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1. GLOBALSOFT TECHNOLOGIES
SmartDC: Mobility Prediction-Based Adaptive Duty
Cycling for Everyday Location Monitoring
ABSTRACT
Monitoring a user's mobility during daily life is an essential requirement in providing advanced
mobile services. While extensive attempts have been made to monitor user mobility, previous
work has rarely addressed issues with predictions of temporal behavior in real deployment. In
this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to
provide contextual information about a user's mobility: time-resolved places and paths. Unlike
previous approaches that focused on minimizing energy consumption for tracking raw
coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful
places with a given energy constraint. SmartDC comprises unsupervised mobility learner,
mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC
estimates the regularity of individual mobility and predicts residence time at places to determine
efficient sensing schedules. Our experiment results show that SmartDC consumes 81 percent less
energy than the periodic sensing schemes, and 87 percent less energy than a scheme employing
context-aware sensing, yet it still correctly monitors 90 percent of a user's location changes
within a 160-second delay.
Existing System:
Monitoring a user's mobility during daily life is an essential requirement in providing advanced
mobile services. While extensive attempts have been made to monitor user mobility, previous
work has rarely addressed issues with predictions of temporal behavior in real deployment. In
this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to
provide contextual information about a user's mobility: time-resolved places and paths. Unlike
previous approaches that focused on minimizing energy consumption for tracking raw
coordinates.
Proposed System:
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2. we propose efficient techniques to maximize the accuracy of monitoring meaningful places with
a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor,
and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of
individual mobility and predicts residence time at places to determine efficient sensing
schedules. Our experiment results show that SmartDC consumes 81 percent less energy than the
periodic sensing schemes, and 87 percent less energy than a scheme employing context-aware
sensing, yet it still correctly monitors 90 percent of a user's location changes wit hin a 160-second
delay
System Specification
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 14’ Colour Monitor.
• Mouse : Optical Mouse.
• Ram : 512 Mb.
Software Requirements:
• Operating system : Windows 7.
• Coding Language : ASP.Net with C#