188 research outputs found
APMEC: An Automated Provisioning Framework for Multi-access Edge Computing
Novel use cases and verticals such as connected cars and human-robot
cooperation in the areas of 5G and Tactile Internet can significantly benefit
from the flexibility and reduced latency provided by Network Function
Virtualization (NFV) and Multi-Access Edge Computing (MEC). Existing frameworks
managing and orchestrating MEC and NFV are either tightly coupled or completely
separated. The former design is inflexible and increases the complexity of one
framework. Whereas, the latter leads to inefficient use of computation
resources because information are not shared. We introduce APMEC, a dedicated
framework for MEC while enabling the collaboration with the management and
orchestration (MANO) frameworks for NFV. The new design allows to reuse
allocated network services, thus maximizing resource utilization. Measurement
results have shown that APMEC can allocate up to 60% more number of network
services. Being developed on top of OpenStack, APMEC is an open source project,
available for collaboration and facilitating further research activities
S-PRAC: Fast Partial Packet Recovery with Network Coding in Very Noisy Wireless Channels
Well-known error detection and correction solutions in wireless
communications are slow or incur high transmission overhead. Recently, notable
solutions like PRAC and DAPRAC, implementing partial packet recovery with
network coding, could address these problems. However, they perform slowly when
there are many errors. We propose S-PRAC, a fast scheme for partial packet
recovery, particularly designed for very noisy wireless channels. S-PRAC
improves on DAPRAC. It divides each packet into segments consisting of a fixed
number of small RLNC encoded symbols and then attaches a CRC code to each
segment and one to each coded packet. Extensive simulations show that S-PRAC
can detect and correct errors quickly. It also outperforms DAPRAC significantly
when the number of errors is high
Neuromorphic Twins for Networked Control and Decision-Making
We consider the problem of remotely tracking the state of and unstable linear
time-invariant plant by means of data transmitted through a noisy communication
channel from an algorithmic point of view. Assuming the dynamics of the plant
are known, does there exist an algorithm that accepts a description of the
channel's characteristics as input, and returns 'Yes' if the transmission
capabilities permit the remote tracking of the plant's state, 'No' otherwise?
Does there exist an algorithm that, in case of a positive answer, computes a
suitable encoder/decoder-pair for the channel? Questions of this kind are
becoming increasingly important with regards to future communication
technologies that aim to solve control engineering tasks in a distributed
manner. In particular, they play an essential role in digital twinning, an
emerging information processing approach originally considered in the context
of Industry 4.0. Yet, the abovementioned questions have been answered in the
negative with respect to algorithms that can be implemented on idealized
digital hardware, i.e., Turing machines. In this article, we investigate the
remote state estimation problem in view of the Blum-Shub-Smale computability
framework. In the broadest sense, the latter can be interpreted as a model for
idealized analog computation. Especially in the context of neuromorphic
computing, analog hardware has experienced a revival in the past view years.
Hence, the contribution of this work may serve as a motivation for a theory of
neuromorphic twins as a counterpart to digital twins for analog hardware
On the Need of Analog Signals and Systems for Digital-Twin Representations
We consider the task of converting different digital descriptions of analog
bandlimited signals and systems into each other, with a rigorous application of
mathematical computability theory. Albeit very fundamental, the problem appears
in the scope of digital twinning, an emerging concept in the field of digital
processing of analog information that is regularly mentioned as one of the key
enablers for next-generation cyber-physical systems and their areas of
application. In this context, we prove that essential quantities such as the
peak-to-average power ratio and the bounded-input/bounded-output norm, which
determine the behavior of the real-world analog system, cannot generally be
determined from the system's digital twin, depending on which of the
above-mentioned descriptions is chosen. As a main result, we characterize the
algorithmic strength of Shannon's sampling type representation as digital twin
implementation and also introduce a new digital twin implementation of analog
signals and systems. We show there exist two digital descriptions, both of
which uniquely characterize a certain analog system, such that one description
can be algorithmically converted into the other, but not vice versa
- …