Successfully defended my PhD dissertation
I would like to sincerely and wholeheartedly thank my faculty advisor, Dr. Beichuan Zhang, for supporting me throughout completing my Doctoral degree. He has provided substantial technical guidance and valuable advice for my future career.
Next, my gratitude goes to the doctoral committee members, Dr. John Hartman, Dr. Chris Gniady, and Dr. Alon Efrat. They have guided and provided critical technical comments to improve my research work, hence this dissertation.
I would also like to thank all the anonymous reviewers (especially the third ones!) for their feedback on published and rejected works related to this dissertation. Special thanks go to my co-authors (Teng Liang, Ju Pan, Davide Pesavento, Alex Afnasyev, Junxiao Shi) and the entire NDN team working relentlessly to improve the internet architecture.
Finally, I would like to thank the entire Computer Science department at the University of Arizona, its faculties for the wonderful learning experience, the students for allowing me to gain valuable teaching and collaboration experience, and the staff for their unwavering support and help throughout the entire Ph.D. program. I feel ever indebted to them.
Participated in the 13th NDN Hackathon as an Organizer
Find more: https://13th-ndn-hackathon.named-data.net/
Presented paper at the 2021 Military Communication Conference (MILCOM)
In this work, we presented a novel technique called BLEnD (Bundling Interest packets with Link-layer Encoding and Decoding), where we show how a link-layer Interest bundling technique can retrieve multiple data packets with single Interest without breaking the upper layers in the NDN architecture, requiring minimum amount of changes, and improve throughput by over 30% in simulated environments.
Presented at the NDN Community Meeting 2021, hosted by NIST
We presented a novel technique called BLEnD (Bundling Interest packets with Link-layer Encoding and Decoding), where we show how a link-layer Interest bundling can retrieve multiple data packets with single Interest without breaking any other layers in NDN architecture, requiring minimum amount of changes, and improve throughput by over 30% in simulated environments.
This work is also accepted and I will present it at MILCOM 2021.
NDN Community Meeting 2021, hosted by NIST
The Named Data Networking Community Meeting 2021 will be hosted by the National Institute of Standards and Technology (NIST) as a virtual meeting on October 28 and 29, 2021. The organizing committee cordially invites you to participate in this event and calls for your contributions.
Presented paper at the 2021 IEEE Local Computer Networks (LCN)
NDN promises a better fit than IP in MANETs. However, we show that in a multi-hop wireless network, NDN's out-of-order data retrieval and redundant data from the built-in multicast leads to increased collision and contention, and in turn, decreases throughput in adaptive-rate applications. We then apply a forced Congestion Window Limit (CWL) to cap the congestion window and implement a novel Dynamic Interest Lifetime (DIL) technique to reduce data redundancy on application retransmission. Together, they significantly improve NDN throughput in wireless ad hoc networks.
Presented paper at the 2021 International Conference on Computer Communications and Networks (ICCCN)
In this work, we establish a baseline architectural comparison between NDN and IP at the network level in mobile ad hoc networks. Our analysis show that a properly designed NDN forwarding strategy can significantly lower network latency by only using named Interest and Data packet, unlike in IP-based routing, say AODV. This helps NDN to achieve better, success-rate. Such performance comes from its caching, built-in multicast, and request aggregation without requiring an IP-like separate routing control plane.