diff --git a/paper/data/exp_sc1.csv b/paper/data/exp_sc1.csv
index a8d667244473b7e7501670b8d1107332c3609ebf..b4cddbe4f182b8ce16082d528be9173d6b3e4e9e 100644
--- a/paper/data/exp_sc1.csv
+++ b/paper/data/exp_sc1.csv
@@ -1,30 +1,30 @@
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diff --git a/paper/data/exp_sc2.csv b/paper/data/exp_sc2.csv
index 2719578a2594fe1027d61f595a9f29fc65ce33e9..b8a9ebfac4d3a61d2de4ec1afe92c9c14ec2f84c 100644
--- a/paper/data/exp_sc2.csv
+++ b/paper/data/exp_sc2.csv
@@ -1,30 +1,30 @@
-55436,30.787
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+55436,33.365
+57100,49.289
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+53744,139.52
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diff --git a/paper/data/exp_sc3.csv b/paper/data/exp_sc3.csv
index e86c00d832172ac2980a826de919a8aebc4dd394..492f7fefda5c65892868e1d51edc235f138b6c57 100644
--- a/paper/data/exp_sc3.csv
+++ b/paper/data/exp_sc3.csv
@@ -1,30 +1,30 @@
-43732,64.501
-30124,0.9841
-41100,10.203
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-29368,0.96521
-66248,9.2126
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-41060,7.4939
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-59488,75.152
-64756,149.84
+43732,67.078
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+41100,12.781
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+66248,11.79
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+49792,8.0753
+30544,3.4527
+32832,3.4881
+45852,82.877
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+66036,58.453
+31536,3.4775
+41060,10.072
+31224,3.5645
+59488,77.73
+64756,152.42
diff --git a/paper/data/exp_sc4.csv b/paper/data/exp_sc4.csv
index a56bdb0264269b014eb5b837c46815433baa19b5..93329e976705d2d371b755acaefbe5098bf8cbe6 100644
--- a/paper/data/exp_sc4.csv
+++ b/paper/data/exp_sc4.csv
@@ -1,30 +1,30 @@
-22240,1.9647
-21080,1.8755
-22768,1.8004
-22620,2.0003
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-37480,14.421
-48236,23.835
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-38940,128.77
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-36540,16.488
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-62112,68.981
-23356,2.07
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+22768,4.3782
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+22368,4.4342
+39272,115.04
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+37480,16.999
+48236,26.413
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+38940,131.35
+23740,4.7058
+36540,19.065
+22100,4.67
+23232,4.7003
+22268,4.3563
+21696,4.455
+23324,4.5184
+40260,18.257
+21492,4.3899
+42720,43.741
+32840,14.679
+21304,4.5087
+62112,71.559
+23356,4.6479
+21928,4.3705
+20912,4.5039
+21404,4.6108
diff --git a/paper/data/exp_sc5.csv b/paper/data/exp_sc5.csv
index 80676fdab4033a8911b77c68ed7500a1c372cdc2..b3873415f0c25a61e6240036380d37e5cf3a948c 100644
--- a/paper/data/exp_sc5.csv
+++ b/paper/data/exp_sc5.csv
@@ -1,30 +1,30 @@
-30328,35.909
-17524,2.9007
-17020,2.76
-36536,72.154
-33800,31.759
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-18332,2.9418
-33900,7.0066
-18280,2.913
-16912,2.9683
-18360,3.0321
-19568,3.0471
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-53140,109.1
-19508,3.3526
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-36856,98.561
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-38676,52.67
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-19324,3.3869
+30328,38.487
+17524,5.4786
+17020,5.3378
+36536,74.732
+33800,34.337
+37832,33.68
+39876,244.48
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+33900,9.5844
+18280,5.4908
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+53140,111.68
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+19288,6.0449
+36856,101.14
+18456,5.8569
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+34256,6.1039
+18888,5.6484
+31240,69.642
+17224,5.921
+15144,5.2966
+24088,6.3127
+38676,55.248
+19092,5.781
+19324,5.9647
diff --git a/paper/data/exp_sc6.csv b/paper/data/exp_sc6.csv
