Personalized power saving profiles generation analyzing smart device usage patterns

Personalized Power Saving Profiles Generation
Analyzing Smart Device Usage Patterns
Soumya Kanti Datta
Research Engineer, EURECOM, France
Email: soumya-kanti.datta@eurecom.fr
7th IFIP Wireless and Mobile Networking Conference

Contents
• Introduction
• Client-server architecture for power saving
• Usage Patterns
• Power Saving Profiles
• Use Cases
• Results
• Conclusion
7-Apr-15 Personalized Power Saving Profiles Generation Analyzing Smart Device Usage Patterns 2

Introduction
• Power hungry components in smart devices
– Display, network, GPS
• Power consumption depends on individual
usage patterns
• Focus
– Analyse the usage pattern
– Recommend personalized power saving profiles

Contents
• Introduction
• Usage Patterns
• Use Cases
• Results
• Conclusion

Client-Server Architecture

Monitoring Module Architecture

Contents
• Introduction
• Usage Patterns
• Use Cases
• Results
• Conclusion

Usage Patterns
• Characterized by
– Day of the week (d)
– Time interval of a day (t)
– Location (s)
• For each (d, t, s)
– An usage pattern is generated

Algorithm to Generate Usage Patterns
• For each day of the week of individual users, repeat the following.
1. Count the number of distinct locations that the user is subscribed to from the
collected data.
2. Determine the time interval(s) associated with each distinct location. It is
possible for a location (e.g. home) to be associated with multiple time
intervals at different parts of the day.
3. For each pair of time interval and location, repeat the steps 4-9 which
produces the respective usage pattern.
4. Determine the running applications and their CPU load.
5. Determine the change in battery level and status.
6. Determine the volume level for audio functions.
7. Calculate the amount of network usage, status of Bluetooth, mobile data, Wi-
Fi and GPS (i.e. on/off) and the duration of GPS usage.
8. Read the CPU loads and corresponding operating frequencies.
9. Record the brightness level, screen timeout value and the interaction time
with the device.

Useful Information Derived
• Patterns corresponding to
– Very high device interaction time
– Higher network usage
– Battery charging
– Intense CPU operations

Contents
• Introduction
• Usage Patterns
• Use Cases
• Results
• Conclusion

Power Saving Profiles
• Composed of different combination of settings
– Regulate network technologies
– Brightness value
– Limit network usage etc.
• Each pattern has its own power saving profile
• Intelligent activation of the profiles reduce
power consumption

Algorithm to generate Profiles
• Provide the users with choices to kill the unnecessary and CPU intensive
applications.
• If the pattern registers high device interaction time
– Reduce brightness and screen time out values
• Set a limit for the amount of daily network usage.
• For very less interaction with the device
– Switch off network when the screen is off
– Switched on network when the screen is on.
• During the night when there is no interaction with the devices
– Turn off the wireless network
• If battery is critically low (i.e. below 10%)
– Tone down brightness and timeout to min and turn off network usage.
– In jail broken devices, scale down frequency to minimum and kill CPU intensive apps.
– Turn off any vibration feedback for notification, haptic feedback and audible touch tones and
selections.
• Auto-sync is periodically switched on during the day. The time period can be
configured by user or the app.
• If GPS is on for long, ask the user to switch it off.

Contents
• Introduction
• Usage Patterns
• Use Cases
• Results
• Conclusion

Use Case - 1

Use Case - 2

Contents
• Introduction
• Usage Patterns
• Use Cases
• Results
• Conclusion

Power Consumption Reduction at Display

Reduction at Network Interfaces

Evaluation using Smart Devices

Advantages
• Dynamic computation of power saving profiles
• Detection of change in user behaviour
• Evolution of profiles
• Usage pattern based feedback

Contents
• Introduction
• Usage Patterns
• Use Cases
• Results
• Conclusion

Conclusion
• Client-server architecture to reduce power
consumption in smart devices
• Derivation of usage patterns and
corresponding power saving profiles
• Detection of change in usage pattern and
evolution of the pattern
• Power saving up to 87%

Personalized power saving profiles generation analyzing smart device usage patterns

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