Privacy Concerned D2D-Assisted Delay-Tolerant Content Distribution System
Type
ThesisAuthors
Ma, Guoqing
Advisors
Shihada, Basem
Committee members
Alouini, Mohamed-Slim
Amin, Osama

Program
Computer ScienceDate
2019-04-28Embargo End Date
2020-04-28Permanent link to this record
http://hdl.handle.net/10754/632512
Metadata
Show full item recordAccess Restrictions
At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2020-04-28.Abstract
It is foreseeable that device-to-device (D2D) communication will become a standard feature in the future, for the reason that it offloads the data traffic from network infrastructures to user devices. Recent researches prove that delivering delay-tolerant contents through content delivery network (CDN) by D2D helps network operators increase spectral and energy efficiency. However, protecting the private information of mobile users in D2D assistant CDN is the primary concern, which directly affects the willingness of mobile users to share their resources with others. In this thesis, we proposed a privacy concerned top layer system for selecting the sub-optimal set of mobile nodes as initial mobile content provider (MCP) for content delivery in any general D2D communications, which implies that our proposed system does not rely on private user information such as location, affinity, and personal preferences. We model the initial content carrier set problem as an incentive maximization problem to optimize the rewards for network operators and content providers. Then, we utilized the Markov random field (MRF) theory to build a probabilistic graphical model to make an inference on the observation of delivered contents. Furthermore, we proposed a greedy algorithm to solve the non-linear binary integer programming (NLBIP) problem for selecting the optimal initial content carrier set. The evaluations of the proposed system are based on both a simulated dataset and a real-world collected dataset corresponding to the off-line and on-line scenarios.Citation
Ma, G. (2019). Privacy Concerned D2D-Assisted Delay-Tolerant Content Distribution System. KAUST Research Repository. https://doi.org/10.25781/KAUST-08FS9ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-08FS9