Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose Sparse-GPS: a new computing framework for GPS acquisition via sparse approximation. Within the framework, GPS signals can be efficiently compressed by random ensembles. The sparse acquisition information, pertaining to the visible satellites that are embedded within these limited measurements, can subsequently be recovered by our proposed representation dictionary. By extensive empirical evaluations, we demonstrate the acquisition quality and energy gains of Sparse-GPS. We show that it is twice as energy efficient than offloading uncompressed data, and has 5-10 times lower energy costs than standalone GPS; with a median positioning accuracy of 40 m.

1. Energy Efficient GPS Acquisition
with Sparse-GPS
(1) Prasant Misra
(2) Wen Hu
(3) Yuzhe Jin
(3) Jie Liu
(4) Amanda Souza de Paula
(5) Niklas Wirstrom
(5) (6) Thiemo Voigt
(1) Robert Bosch Centre for Cyber Physical Systems, Indian Institute of Science, Bangalore
(2) CSIRO Computational Informatics, Brisbane, Australia
(3) Microsoft Research, Redmond, USA
(4) University of Sao Paulo, Sao Paulo, Brazil
(5) SICS Swedish ICT, Stockholm, Sweden
(6) Uppsala Universitet, Uppsala, Sweden
IEEE/ACM IPSN 2014, Berlin 1
“Energy Efficient GPS Acquisition with Sparse-GPS”
P. Misra, W. Hu, Y. Jin, J. Liu, A.S. de Paula, N. Wirstrom, and T. Voigt
In Proceedings of the 13th ACM/IEEE Conference on Information Processing in Sensor Networks,
IPSN '14 (IP Track)
[ A.R.: 23/111 = 20% | CORE Ranking: A* ]

2. Motivating Application
IEEE/ACM IPSN 2014, Berlin 2

3. IEEE/ACM IPSN 2014, Berlin
Tracking & Monitoring Megabats: Flying Foxes
Motivating Application
Disease
Vector
Agricultural
pests
Behavior
Interaction
• Hendra Virus
• Ebola
• SARS-like Coronavirus
• Crop Damage
• Not well understood
• Threatened species
• With other flying
foxes and animals
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4. IEEE/ACM IPSN 2014, Berlin
Monitoring Platform
Platform details
Low power SoC
- microcontroller core
- IEEE 802.15.4 radio transceiver
Power Supply
Li-Ion Battery Li-Ion Charger Solar Panels
Multi-modal
sensors
GPS
Flash
Wireless Sensor Node
R. Jurdak, B. Kusy, P. Sommer, N. Kottege, C. Crossman, A. McKeown, D. Westcott, "Camazotz: Multimodal Activity-based GPS Sampling," In IPSN 2013.
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5. IEEE/ACM IPSN 2014, Berlin
Application Requirements and Challenges
Computing challenge
• Operate within very tight energy budgets
• Needs to use energy hungry GPS for tracking
Application requirements
• Long-term, unsupervised operation
• Delay tolerant
• Bats travel between different camps and other locations
• Store sensed data locally in the flash; and upload the contents
at known camps
Base-station
DB/Server
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6. GPS is highly demanding in energy ?
IEEE/ACM IPSN 2014, Berlin 6

7. IEEE/ACM IPSN 2014, Berlin
GPS Overview-I: Infrastructure
• Constellation of 32 GPS satellites vehicles (SV), synced in time to
the nanosecond level
• SVs, periodically, transmit signals containing time and trajectory
parameters (Ephemeris)
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8. IEEE/ACM IPSN 2014, Berlin
Unique sequence of 1023 bits
transmitted at 1023 kbps,
repeats every millisecond
50 bps
1.575 GHz
GPS Overview-II: GPS Signal
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9. IEEE/ACM IPSN 2014, Berlin
GPS Receiver estimates its location by:
• Computing time-of-flight (pseudo range) from each visible SV to itself
• Combining with the respective SVs’ location at the time
GPS Overview-III: GPS Receiving Operation
Sense &
Record
Acquisition Tracking Decoding
Least
Square
1 ms of data (4kB)
Intensive computation
 SV IDs
 Codephase (subMS)
 Doppler Shift
Continuous,
every millisecond
 Timestamp (MS) – 06 s
 Ephemeris – 30 s
 TOF (Pseudo range) = MS + subMS
 Ephemeris
[Lat., Long.]
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10. IEEE/ACM IPSN 2014, Berlin
Operational Impact
1. Standalone GPS receiver needs to be turned ON for up to 30 seconds in order to receive a complete
data packet from the SV  Difficult to duty-cycle
2. Requires significant amount of processing  Needs a sophisticated CPU
Sense &
Record
Acquisition Tracking Decoding
Least
Square
1 ms of data (4kB)
Intensive computation
 SV IDs
 Codephase (subMS)
 Doppler Shift
Continuous,
every millisecond
 Timestamp (MS) – 06 s
 Ephemeris – 30 s
[Lat., Long.]
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11. Previous work:
Delay tolerant + Constrained platforms (wireless sensor nodes)
IEEE/ACM IPSN 2014, Berlin 11

12. Sense &
Record
Cloud-Offloaded GPS (CO-GPS)
Acquisition
Tracking
Decoding
Least-square
J. Liu, B. Priyantha, T. Hart, H. S. Ramos, A. A. F. Loureiro, and Q. Wang, “Energy efficient GPS sensing with cloud offloading,” in SenSys ’12.
IEEE/ACM IPSN 2014, Berlin 12

13. Scope for Improvement
The task of offloading GPS data (raw samples) to the cloud (base-station)
introduces an additional cost in energy.
Can this be limited ?
Sense &
Record
Acquisition
Tracking
Decoding
Least-square
IEEE/ACM IPSN 2014, Berlin 13

