Hassan Sinky

TL;DR: A comprehensive taxonomy of machine learning techniques for in-network caching in edge networks is formulated and a comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy.

TL;DR: Findings are presented on transport layer handoff issues in currently deployed networks and a handoff-aware cross-layer-assisted MPTCP (CLA-MPTCP) congestion control algorithm is designed and evaluated.

TL;DR: This work proposes a resource-efficient clustering framework for AIS that hierarchically performs geo-textual clustering without significantly lowering the clustering quality, and achieves substantial time and memory efficiency while reducing the overall resource requirements for constrained end-user and edge devices compared to the standard hybrid geo- Textual Clustering framework.

TL;DR: Content-centric, in-network content caching and placement approaches that leverage cooperation among edge cloud devices, content popularity, and GPS trajectory information to improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities are proposed.

TL;DR: In this paper, a content-centric and delivery framework is proposed leveraging NYC's population densities and content delivery cloudlets for interest-based in-network caching, which is validated using LinkNYC as a use-case.