Author ORCID Identifier
0000-0001-5602-7841
Document Type
Dissertation
Date of Award
5-31-2024
Degree Name
Doctor of Philosophy in Computer Engineering - (Ph.D.)
Department
Electrical and Computer Engineering
First Advisor
Nirwan Ansari
Second Advisor
Abdallah Khreishah
Third Advisor
Roberto Rojas-Cessa
Fourth Advisor
Qing Gary Liu
Fifth Advisor
David A. Haessig
Abstract
Drone-mounted base stations (DBSs) have emerged as a promising solution to enhance the flexibility and coverage of wireless networks, potentially revolutionizing communication systems. This dissertation explores the integration of DBSs into 5G and beyond networks, focusing on methodologies to optimize their deployment and performance. A laser charging-enabled DBS framework is proposed to extend flight time and enhance network coverage. By leveraging laser charging technology, the DBS can receive continuous energy transmission from a ground-based charging station while providing communication services to users. The framework is formulated as an optimization problem to jointly maximize flight time and communication data rate, while addressing challenges in power and bandwidth allocation as well as DBS placement. In investigating the deployment of DBS to assist mobile edge computing and leveraging laser charging technology to extend service time, the dissertation formulates another optimization problem to minimize task completion time while maximizing DBS service time by optimizing DBS placement, user association, bandwidth, and computation resource allocation. Innovative algorithms are developed to tackle this optimization problem, showcasing improvements in UE task offloading completion time and DBS service time. Furthermore, a secure mobile edge computing framework using free space optics for backhauling from DBS to a macro base station is proposed. The joint bandwidth and computation resource assignment, DBS transmission power control, UE power control, UE association and DBS placement problem is formulated to jointly maximize average secrecy rate and minimize task completion time while considering limited bandwidth and computing resources as well as the existence of eavesdroppers. Successive convex approximations are employed to tackle this multi-objective problem, showcasing enhancements in average secrecy rate and task completion time as compared to existing approaches. Combining insights from these studies, this dissertation contributes to advancing the state of the art in drone-assisted edge computing, addressing key challenges in energy efficiency, resource allocation, and security. The proposed methodologies and frameworks offer valuable contributions to the design and implementation of future wireless communication systems.
Recommended Citation
Liu, Weiqi, "Integrating laser charging and drones for secure edge computing" (2024). Dissertations. 1757.
https://digitalcommons.njit.edu/dissertations/1757
