K
Koojana Kuladinithi
Researcher at Hamburg University of Technology
Publications - 44
Citations - 747
Koojana Kuladinithi is an academic researcher from Hamburg University of Technology. The author has contributed to research in topics: Wireless sensor network & Routing protocol. The author has an hindex of 15, co-authored 41 publications receiving 709 citations. Previous affiliations of Koojana Kuladinithi include University of Bremen.
Papers
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Flow Bindings in Mobile IPv6 and Nemo Basic Support
TL;DR: Extensions to Mobile IPv6 and Nemo Basic Support that allow nodes to bind one or more flows to a care-of address and allow multihomed nodes to take full advantage of the different properties associated with each of their interfaces are introduced.
Flow Bindings in Mobile IPv6 and Network Mobility (NEMO) Basic Support
TL;DR: This document introduces extensions to Mobile IPv6 that allow nodes to bind one or more flows to a care-of address and allows multihomed nodes to instruct their peers to direct downlink flows to specific addresses.
Journal ArticleDOI
Simulating Opportunistic Networks: Survey and Future Directions
Jens Dede,Anna Förster,Enrique Hernández-Orallo,Jorge Herrera-Tapia,Koojana Kuladinithi,Vishnupriya Kuppusamy,Pietro Manzoni,Anas Bin Muslim,Asanga Udugama,Zeynep Vatandas +9 more
TL;DR: This paper performs a gap analysis of state-of-the-art OppNet simulations and sketches out possible further development and lines of research, and experimentally shows the scalability of different simulators.
Motivations and Scenarios for Using Multiple Interfaces and Global Addresses
TL;DR: The purpose of this document is to explain the motivations for fixed and mobile nodes using multiple interfaces and the scenarios where this may end up using multiple global addresses on their interfaces.
Proceedings ArticleDOI
An On-demand Multi-Path Interest Forwarding strategy for content retrievals in CCN
TL;DR: OMP-IF strategy is designed and evaluated using a simulator with a large scale network scenario and a realistic traffic generation model and the results show improved performance in CCN networks considering download time, load balancing, content hit ratios, and others.