With the recent development of network-related technologies, various types of networks have been studied and become an essential part of modern society, such as the wireless rechargeable sensor network (WRSN), (electric) bike sharing networks, and autonomous vehicles networks. WRSN can utilize ubiquitous sensors to collect various data and monitor interesting targets; users of bike sharing networks can enjoy the flexibility of accessing and parking bikes anywhere which addresses the last few miles transportation problem; autonomous electric vehicle networks are promising to address the air pollution and traffic congestions in cities thoroughly. My dissertation focuses on the optimization of these networks via delicately designed heuristic algorithms and approximation algorithms with theoretical performance bound. The feasibility of proposed algorithms and system is validated via the extensive simulations on experimental data and public datasets. We start with an overview of the studied networks, discussing the necessities and challenges in the face of these networks. Next, we address the critical issues of these networks in order to decrease the network cost, increase efficiency, and extend network lifetime. First, we take advantage of the redundancy of deployed rechargeable sensors and propose a new charging framework for a mobile charger (MC) to extend the lifetime of networks significantly while ensuring the target k-coverage. Second, we design a novel system based on multi-source harvestable energy to address the energy provision problem of WRSN. The optimal combinations of network components are derived via the proposed approximation algorithms. Third, we study vehicular networks composed of solar-powered autonomous electric vehicles, which can operate three times longer than the ordinary electric vehicles with the proposed charging station placement and vehicle routing algorithms. Next, we study dynamic positioning of the parking location of electric sharing bike networks, combine the offline expectation with online user demands, and build a two-tier holistic system to maximize user satisfaction and minimize the cost of sharing bike companies simultaneously. Finally, based on the previous work, we further explore the design of incentivizing mechanisms to motivate bike users to reposition bikes in desiring locations considering the heterogeneous difficulties of tasks via a new reinforcement learning framework.
|Commitee:||Ye, Fan, Liu, Ji, Liu, Zhenhua|
|School:||State University of New York at Stony Brook|
|School Location:||United States -- New York|
|Source:||DAI-A 82/4(E), Dissertation Abstracts International|
|Subjects:||Computer science, Artificial intelligence, Electrical engineering, Design, Remote sensing, Automotive engineering, Energy, Civil engineering, Economics|
|Keywords:||Algorithm designs, Combinatorial optimization, Sharing economy, Vehicular networks, Wireless rechargeable sensor network, Network-related technologies, Network optimization, Electric bike sharing networks, Online user demands|
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