Document Type

Dissertation

Date of Award

5-31-2021

Degree Name

Doctor of Philosophy in Computer Engineering - (Ph.D.)

Department

Electrical and Computer Engineering

First Advisor

Nirwan Ansari

Second Advisor

Ashutosh Dutta

Third Advisor

Abdallah Khreishah

Fourth Advisor

Qing Liu

Fifth Advisor

MengChu Zhou

Abstract

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of computing tasks instead of performing all tasks in the remote cloud, thus enabling immediate service response. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones' battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage.

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