it is iee based ppt it is heelpful for b.tech students seminar

1. Heyna johnsson.
Reg no. 14022119
S7 cse.
Under guidance of
Sumi miss.
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2. INTRODUCTION
 In this paper we assert that GPS signal strength not
only affaires location sensing but also the actual
consumed power from a smart phone battery.
 Specifically, we investigate and analyze the
quantitative relationship between the SNR(signal-
to-noise-ratio) of GPS satellite and the amount of
power consumption while using a localization
service.
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3. SMART CITY
 The term smart city means that investment of human
and social capital infrastructure is fuel sustainable
economic growth and high quality of life with wise
management of natural resources

 Sensors provide pulse of cities
 Most important sensing device now is smart phones
that connect smart city system with its citizen by
using its various sensors.
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4. Enabling technologies
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GPS
Finger print sensor
GSM/CDMA
camera
accelerometer
Gyroscope
Magnetometer
Barometer
Proximity
Light sensor
Touch sensor
•Smart phone has 14 or more sensors to monitor to the
environment and provide various facilities to the user

5. Characterizing the Impact of
GPS Signal Strength on
Power Consumption
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6. MOTIVATIONS
 The energy drawn from a battery depends on the
number of applications and their energy
requirements.
 Some applications are much heavier than others in
terms of their energy requirements.
 GPS using apps are among the top power hungry
applications.
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7.  There is a huge gap between smart phone
development and battery enhancement.
 New smart phone applications demand location
positioning systems to provide location based
services.
 GPS stays the best among its alternatives even it is the
hungriest for power because of it’s accuracy.
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8. Objective:
 We review power states that smart phone experiences
while sensing for GPS satellites signals to determine
specific location.
 We develop an android mobile app for the power
consumption related measurements.
 We use the results from the measurements of our
developed app as well as from an off shelf app.
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9. ANALYSIS METHODS
 TRACE COLLECTION/ENERGY IMPACT OF WEAK
AND STRONG GPS SATELLITES SIGNAL STRENGTH
DEPENDING ON BATTERY CHARGE LEVEL
 TRACE COLLECTION/ENERGY IMPACT OF WEAK
AND STRONG GPS SATELLITES SIGNAL STRENGTH
USING MONSOON
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10. TRACE COLLECTION/ENERGY IMPACT OF WEAK
AND STRONG GPS SATELLITES SIGNAL STRENGTH
DEPENDING ON BATTERY CHARGE LEVEL
 We ran an app from play store on fully charged LG
Nexus 4 smart phone.
 Observe its battery for one hr. and we recorded the
battery every 6 min.
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11.  We did this experiment where GPS satellite signal
strength is weaker(>25) and out side where it is
stronger( around 42) .
 Then we did same on app that we had developed
ourselves.
 The results is that battery charge level differ according
to their location.
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12.  Running the application inside battery charge level
decreases from 100% to 79%, that means 21%.
 The device running out side battery charge level
decreases 100% to 87%. That means 13%.
 So running a location based application under good
satellite SNR reduces power consumed by 38%.
Compared against power consumed under bad satellite
SNR

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13. GPS SNR
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16. TRACE COLLECTION/ENERGY IMPACT OF WEAK AND
STRONG GPS SATELLITES SIGNAL STRENGTH USING
MONSOON
 The process of finding mobile location coordination
as per application request consist of following ste
ps.
1- Finding the InView satellites.
2- Determine which of the InView satellites
can be used to find the current location of inuse
satellite
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17. 3- Start calculating the location coordination
This steps takes variable time that is called time to first
fix TTFF.
 We know SNR affects the process of location finding
and hence the consumed power to prove this we have
used a power monitor tool called monsoon, this tool
monitor The power consumed from the mobile device
battery.
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18.  “We know SNR affects the process of location finding
and hence the consumed power ?” to proove this we
have used a power monitor tool called monsoon, this
tool monitor the power consumed from the mobile
device battery
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19. Findings
Indoor
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20.  “We know SNR affects the process of location finding
and hence the consumed power ?” to proove this we
have used a power monitor tool called monsoon, this
tool monitor the power consumed from the mobile
device battery
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21. Out door
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22.  Higher SNR value GPS signal consume less energy and
While low GPS signal causing faster energy drain.
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23. RESULTS
 In this paper we motivated by the fact that location
based applications among the most power hungry
apps, we have performed a measurement study of GPS
satellite signal strength.
 The relationship between satellite SNR, fixing time
and power consumption is weaker satellite SNR leads
to longer fixing time and hence more consumed
power.
 We also performed experiments to quantify the energy
consumption of location specific points using GPS
under poor and good signal strength.
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24. Conclusion
 To the best of our knowledge, this is the first study
that provides a quantitative understanding of how the
poor strength (SNR) of satellite signals will cause
relatively higher power drain from battery .
 This work is an important step towards understanding
the power usage of location based apps.
 Future work will look into further evaluation of the
proposed model and explore strategies to reduce
power consumption of location based application.
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25. References
 A. Caragliu, C. D. Bo, and P. Nijkamp, ‘‘Smart cities in
europ
 [2] (Dec. 1, 2015). Citymapper.
 [3] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and
M. Zorzi, ‘‘InternetofThingsforsmartcities,
 [4] M. d’Aquin, J. Davies, and E. Motta, ‘‘Smart cities,
data: Challenges opportunities for semantic
technologies,’’.
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