115,140 research outputs found
Business Case and Technology Analysis for 5G Low Latency Applications
A large number of new consumer and industrial applications are likely to
change the classic operator's business models and provide a wide range of new
markets to enter. This article analyses the most relevant 5G use cases that
require ultra-low latency, from both technical and business perspectives. Low
latency services pose challenging requirements to the network, and to fulfill
them operators need to invest in costly changes in their network. In this
sense, it is not clear whether such investments are going to be amortized with
these new business models. In light of this, specific applications and
requirements are described and the potential market benefits for operators are
analysed. Conclusions show that operators have clear opportunities to add value
and position themselves strongly with the increasing number of services to be
provided by 5G.Comment: 18 pages, 5 figure
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Legacy Handbook reviewing emda's experience of innovation activity. Identifies key achievements and draws out lessons learned that may be relevant to successor bodies active in this area
Enhanced broadband access as a solution to the social and economic problems of the rural digital divide
Peer reviewedPublisher PD
A Federated Filtering Framework for Internet of Medical Things
Based on the dominant paradigm, all the wearable IoT devices used in the
healthcare sector also known as the internet of medical things (IoMT) are
resource constrained in power and computational capabilities. The IoMT devices
are continuously pushing their readings to the remote cloud servers for
real-time data analytics, that causes faster drainage of the device battery.
Moreover, other demerits of continuous centralizing of data include exposed
privacy and high latency. This paper presents a novel Federated Filtering
Framework for IoMT devices which is based on the prediction of data at the
central fog server using shared models provided by the local IoMT devices. The
fog server performs model averaging to predict the aggregated data matrix and
also computes filter parameters for local IoMT devices. Two significant
theoretical contributions of this paper are the global tolerable perturbation
error () and the local filtering parameter (); where the
former controls the decision-making accuracy due to eigenvalue perturbation and
the later balances the tradeoff between the communication overhead and
perturbation error of the aggregated data matrix (predicted matrix) at the fog
server. Experimental evaluation based on real healthcare data demonstrates that
the proposed scheme saves upto 95\% of the communication cost while maintaining
reasonable data privacy and low latency.Comment: 6 pages, 6 Figures, accepted for oral presentation in IEEE ICC 2019,
Internet of Things, Federated Learning and Perturbation theor
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