index 784d0b25ce2e6d4ab22c30939e20701d3d3fbb85..a173a618441cc30e2532a1afbc4533e0814fdaeb 100644
--- a/paper/data/exp_sc6.csv
+++ b/paper/data/exp_sc6.csv
@@ -1,30 +1,30 @@
-14812,4.1445
-31052,19.709
-13928,4.0159
-15320,4.3439
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-56516,197.19
-16224,4.7017
-31788,10.941
-15852,4.9283
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-34720,68.373
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-15628,3.9574
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-30032,17.31
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+16028,7.5061
+14968,6.7159
diff --git a/paper/main.bib b/paper/main.bib
index 02a9448281606b3707fffe5edb5624982761527c..358e5a8d99f0d47b6203e57835e2f87435951a8c 100644
--- a/paper/main.bib
+++ b/paper/main.bib
@@ -5,7 +5,7 @@ title={A novel multi-step algorithm for low-energy positioning using GPS},
year={2016},
volume={},
number={},
-pages={1469-1476},
+removed_pages={1469-1476},
month={July},}
@ARTICLE{4770175,
@@ -15,7 +15,7 @@ title={In-Car Positioning and Navigation Technologies?A Survey},
year={2009},
volume={10},
number={1},
-pages={4-21},
+removed_pages={4-21},
mask_doi={10.1109/TITS.2008.2011712},
ISSN={1524-9050},
month={March},}
@@ -25,9 +25,9 @@ month={March},}
title = {Multisensor Data Fusion},
year = {2008},
isbn = {9781420053067},
- edition = {2nd},
- publisher = {CRC Press, Inc.},
- address = {Boca Raton, FL, USA},
+ removed_edition= {2nd},
+ removed_publisher= {CRC Press, Inc.},
+ removed_address= {Boca Raton, FL, USA},
}
@article{EKF,
@@ -44,27 +44,27 @@ month={March},}
author={Kaplan, E.D.},
isbn={9780890067932},
lccn={95049984},
- series={Artech House telecommunications library},
+ removed_series={Artech House telecommunications library},
mask_url={https://books.google.it/books?id=5OYInwEACAAJ},
year={1996},
- publisher={Artech House}
+ removed_publisher={Artech House}
}
%lavoro di microsoft in cui discutono la possibilità di usare i dati precedenti di fase e frequenza per velocizzzare il fetching
@inproceedings{bib:microsoft-leap,
author = {Ramos, Heitor S. and Zhang, Tao and Liu, Jie and Priyantha, Nissanka B. and Kansal, Aman},
title = {LEAP: A Low Energy Assisted GPS for Trajectory-based Services},
booktitle = {Proceedings of the 13th International Conference on Ubiquitous Computing},
- series = {UbiComp '11},
+ removed_series= {UbiComp '11},
year = {2011},
isbn = {978-1-4503-0630-0},
location = {Beijing, China},
- pages = {335--344},
- numpages = {10},
+ removed_pages= {335--344},
+ numremoved_pages= {10},
mask_url= {http://doi.acm.org/10.1145/2030112.2030158},
mask_doi= {10.1145/2030112.2030158},
acmid = {2030158},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {gps, low energy, mobile location},
}
%studio di fattibilità del downsampling per ricostruire la traiettoria
@@ -75,7 +75,7 @@ title={Feasibility of software-based duty cycling of GPS for trajectory-based se
year={2016},
volume={},
number={},
-pages={18-26},
+removed_pages={18-26},
keywords={Global Positioning System;smart phones;software based duty cycling;trajectory based services;smartphone apps;route accuracy;map matching;path construction algorithm;smartphone GPS receiver;Global Positioning System;Roads;Databases;Energy consumption;Conferences;Google;Image color analysis},
mask_doi={10.1109/CCNC.2016.7444725},
ISSN={2331-9860},
@@ -87,17 +87,17 @@ month={Jan},}
author = {Zhuang, Zhenyun and Kim, Kyu-Han and Singh, Jatinder Pal},
title = {Improving Energy Efficiency of Location Sensing on Smartphones},
booktitle = {Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services},
- series = {MobiSys '10},
+ removed_series= {MobiSys '10},
year = {2010},
isbn = {978-1-60558-985-5},
location = {San Francisco, California, USA},
- pages = {315--330},
- numpages = {16},
+ removed_pages= {315--330},
+ numremoved_pages= {16},
mask_url= {http://doi.acm.org/10.1145/1814433.1814464},
mask_doi= {10.1145/1814433.1814464},
acmid = {1814464},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {energy efficiency, location sensing, location-based applications, smartphone},
