hello everyone this is tao xiao from
Hunan university i'm honored to be
here and make this presentation
the topic of our project is mainly about
using crowd sensing on shared bikes to
collect air data
as the pictures shown below shared bikes
are booming over the world
these mobile platforms provide a natural
scalable and ubiquitous solution to
carry IoT devices especially
appropriate for task which has
short-term and geographical demands within
a certain range such as air quality
monitoring
there are several challenges when we
design our system the first one is how
to deploy geographically-dense measurements
which are micro scale size
secondly how to reduce the measurements
errors on IoT sensors employed by mobile
crowd sensing
and the third is how to exploit
collected data to infer the air quality
status on the data points without
measurements
in our project we did some research on
related walks as belows
here is a system overview of mobile
crowd-sensing system we named it UbiAir
it is designed using a mobile crowd
sensing framework and implemented as a
novel client server system following
the sharing economic paradigm
UbiAir contains three modules which are
data sampling, data collaboration, and
dates analysis respectively
this is a diagram of data sampling
module according to the spatial
precision of air data we want to
reconstruct, the space would be split
into grids within specific density and
we deployed the red envelope, namely
rewards, to motivate users to collect the
data at crucial grid points
for the user end, we implemented a
mobile application for user to interact
with our system server
users can simply get it by scanning the
QR code on the shared bikes
In hard calibration part, to obtain a
well-trained mode
the relationship between the
measurements of low-cost sensors and the
expected calibration measurements
is decomposed into a linear part and
a non-linear part
then in software calibration part
in this example we estimated the data at
points at S0 without data
based on the data along a certain
trajectory around, then S0 would be
calculated
with different formula according to
whether it is a emission source point or
not
we proposed an adaptive Bayesian
compressive sensing mode reconstructed
the data into fine-grained. In general, it
contains three phases which are sampling
recovery and reconstruction respectively
see the paper for details if you are
interested
this is the formulas about selecting
projections adaptively to get the
reconstructed data
simply put, it gradually takes the
current estimates variance as the
criterion to optimizing the red
envelope deployments
by embedding our sensors box and its
power system into shared bikes
We verified our UbiAir system initially at our
university
here
and above are the comparisons
between sampling maps and reconstruction
maps of the BCS approach in the same
monitoring area
We can see from them that the data
reconstruction maps present smoother
color transition than the date sampling
map
and the grid squares at the same
position present similar colors among
these heatmaps
here we conduct several experiments on
external factors to help us to set proper
parameters
in our projects
then we compare our hardware and
software calibration methods
with other methods. Overall, both of them
present better performance than other
methods
in order to evaluate the efficiency of our
proposed adaptive BCS algorithms on data
recovery we compared adaptive BCS with
other three methods
Furthermore, we studied the relationship
between the number of model's input
data and the reconstruction error
by comparing adaptive BCS with other
three methods
to sum up, UbiAir is light-weight, low-cost
accurate and scalable system
presenting promising performance and the
usuality on combined mobile crowdsensing with
sharing economy for fine-grained air
quality monitoring
for future works we might extend the
estimation to be an online operation for
the cases without position signals
and design better rewards to encourage
users to upload collected data
immediately
additionally we could use
blockchain to protect user privacy
that's all thanks for
listening