7,442 research outputs found
The Road Ahead for Networking: A Survey on ICN-IP Coexistence Solutions
In recent years, the current Internet has experienced an unexpected paradigm
shift in the usage model, which has pushed researchers towards the design of
the Information-Centric Networking (ICN) paradigm as a possible replacement of
the existing architecture. Even though both Academia and Industry have
investigated the feasibility and effectiveness of ICN, achieving the complete
replacement of the Internet Protocol (IP) is a challenging task.
Some research groups have already addressed the coexistence by designing
their own architectures, but none of those is the final solution to move
towards the future Internet considering the unaltered state of the networking.
To design such architecture, the research community needs now a comprehensive
overview of the existing solutions that have so far addressed the coexistence.
The purpose of this paper is to reach this goal by providing the first
comprehensive survey and classification of the coexistence architectures
according to their features (i.e., deployment approach, deployment scenarios,
addressed coexistence requirements and architecture or technology used) and
evaluation parameters (i.e., challenges emerging during the deployment and the
runtime behaviour of an architecture). We believe that this paper will finally
fill the gap required for moving towards the design of the final coexistence
architecture.Comment: 23 pages, 16 figures, 3 table
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
IETF standardization in the field of the Internet of Things (IoT): a survey
Smart embedded objects will become an important part of what is called the Internet of Things. However, the integration of embedded devices into the Internet introduces several challenges, since many of the existing Internet technologies and protocols were not designed for this class of devices. In the past few years, there have been many efforts to enable the extension of Internet technologies to constrained devices. Initially, this resulted in proprietary protocols and architectures. Later, the integration of constrained devices into the Internet was embraced by IETF, moving towards standardized IP-based protocols. In this paper, we will briefly review the history of integrating constrained devices into the Internet, followed by an extensive overview of IETF standardization work in the 6LoWPAN, ROLL and CoRE working groups. This is complemented with a broad overview of related research results that illustrate how this work can be extended or used to tackle other problems and with a discussion on open issues and challenges. As such the aim of this paper is twofold: apart from giving readers solid insights in IETF standardization work on the Internet of Things, it also aims to encourage readers to further explore the world of Internet-connected objects, pointing to future research opportunities
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