}
@@ -107,17 +107,17 @@ month={Jan},}
author = {Lin, Kaisen and Kansal, Aman and Lymberopoulos, Dimitrios and Zhao, Feng},
title = {Energy-accuracy Trade-off for Continuous Mobile Device Location},
booktitle = {Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services},
- series = {MobiSys '10},
+ removed_series= {MobiSys '10},
year = {2010},
isbn = {978-1-60558-985-5},
location = {San Francisco, California, USA},
- pages = {285--298},
- numpages = {14},
+ removed_pages= {285--298},
+ numremoved_pages= {14},
mask_url= {http://doi.acm.org/10.1145/1814433.1814462},
mask_doi= {10.1145/1814433.1814462},
acmid = {1814462},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {GPS energy, continuous location, location-based applications},
}
@@ -126,17 +126,17 @@ month={Jan},}
author = {Thiagarajan, Arvind and Ravindranath, Lenin and LaCurts, Katrina and Madden, Samuel and Balakrishnan, Hari and Toledo, Sivan and Eriksson, Jakob},
title = {VTrack: Accurate, Energy-aware Road Traffic Delay Estimation Using Mobile Phones},
booktitle = {Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems},
- series = {SenSys '09},
+ removed_series= {SenSys '09},
year = {2009},
isbn = {978-1-60558-519-2},
location = {Berkeley, California},
- pages = {85--98},
- numpages = {14},
+ removed_pages= {85--98},
+ numremoved_pages= {14},
mask_url= {http://doi.acm.org/10.1145/1644038.1644048},
mask_doi= {10.1145/1644038.1644048},
acmid = {1644048},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {GPS, car, congestion, energy, localization, location, mobile, phone, sensor, traffic, transportation},
}
@@ -145,17 +145,17 @@ month={Jan},}
author = {Kj{\ae}rgaard, Mikkel Baun and Langdal, Jakob and Godsk, Torben and Toftkj{\ae}r, Thomas},
title = {EnTracked: Energy-efficient Robust Position Tracking for Mobile Devices},
booktitle = {Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services},
- series = {MobiSys '09},
+ removed_series= {MobiSys '09},
year = {2009},
isbn = {978-1-60558-566-6},
location = {Krak\&\#243;w, Poland},
- pages = {221--234},
- numpages = {14},
+ removed_pages= {221--234},
+ numremoved_pages= {14},
mask_url= {http://doi.acm.org/10.1145/1555816.1555839},
mask_doi= {10.1145/1555816.1555839},
acmid = {1555839},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {energy-efficient, gps, positioning, tracking},
}
@@ -164,17 +164,17 @@ month={Jan},}
author = {Wang, Yi and Lin, Jialiu and Annavaram, Murali and Jacobson, Quinn A. and Hong, Jason and Krishnamachari, Bhaskar and Sadeh, Norman},
title = {A Framework of Energy Efficient Mobile Sensing for Automatic User State Recognition},
booktitle = {Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services},
- series = {MobiSys '09},
+ removed_series= {MobiSys '09},
year = {2009},
isbn = {978-1-60558-566-6},
location = {Krak\&\#243;w, Poland},
- pages = {179--192},
- numpages = {14},
+ removed_pages= {179--192},
+ numremoved_pages= {14},
mask_url= {http://doi.acm.org/10.1145/1555816.1555835},
mask_doi= {10.1145/1555816.1555835},
acmid = {1555835},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {eemss, energy efficiency, human state recognition, mobile sensing},
}
@@ -188,9 +188,9 @@ and Narasimhan, Priya",
title="Optimizing Mobile Application Performance with Model--Driven Engineering",
booktitle="Software Technologies for Embedded and Ubiquitous Systems",
year="2009",
-publisher="Springer Berlin Heidelberg",
-address="Berlin, Heidelberg",
-pages="36--46",
+removed_publisher="Springer Berlin Heidelberg",
+removed_address="Berlin, Heidelberg",
+removed_pages="36--46",
abstract="Future embedded and ubiquitous computing systems will operate continuously on mobile devices, such as smartphones, with limited processing capabilities, memory, and power. A critical aspect of developing future applications for mobile devices will be ensuring that the application provides sufficient performance while maximizing battery life. Determining how a software architecture will affect power consumption is hard because the impact of software design on power consumption is not well understood. Typically, the power consumption of a mobile software architecture can only be determined after the architecture is implemented, which is late in the development cycle when design changes are costly.",
