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Energy-Efficient Scheduling and Optimization for Connected and Autonomous Vehicles
- Author(s)
- 정소이
- Alternative Author(s)
- Soyi Jung
- Advisor
- 김재현
- Department
- 일반대학원 전자공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2021-02
- Language
- eng
- Keyword
- Connected and Autonomous Vehicles; Convex optimization; Energy-Efficient; Lyapunov optimization; Unmanned Aerial Vehicles
- Alternative Abstract
- In this dissertation, energy-efficient scheduling and optimization algorithms are proposed for unmanned aerial vehicle (UAV) mobile networks. The design and implementation of scheduling and optimization are challenging especially for the energy-limited UAV networks. Therefore, this dissertation proposes novel algorithms for solving the following three challenging problems in energy-efficient scheduling. First of all, a multi-UAV charging scheduling algorithm is proposed in terms of joint optimization between scheduling and energy delivery. This algorithm computes message-passing based scheduling by deactivating selected surveillance Closed-Circuit Television (CCTV) cameras located in overlapping areas. After this computation, optimal matching between UAVs and charging towers is performed. Second, an algorithm for joint UAV scheduling and deep learning-based active energy sharing among charging facilities is proposed. The cooperative energy sharing among towers is designed via multi-agent deep reinforcement learning, and thus intelligent sharing can be realized. Lastly, an adaptive learning computation outsourcing algorithm is designed in distributed big-data outsourcing systems. In order to make the outsourcing decision when a single UAV cannot conduct deep learning computation alone, the edges which can compute the learning computation instead of the UAV can be scheduled under the concept of max-weight. After the scheduling, Lyapunov-based transmit power allocation is considered for stabilized time-average UAV energy consumption minimization in order to deliver the learning data to scheduled edges. Based on the proposed three energy-efficient scheduling and learning algorithms, UAV mobile networks can extend their lifetime for scalable, flexible, and robust operations.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 4. Theses(Ph.D)
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