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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: 
IEEE PROJECTS & SOFTWARE DEVELOPMENTS 
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE 
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS 
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com
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#

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2014 IEEE DOTNET MOBILE COMPUTING PROJECT Smartdc mobility prediction based adaptive duty cycling for everyday location monitoring

  • 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: IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com
  • 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#