isbn="978-3-642-10265-3"
}
@@ -211,7 +211,7 @@ title={EnLoc: Energy-Efficient Localization for Mobile Phones},
year={2009},
volume={},
number={},
-pages={2716-2720},
+removed_pages={2716-2720},
keywords={Global Positioning System;mobile computing;wireless LAN;EnLoc;energy-efficient localization;mobile phones;location-based applications;GPS;WiFi;GSM;Energy efficiency;Mobile handsets;Global Positioning System;GSM;Batteries;Energy measurement;Wireless sensor networks;Costs;Poles and towers;Communications Society},
mask_doi={10.1109/INFCOM.2009.5062218},
ISSN={0743-166X},
@@ -222,18 +222,18 @@ month={April},}
author = {Thokala, Sravan Kumar and Koundinyaa, Pranav and Mishra, Shivakant and Shi, Larry},
title = {Virtual GPS: A Middleware for Power Efficient Localization of Smartphones Using Cross Layer Approach},
booktitle = {Proceedings of the Middleware Industry Track},
- series = {Industry papers},
+ removed_series= {Industry papers},
year = {2014},
isbn = {978-1-4503-3219-4},
location = {Bordeaux, France},
- pages = {2:1--2:7},
+ removed_pages= {2:1--2:7},
articleno = {2},
- numpages = {7},
+ numremoved_pages= {7},
mask_url= {http://doi.acm.org/10.1145/2676727.2676729},
mask_doi= {10.1145/2676727.2676729},
acmid = {2676729},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {GPS abstraction, location based services, power efficient localization, virtual GPS},
}
@@ -242,17 +242,17 @@ month={April},}
author = {Ben Abdesslem, Fehmi and Phillips, Andrew and Henderson, Tristan},
title = {Less is More: Energy-efficient Mobile Sensing with Senseless},
booktitle = {Proceedings of the 1st ACM Workshop on Networking, Systems, and Applications for Mobile Handhelds},
- series = {MobiHeld '09},
+ removed_series= {MobiHeld '09},
year = {2009},
isbn = {978-1-60558-444-7},
location = {Barcelona, Spain},
- pages = {61--62},
- numpages = {2},
+ removed_pages= {61--62},
+ numremoved_pages= {2},
mask_url= {http://doi.acm.org/10.1145/1592606.1592621},
mask_doi= {10.1145/1592606.1592621},
acmid = {1592621},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {energy, sensing, wireless},
}
@@ -264,7 +264,7 @@ title={Energy efficient GPS acquisition with Sparse-GPS},
year={2014},
volume={},
number={},
-pages={155-166},
+removed_pages={155-166},
keywords={compressed sensing;Global Positioning System;radio receivers;synchronisation;compressed sensing;visible satellites;sparse acquisition information;sparse approximation;data offloading operation;energy cost;delay-tolerant applications;energy efficient GPS sensing;low-cost receivers;sparse-GPS;energy efficient GPS acquisition;Global Positioning System;Satellites;Dictionaries;Sensors;Receivers;Satellite broadcasting;Approximation methods;GPS;synchronization;location sensing;energy efficiency;sparse approximation;compressed sensing},
mask_doi={10.1109/IPSN.2014.6846749},
ISSN={},
@@ -275,17 +275,17 @@ month={April},}
author = {Hassanieh, Haitham and Adib, Fadel and Katabi, Dina and Indyk, Piotr},
title = {Faster GPS via the Sparse Fourier Transform},
booktitle = {Proceedings of the 18th Annual International Conference on Mobile Computing and Networking},
- series = {Mobicom '12},
+ removed_series= {Mobicom '12},
year = {2012},
isbn = {978-1-4503-1159-5},
location = {Istanbul, Turkey},
- pages = {353--364},
- numpages = {12},
+ removed_pages= {353--364},
+ numremoved_pages= {12},
mask_url= {http://doi.acm.org/10.1145/2348543.2348587},
mask_doi= {10.1145/2348543.2348587},
acmid = {2348587},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {GPS, sparse fourier transform, synchronization},
}
@@ -294,17 +294,17 @@ month={April},}
author = {Windihastuty, Wiwin and Irsan, Muhammad},
title = {Explorers Zoo: An Application Model of Global Positioning System A Case Study in Ragunan Zoo},
