A paper co-written by a U.S. Army Research Laboratory researcher gained recognition at a conference in Thailand.
Dr. Kevin Chan received the best paper award at the Association for Computing and Machinery Asian Internet Engineering Conference 2017 in November in Bangkok.
Bongjun Ko, of IBM Corporation; Lixia Zhang and Spyros Mastorakis from the University of California at Los Angeles; and Alex Afanasyev of the Florida International University co-authored the paper.
"Fuzzy Interest Forwarding," addressed the initial steps to providing fuzzing matching to interests in named data networking, known as NDN, based on several approaches, including latent semantic hashing and ontology-based matching. This matching alleviates the need for exacting name matching in NDN, Chan said.
Fuzzy matching is a technique used in computer-assisted translation as a special case of record linkage like when conducting web or database searches for research papers, online videos or consumer items such as books. NDN is a new networking approach that realizes an information-centric networking vision by focusing on data retrieval by names rather than on point-to-point data delivery based on addresses.
NDN routers use names carried in interests to decide where to forward each interest if the requested data is not available in the local content store, creating pending state for the forwarded interest. When the data is found, routers use these pending states to forward data back to one or more requesters.
The researcher proposes a fuzzy interest forwarding augmentation to NDN routers that enables discovery and retrieval of data using approximate knowledge of the data namespace. In particular, such fuzzy forwarding can be beneficial in highly dynamic and heterogeneous environments, for example, ad hoc encounters of tourists in national parks, where the exact namespace knowledge among communicating parties is either infeasible to obtain or costly to discover.
Semantic-aware fuzzy forwarding can enable NDN routers to forward interest packets with controlled uncertainty and receive potentially relevant and useful data most of the time. The returned data can also provide a feedback loop that allows applications to learn about the namespace of available data.
This research supports Army modernization priority, set by the Chief of Staff, Network, Command, Control, Communication and Intelligence by potentially providing simpler configuration of network devices and improved delivery speed for network environments with a high degree of heterogeneity. These approaches have the potential to increase reliability and robustness of the delivery of Army computer information to Soldiers and analysts in tactical coalition networks.
This work is a part of collaboration within the US/UK Distributed Analytics and Information Sciences International Technology Alliance. ARL and the UK launched the program in September 2016 to develop the fundamental underpinning research required to enable secure, dynamic, semantically-aware, distributed analytics for deriving situational understanding in coalition operations.
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The U.S. Army Research Laboratory, currently celebrating 25 years of excellence in Army science and technology, is part of the U.S. Army Research, Development and Engineering Command, which has the mission to provide innovative research, development and engineering to produce capabilities that provide decisive overmatch to the Army against the complexities of the current and future operating environments in support of the joint warfighter and the nation. RDECOM is a major subordinate command of the U.S. Army Materiel Command.