocation strategy. In this paper, we jointly optimize UAV deployment, frequency scaling, and task scheduling to minimize energy consumption for devices while ensuring system stability in the long term. Due to the dynamic and randomness of task arrival rate and wireless channel, the original problem is defined as a stochastic optimization problem. The Drone Placement and Online Task oFFloading (DPOTFF) algorithm is designed to decouple the original problem into several sub-problems and solve them within a limited time complexity. It is also proved theoretically that the DPOTFF can obtain close-to-optimal energy consumption while ensuring system stability. The effectiveness and reliability of the algorithm are also verified by simulation and comparative experiments.

Keyword:

Mobile Edge Computing

Energy efficiency

Resources allocation

Task offloading

UAV deployment