booktitle = {Proceedings of the 5th International Conference on Information and Education Technology},
- series = {ICIET '17},
+ removed_series= {ICIET '17},
year = {2017},
isbn = {978-1-4503-4803-4},
location = {Tokyo, Japan},
- pages = {182--186},
- numpages = {5},
+ removed_pages= {182--186},
+ numremoved_pages= {5},
mask_url= {http://doi.acm.org/10.1145/3029387.3029408},
mask_doi= {10.1145/3029387.3029408},
acmid = {3029408},
- publisher = {ACM},
- address = {New York, NY, USA},
+ removed_publisher= {ACM},
+ removed_address= {New York, NY, USA},
keywords = {Android, GPS, Google Maps, Zoo},
}
@@ -316,7 +316,7 @@ title={Theoretical model of space weather impact on GPS ionospheric delay},
year={2007},
volume={},
number={},
-pages={75-78},
+removed_pages={75-78},
keywords={Global Positioning System;ionospheric disturbances;ionospheric electromagnetic wave propagation;radiowave propagation;space weather impact;GPS ionospheric delay;physical-chemical processes;magnetic-ionospheric conditions;electromagnetic waves propagation;satellite positioning systems;solar activity;Global Positioning System;Delay;Sun;Earth;Ionosphere;Geomagnetism;Satellite broadcasting;Wind;Electronic mail;Chemical processes;space weather;ionospheric disturbances;GPS ionospheric delay},
mask_doi={10.1109/ELMAR.2007.4418803},
ISSN={1334-2630},
@@ -328,7 +328,7 @@ month={Sept},}
author={Thomas Olutoyin Oshin and Stefan Poslad and Athen Ma},
journal={2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications},
year={2012},
- pages={1698-1705}
+ removed_pages={1698-1705}
}
%libro su GPS+inertial navigation
@@ -337,24 +337,24 @@ month={Sept},}
title = {Global Positioning Systems, Inertial Navigation, and Integration},
year = {2007},
isbn = {0470041900},
- publisher = {Wiley-Interscience},
- address = {New York, NY, USA},
+ removed_publisher= {Wiley-Interscience},
+ removed_address= {New York, NY, USA},
}
@incollection{bib:A,
title={Inertial Sensing, GPS and Odometry},
author={Dudek, Gregory and Jenkin, Michael},
booktitle={Springer Handbook of Robotics},
- pages={737--752},
+ removed_pages={737--752},
year={2016},
- publisher={Springer}
+ removed_publisher={Springer}
}
@inproceedings{bib:B,
title={An introduction to event-triggered and self-triggered control},
author={Heemels, WPMH and Johansson, Karl Henrik and Tabuada, Paulo},
booktitle={Decision and Control (CDC), 2012 IEEE 51st Annual Conference on},
- pages={3270--3285},
+ removed_pages={3270--3285},
year={2012},
organization={IEEE}
}
diff --git a/paper/main.tex b/paper/main.tex
index fef262b3f7cfa84aff64b0d1ea0dd0fa90c83a95..497e2e52e8b65d3298fb501519d979623f9a2e47 100644
--- a/paper/main.tex
+++ b/paper/main.tex
@@ -101,7 +101,7 @@ Politecnico di Milano, Italy}
positioning accuracy. We present the model and propose a GPS
sampling strategy that uses both the current positioning confidence,
and information about the GPS status. We complement the GPS sensor
- with internal measurment units and show how the given model exposes
+ with internal measurement units and show how the given model exposes
the battery-accuracy trade-off in the context of sensor fusion. We
demonstrate the usefulness of the proposed sampling strategy using
both simulation and real data.
@@ -158,6 +158,10 @@ Politecnico di Milano, Italy}
\label{sec:concl}
\input{sections/07-concl}
+\vspace{2mm}
+\noindent\textbf{Acknowledgements}:\\
+We would like to thank Isaac Skog (Link{\"o}ping University) that allowed us to reuse and build on his code for the Matlab model. This work was supported by the ELLIIT Strategic Research Area. This work was partially supported by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation.
+
\bibliographystyle{ACM-Reference-Format}
\bibliography{main}
diff --git a/paper/sections/01-intro.tex b/paper/sections/01-intro.tex
index 8bb3fa014537241998211906e92e7db62586b6b1..f867c05b93529c14864def7fa20f90c14f2c3ce8 100644
--- a/paper/sections/01-intro.tex
+++ b/paper/sections/01-intro.tex
@@ -13,12 +13,12 @@ provided by more than one sensor type could allow one to exploit the
sensor benefits and to limit their drawbacks.
