32,019 research outputs found
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
Probabilistic Human Mobility Model in Indoor Environment
Understanding human mobility is important for the development of intelligent
mobile service robots as it can provide prior knowledge and predictions of
human distribution for robot-assisted activities. In this paper, we propose a
probabilistic method to model human motion behaviors which is determined by
both internal and external factors in an indoor environment. While the internal
factors are represented by the individual preferences, aims and interests, the
external factors are indicated by the stimulation of the environment. We model
the randomness of human macro-level movement, e.g., the probability of visiting
a specific place and staying time, under the Bayesian framework, considering
the influence of both internal and external variables. We use two case studies
in a shopping mall and in a college student dorm building to show the
effectiveness of our proposed probabilistic human mobility model. Real
surveillance camera data are used to validate the proposed model together with
survey data in the case study of student dorm.Comment: 8 pages, 9 figures, International Joint Conference on Neural Networks
(IJCNN) 201
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
Characterising two-pathogen competition in spatially structured environments
Different pathogens spreading in the same host population often generate
complex co-circulation dynamics because of the many possible interactions
between the pathogens and the host immune system, the host life cycle, and the
space structure of the population. Here we focus on the competition between two
acute infections and we address the role of host mobility and cross-immunity in
shaping possible dominance/co-dominance regimes. Host mobility is modelled as a
network of traveling flows connecting nodes of a metapopulation, and the
two-pathogen dynamics is simulated with a stochastic mechanistic approach.
Results depict a complex scenario where, according to the relation among the
epidemiological parameters of the two pathogens, mobility can either be
non-influential for the competition dynamics or play a critical role in
selecting the dominant pathogen. The characterisation of the parameter space
can be explained in terms of the trade-off between pathogen's spreading
velocity and its ability to diffuse in a sparse environment. Variations in the
cross-immunity level induce a transition between presence and absence of
competition. The present study disentangles the role of the relevant biological
and ecological factors in the competition dynamics, and provides relevant
insights into the spatial ecology of infectious diseases.Comment: 30 pages, 6 figures, 1 table. Final version accepted for publication
in Scientific Report
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