7,834 research outputs found
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN
Mobile devices are rapidly becoming the primary computing device in people's
lives. Application delivery platforms like Google Play, Apple App Store have
transformed mobile phones into intelligent computing devices by the means of
applications that can be downloaded and installed instantly. Many of these
applications take advantage of the plethora of sensors installed on the mobile
device to deliver enhanced user experience. The sensors on the smartphone
provide the opportunity to develop innovative mobile opportunistic sensing
applications in many sectors including healthcare, environmental monitoring and
transportation. In this paper, we present a collaborative mobile sensing
framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on
smartphones capturing and sharing sensed data between multiple distributed
applications and users. MOSDEN follows a component-based design philosophy
promoting reuse for easy and quick opportunistic sensing application
deployments. MOSDEN separates the application-specific processing from the
sensing, storing and sharing. MOSDEN is scalable and requires minimal
development effort from the application developer. We have implemented our
framework on Android-based mobile platforms and evaluate its performance to
validate the feasibility and efficiency of MOSDEN to operate collaboratively in
mobile opportunistic sensing applications. Experimental outcomes and lessons
learnt conclude the paper
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NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping
In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest
MilliSonic: Pushing the Limits of Acoustic Motion Tracking
Recent years have seen interest in device tracking and localization using
acoustic signals. State-of-the-art acoustic motion tracking systems however do
not achieve millimeter accuracy and require large separation between
microphones and speakers, and as a result, do not meet the requirements for
many VR/AR applications. Further, tracking multiple concurrent acoustic
transmissions from VR devices today requires sacrificing accuracy or frame
rate. We present MilliSonic, a novel system that pushes the limits of acoustic
based motion tracking. Our core contribution is a novel localization algorithm
that can provably achieve sub-millimeter 1D tracking accuracy in the presence
of multipath, while using only a single beacon with a small 4-microphone
array.Further, MilliSonic enables concurrent tracking of up to four smartphones
without reducing frame rate or accuracy. Our evaluation shows that MilliSonic
achieves 0.7mm median 1D accuracy and a 2.6mm median 3D accuracy for
smartphones, which is 5x more accurate than state-of-the-art systems.
MilliSonic enables two previously infeasible interaction applications: a) 3D
tracking of VR headsets using the smartphone as a beacon and b) fine-grained 3D
tracking for the Google Cardboard VR system using a small microphone array
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