14. Sparse-GPS (S-GPS)
S-GPS adopts the sample-and-process approach of CO-GPS,
but reduces the energy cost of data offloading.
In our application, energy is a critical resource and its
conservation is greatly valued.
Sense & Record
Compressed data
Acquisition
Tracking
Decoding
Least-square
IEEE/ACM IPSN 2014, Berlin 14

15. Sparse-GPS (S-GPS)
IEEE/ACM IPSN 2014, Berlin 15

16. IEEE/ACM IPSN 2014, Berlin
Key Insight Leading to S-GPS
Conventional: GPS Acquisition by Cross-correlation
Key insight:
The information content is sparse as the value of the code phase (i.e., time delay) and frequency bin (i.e.,
Doppler shift) corresponding to the correlation peak is only useful.
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17. IEEE/ACM IPSN 2014, Berlin
S-GPS: Theoretical Foundation - I
S-GPS is based on the theory of Sparse Approximation
Motivating insight of Sparse Approximation. One can accurately and efficiently recover the information of a high
dimensional signal from only a small number of compressed measurements, when the signal-of-interest is
sufficiently sparse in a certain transform domain.
x s

sx 
To recover s sxtsss 

:..minarg:)( 11
1


Dictionary
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18. IEEE/ACM IPSN 2014, Berlin
S-GPS: Theoretical Foundation - II
Dimensionality reduction with random ensembles ?
x s

x
y
ssxy )()(  
 

211
1
:..minarg:)( ystsss

Dictionary
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19. IEEE/ACM IPSN 2014, Berlin
Design of Dictionary - I
[-] Sparsely represents the code-phase, but not the frequency bin
[-] Breaks down the joint (codephase delay, Doppler) estimation
problem into smaller sub-problems
 global optimal cannot be guaranteed
Multi-channel Stacked

Properties:
1. Should sparsely represent both:
(codephase delay, Doppler shift )
2. Should be incoherent with

Dictionary
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20. IEEE/ACM IPSN 2014, Berlin
Design of Dictionary - II
Multi-channel Flattened
Single-channel Flattened
[+] Sparsely represents both code-phase and frequency bin
[+] Joint (Delay, Doppler) estimation per satellite
[-] Optimizer gets biased towards satellites with strongest signal
[-] Requires a minimum of 32 times more memory
(easily scales into tens of gigabytes)
[+] Sparsely represents both code-phase and frequency bin
[+] Joint (Delay, Doppler) estimation over ALL satellites
[+] Good tradeoff : sparsity, performance and space complexity
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21. IEEE/ACM IPSN 2014, Berlin
Search Algorithm: S-GPS
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22. IEEE/ACM IPSN 2014, Berlin
Both the methods yield the same result for the position of the tallest peak
(code phase = 515 chips and frequency bin index = 19)
Uncompressed 70% compressed (alpha=0.30)
Basic Performance Test
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23. IEEE/ACM IPSN 2014, Berlin
Low peak sharpness (i.e., ratio of
the first to the second tallest
peak), and high recovery noise
Incorrect recovery Low peak sharpness, but low
recovery noise
S-GPS Acquisition Challenges with 2ms of data
Accepted practice is to use 2ms of data for GPS acquisition
Low SNR levels (18-25 dB), typical in GPS signals, severely impacts the recovery accuracy
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24. IEEE/ACM IPSN 2014, Berlin
SNR Boost Stage:
Accumulate K coefficient vectors (from the recovery process), and sum their intensities to derive the consolidated
estimation point. [each K corresponds to recovery result of 1ms of data]
Improving Acquisition Reliability
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25. Evaluation and Results
IEEE/ACM IPSN 2014, Berlin 25

26. IEEE/ACM IPSN 2014, Berlin
Q) Does processing longer data lengths (such as 10/20 ms) promise better acquisition quality than the accepted
practice of 2ms ?
A) There is a high probability of detecting > 1.5x more satellites by processing longer data segments.
Acquisition Quality
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27. IEEE/ACM IPSN 2014, Berlin
For acquiring the same number of satellites as
their equivalent uncompressed data lengths,
there is a 95% probability of success by using as
low as K = 10 for alpha = 0.30.
Median location error is less than 40 m.
Acquisition Quality and Location Accuracy
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28. IEEE/ACM IPSN 2014, Berlin
2x
Energy Consumption
5x
Goodput reported for IEEE 802.15.4 complaint transceivers are between 42kbps and 93.6kbps
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29. IEEE/ACM IPSN 2014, Berlin
Highlights and Way forward …
• We introduced Sparse-GPS, a new computing framework for energy efficient GPS acquisition via sparse
approximation
• Proposed: A new dictionary that combines the information sparsity along all search dimensions
• Analyzed the dependency of the received SNR and satellite acquisition count on data length
• Showed: Using 10-20 ms over 2ms of data, there is a high probability of acquiring 50% additional satellites
with both the conventional and S-GPS
• Demonstrated the GPS acquisition capability and energy gains by empirical evaluations on real GPS signals.
• Showed: S-GPS is 2 times more energy efficient than offloading uncompressed data
• Showed: S-GPS is 5-10 times more energy efficient than standalone GPS
• Showed: S-GPS has a median positioning error of 40m
Highlights
Way forward …
• Develop mechanisms to enhance the received SNR (the most daunting task in realizing the S-GPS solution)
• Explore hardware designs for combining [sense-compress] of the target platform
• Optimize energy expenditure and execution time on the server side
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30. Thanks !!!
IEEE/ACM IPSN 2014, Berlin 30