Page 16 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

MSICA: Multi-scale signal decomposition based on independent component

analysis with application to denoising and reliable multi-channel transmission
Pages 25-35

Abolfazl Hajisami, Dario Pompili
Multi-scale decomposition is a signal description method in which the signal is decomposed into
multiple scales, which has been shown to be a valuable method in information preservation. Much
focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this
article, a new powerful method to perform multi-scale decomposition exploiting Independent
Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple
statistically independent scales. It is proven that extracting the independent components of the even and
odd samples of a digital signal results in the decomposition of the same into approximation and detail.
It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure.
Performance results of MSICA in signal denoising are presented; also, the statistical independency of
the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel
noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

View Article

SDN-based Sociocast group communications in the Internet of Things

Pages 37-54
Luigi Atzori, Claudia Campolo, Antonio Iera, Giuseppe Massimiliano Milotta, Giacomo Morabito,
Salvatore Quattropani

The new applications populating the Future Internet will increasingly rely on the exchange of data
between groups of devices, dynamically established according to their profile and habits (e.g., a
common interest in the same software updates and services). This will definitely challenge traditional
group communication solutions that lack the necessary flexibility in group management and do not
support effective control policies on involved endpoints (i.e., authorized senders and intended receivers).
To address the cited issues, the idea of introducing new disruptive network-layer solutions has emerged
from recent literature. Among them, Sociocast has been theorized as an enabler of flexible interactions
between groups of devices tied by social relationships. In this paper we start from the concept of
Sociocast and propose a solution based on Software Defined Networking (SDN) for its implementation
at the network layer in the Internet of Things. The performance of Sociocast is studied and compared
to methods running at the application layer that provide similar features. Experimental results, achieved
through an emulation-based playground, confirm that the Sociocast approach allows for significant
reduction of signaling and data packets circulating in the network with respect to traditional approaches.
View Article

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