Electronic Theses and Dissertations

Date

2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Computer Science

Committee Chair

Lan Wang

Committee Member

Christos Papadopoulos

Committee Member

Myounggyu Won

Committee Member

Lixia Zhang

Abstract

Today’s Internet falls short in supporting robust data delivery and strong data security in a wireless mobile environment, due to its host-based communication model and container-based security model. Named Data Networking (NDN) is a data-centric network architecture that provides a stateful forwarding plane and built-in security support. This work proposes forwarding strategies for NDN-based mobile ad-hoc networks (MANETs) and infrastructure-based wireless networks, and mechanisms for securing NDN-based vehicular networks. First, we propose Content Connectivity and Location-Aware Forwarding (CCLF), a forwarding strategy for MANETs. CCLF broadcasts NDN packets and lets each node make independent decisions on whether to forward packets based on per-prefix performance measurements and any available geo-location information. In addition, it employs a density-aware suppression mechanism to reduce unnecessary packet transmissions. Moreover, we developed a link adaptation layer for ad-hoc links to bridge the gap between CCLF and the capabilities of the underlying link. Second, we present m-ASF, a forwarding strategy for infrastructure-based wireless networks. It is an extension of ASF (Adaptive SRTT based Forwarding), which is designed for largely stable networks. Our work improves the ASF strategy by probing multiple paths when the primary path is experiencing failures, employing fine-grained ranking of faces, and avoiding path oscillations caused by short-lived network dynamics. Our experiment results show that these changes are highly effective when the data producers are mobile and/or links are dynamic. Lastly, we demonstrate how NDN can protect data security in vehicular networks. We examine two potential threats: false information dissemination and vehicle tracking. To detect false data, we propose two trust models and the associated naming schemes for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to protect vehicle identity. We implemented our proposed schemes and conducted experiments in simulated and real environments.

Comments

Data is provided by the student.”

Library Comment

Dissertation or thesis originally submitted to ProQuest.

Notes

Open Access

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