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Robust Trajectory Estimation for
Crowdsourcing-Based Mobile Applications
ABSTRACT:
Crowdsourcing-based mobile applications are becoming more and more
prevalent in recent years, as smartphones equipped with various built-in
sensors are proliferating rapidly. The large quantity of crowdsourced
sensing data stimulates researchers to accomplish some tasks that used to
be costly or impossible, yet the quality of the crowdsourced data, which is
of great importance, has not received sufficient attention. In reality, the
low-quality crowdsourced data are prone to containing outliers that may
severely impair the crowdsourcing applications. Thus in this work, we
conduct pioneer investigation considering crowdsourced data quality.
Specifically, we focus on estimating user motion trajectory information,
which plays an essential role in multiple crowdsourcing applications, such
as indoor localization, context recognition, indoor navigation, etc. We
resort to the family of robust statistics and design a robust trajectory
estimation scheme, name TrMCD, which is capable of alleviating the
negative influence of abnormal crowdsourced user trajectories,
differentiating normal users from abnormal users, and overcoming the
challenge brought by spatial unbalance of crowdsourced trajectories. Two
real field experiments are conducted and the results show that TrMCD is
robust and effective in estimating user motion trajectories and mapping
fingerprints to physical locations.

2. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101
#1, Ist
Cross, Ist
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EXISTING SYSTEM:
We conduct pioneer investigation considering crowdsourced data quality.
Specifically, we focus on estimating user motion trajectory information,
which plays an essential role in multiple crowdsourcing applications, such
as indoor localization, context recognition, indoor navigation, etc. We
resort to the family of robust statistics and design a robust trajectory
estimation scheme, name TrMCD, which is capable of alleviating the
negative influence of abnormal crowdsourced user trajectories,
differentiating normal users from abnormal users, and overcoming the
challenge brought by spatial unbalance of crowdsourced trajectories.
Smart phones today are equipped with various functional sensors, which
act as important information interfaces between users and environments.
These advances have stimulated the development of crowdsourced sensing
applications based on smartphones, such as indoor localization, context
recognition, indoor navigation, etc.
PROPOSED SYSTEM:
The widely existed problem of trajectory density unbalance in
crowdsourcing is accommodated in our proposed method. Accurate
trajectory estimation in trajectory-sparse area cannot be accomplished by
employing statistical methods even without outliers. Our method
overcomes the difficulty based on the trajectory information in trajectory

3. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101
#1, Ist
Cross, Ist
Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com
www.ecruitments.com
dense area. Our proposed method also relies on robust statistics. However,
it differs from the above methods in that the measurement dimension of
our method is higher. The previous estimators are estimating a single
measurement value between sensor nodes, but our target is to estimate
multivariate measurements simultaneously.
CONCLUSION:
We investigated the problem of how to estimate crowdsourced user motion
trajectories robustly and map the fingerprints to the physical locations
correctly. We have derived a robust estimation method, named TrMCD,
which can not only predict user trajectories, but also differentiate normal
users from abnormal users, resulting in a novel user based robustness. The
difficulty in trajectory estimation due to unbalance distribution of
crowdsourced trajectories is also accommodated in TrMCD. The
preliminary experiment results show that TrMCD achieves high robustness
and effectiveness compared to the traditional LV estimator and LS
estimator. TrMCD sets up a pioneer work to guarantee that the large
amount of crowdsourced data is used in a robust and effective way. Our
on-going research focus on tailoring our method for specific
crowdsourcing-based mobile applications.