174,372 research outputs found
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
We report on a data-driven investigation aimed at understanding the dynamics
of message spreading in a real-world dynamical network of human proximity. We
use data collected by means of a proximity-sensing network of wearable sensors
that we deployed at three different social gatherings, simultaneously involving
several hundred individuals. We simulate a message spreading process over the
recorded proximity network, focusing on both the topological and the temporal
properties. We show that by using an appropriate technique to deal with the
temporal heterogeneity of proximity events, a universal statistical pattern
emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data
dissemination, as well as to validate established models of human mobility and
proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data
diffusion in ad-hoc networks, Ad Hoc Netw. (2011
Temporal networks of face-to-face human interactions
The ever increasing adoption of mobile technologies and ubiquitous services
allows to sense human behavior at unprecedented levels of details and scale.
Wearable sensors are opening up a new window on human mobility and proximity at
the finest resolution of face-to-face proximity. As a consequence, empirical
data describing social and behavioral networks are acquiring a longitudinal
dimension that brings forth new challenges for analysis and modeling. Here we
review recent work on the representation and analysis of temporal networks of
face-to-face human proximity, based on large-scale datasets collected in the
context of the SocioPatterns collaboration. We show that the raw behavioral
data can be studied at various levels of coarse-graining, which turn out to be
complementary to one another, with each level exposing different features of
the underlying system. We briefly review a generative model of temporal contact
networks that reproduces some statistical observables. Then, we shift our focus
from surface statistical features to dynamical processes on empirical temporal
networks. We discuss how simple dynamical processes can be used as probes to
expose important features of the interaction patterns, such as burstiness and
causal constraints. We show that simulating dynamical processes on empirical
temporal networks can unveil differences between datasets that would otherwise
look statistically similar. Moreover, we argue that, due to the temporal
heterogeneity of human dynamics, in order to investigate the temporal
properties of spreading processes it may be necessary to abandon the notion of
wall-clock time in favour of an intrinsic notion of time for each individual
node, defined in terms of its activity level. We conclude highlighting several
open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series:
Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.
Pervasive intelligent routing in content centric delay tolerant networks
This paper introduces a Swarm-Intelligence based Routing protocol (SIR) that aims to efficiently route information in content centric Delay Tolerant Networks (CCDTN) also dubbed pocket switched networks. First, this paper formalizes the notion of optimal path in CCDTN and introduces an original and efficient algorithm to process these paths in dynamic graphs. The properties and some invariant features of these optimal paths are analyzed and derived from several real traces. Then, this paper shows how optimal path in CCDTN can be found and used from a fully distributed swarm-intelligence based approach of which the global intelligent behavior (i.e. shortest path discovery and use) emerges from simple peer to peer interactions applied during opportunistic contacts. This leads to the definition of the SIR routing protocol of which the consistency, efficiency and performances are demonstrated from intensive representative simulations
Social-aware Forwarding in Opportunistic Wireless Networks: Content Awareness or Obliviousness?
With the current host-based Internet architecture, networking faces
limitations in dynamic scenarios, due mostly to host mobility. The ICN paradigm
mitigates such problems by releasing the need to have an end-to-end transport
session established during the life time of the data transfer. Moreover, the
ICN concept solves the mismatch between the Internet architecture and the way
users would like to use it: currently a user needs to know the topological
location of the hosts involved in the communication when he/she just wants to
get the data, independently of its location. Most of the research efforts aim
to come up with a stable ICN architecture in fixed networks, with few examples
in ad-hoc and vehicular networks. However, the Internet is becoming more
pervasive with powerful personal mobile devices that allow users to form
dynamic networks in which content may be exchanged at all times and with low
cost. Such pervasive wireless networks suffer with different levels of
disruption given user mobility, physical obstacles, lack of cooperation,
intermittent connectivity, among others. This paper discusses the combination
of content knowledge (e.g., type and interested parties) and social awareness
within opportunistic networking as to drive the deployment of ICN solutions in
disruptive networking scenarios. With this goal in mind, we go over few
examples of social-aware content-based opportunistic networking proposals that
consider social awareness to allow content dissemination independently of the
level of network disruption. To show how much content knowledge can improve
social-based solutions, we illustrate by means of simulation some
content-oblivious/oriented proposals in scenarios based on synthetic mobility
patterns and real human traces.Comment: 7 pages, 6 figure
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