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Author Topic: seminari Stefano Chessa da UNIPI 1 luglio presso di noi  (Read 506 times)
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Giampaolo Bella
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« on: 29-06-2016, 13:00:00 »

Il Prof Stefano Chessa dell'Università di Pisa ci visita *giorno 1 luglio*.

Terrà un duplice seminario al quale siete invitati a partecipare *alle ore 12 in aula 2*, dettagli in calce.
--
    Giamp


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Titolo: Signals From the Depths: Properties of Percolation Strategies with
the Argo Dataset

Autori: Flaviano Di Rienzo, Michele Girolami, Stefano Chessa, Francesco
paparella, Antonio Caruso

Abstract: Underwater communications though acoustic
modems rise several networking challenges for the Underwater
Acoustic Sensor Networks (UASN). In particular, opportunistic
routing is a novel but promising technique that can remarkably
increase the reliability of the UASN, but its use in this context
requires studies on the nature of mobility in UASN. Our goal is
to study a real-world mobility dataset obtained from the Argo
project. In particular, we observe the mobility of 51 free-drifting
floats deployed on the Mediterranean Sea for approximately
one year and we analyze some important properties of the
underwater network we built. Specifically, we analyze the
contact-time, inter-contact time as well density and network
degree while varying the connectivity degree of the whole
dataset. We then consider three known routing algorithms,
namely Epidemic, PROPHET and Direct Delivery, with the goal
of measuring their performance in real conditions for USANs.
We finally discuss the opportunities arising from the adoption
of opportunistic routing in UASN showing that, even in a very
sparse and strongly disconnected network, it is still possible to
build a limited but working networking framework.

***********************
Titolo: Using Spatial Interpolation to Extend Crowdsensing Coverage in Smart
Cities

Michele Girolami, Stefano Chessa, Mauro Dragone, Mélanie Bouroche, Vinny
Cahill

Abstract: Mobile Crowd Sensing (MCS) is an emerging paradigm that exploits
the ubiquity of smartphones and cheap sensor devices to collect data and
thus contribute to the provision of useful services, especially in the
domains of urban life. While many MCS implementations have been proposed for
different applications, the lack of common performance metrics means that
their efficiency cannot be easily compared. In this paper, we formalize a
generic coverage model for the class of MCS systems sampling spatial
phenomena before introducing a way to produce one such a metric by
exploiting a spatio-temporal estimator. We avail of a large-scale dataset of
users? mobility traces to demonstrate the use of the newly introduced metric
in informing the resolution of a typical problem in MCS system design.
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