One alternative is merging data from GPS sensors with data provided by
-intertial measurments sensors~\cite{bib:gps-imu}. While the GPS is
-power-hungry but provided very precise information, inertial
-measurments sensors are less demanding in terms of battery, but also
+inertial measurements sensors~\cite{bib:gps-imu}. While the GPS is
+power-hungry but provides very precise information, inertial
+measurements sensors are less demanding in terms of battery, but also
less precise. In the literature, optimizations of this type are
-accompanied by experimental data~\cite{ bib:microsoft-leap, bib:enloc-smartphones,
- bib:virtualGPS, bib:accuracy-adaptation,
+accompanied by experimental data~\cite{bib:microsoft-leap,
+ bib:enloc-smartphones, bib:virtualGPS, bib:accuracy-adaptation,
bib:feasibility-duty-cycling, bib:traffic-delay,
bib:entracked-datadriven-modeling, bib:senseLess,
bib:framework-for-energy-efficiency}, that are time-consuming to
@@ -47,15 +47,15 @@ design choices that are behind the GPS system. These choices greatly
influence what can be achieved with any GPS sensor, as they introduce
basic limitations and characteristics of the technology. In this
specific context, we highlight how a dynamical model is necessary to
-capture the involved \emph{phenomena}. In fact, a GPS sensor that
-receive the same input data can behave differently, depending on its
-internal state.
+capture the involved \emph{phenomena}. In fact, GPS sensors that
+receive the same input data can behave differently, depending on the
+sensor's internal state.
\item \textbf{Design:} It identifies opportunities for battery
savings. Specifically, modeling the GPS-related \emph{phenomena}
allows us to devise a sampling strategy that exploits the technology
characteristics.
\item \textbf{Integration:} It integrates the GPS with an ecosystem of
-intertial measurment sensors. While this is not a new idea, thanks to
+inertial measurement sensors. While this is not a new idea, thanks to
our model we are able to capture the trade-offs between the different
sensor types programmatically and to exploit the characteristics of
each sensor.
@@ -66,7 +66,7 @@ the topic of GPS optimization, Section~\ref{sec:related} describes
related work. Section~\ref{sec:gps} describes the physical principles
behind the GPS receiver and shows how we capture these principles with
our model. Section~\ref{sec:fusion} discusses a sensor fusion
-algorithm that merges the information obtained by Intertial
+algorithm that merges the information obtained by Inertial
Measurement Units (IMUs) with the GPS data. The sampling strategy that
is derived using the given models is described in
Section~\ref{sec:control}. We evaluate our proposal with data from
diff --git a/paper/sections/02-related-work.tex b/paper/sections/02-related-work.tex
index 30a16ea50601a7d5ef2eee83ab5cd4a776ce273e..4ef0cc785ddbaea1726a2c1b868b4f84683cefa5 100644
--- a/paper/sections/02-related-work.tex
+++ b/paper/sections/02-related-work.tex
@@ -12,7 +12,7 @@ bib:computation-offloading, bib:selective-tracking,
bib:microsoft-leap, bib:sparse-fourier}. The authors of
\cite{bib:computation-offloading} aim at outsourcing the device
computation (once the data has been received) to some server, using a
-network connnection. \cite{bib:selective-tracking} improves the GPS
+network connection. \cite{bib:selective-tracking} improves the GPS
receiver power-efficiency selecting only a subset of visible
satellites to be tracked. Other works aim at improving the speed of
the signal acquisition, either using information from previous
@@ -21,13 +21,13 @@ for the decoding of the signal~\cite{bib:sparse-fourier, 7528057}.
The second class includes several attempts to build an
\emph{adaptation} layer that controls the usage of positioning
-sensors, keeping the antenna off for as much time as
+sensors, keeping the receiver off for as much time as
possible~\cite{bib:enloc-smartphones, bib:virtualGPS,
bib:accuracy-adaptation}. Usually, this is achieved implementing a
trade-off controller, that trades accuracy for energy consumption. In
the same class we can include works that exploit other sensors. When
the adaptation layer detects that the user state does not need high
-accuracy, it minimizes the GPS antenna usage by turning it off and
+accuracy, it minimizes the GPS receiver usage by turning it off and
enabling it again only on demand~\cite{bib:feasibility-duty-cycling,
bib:traffic-delay, bib:entracked-datadriven-modeling, bib:senseLess,
bib:framework-for-energy-efficiency}. Among the works on this
diff --git a/paper/sections/03-model.tex b/paper/sections/03-model.tex
index 28bc6496956899f64bad2f1c69ffb3a974758088..b873a3184093d72cc3743a7f1c4a1067ed1c182f 100644
--- a/paper/sections/03-model.tex
+++ b/paper/sections/03-model.tex
@@ -79,19 +79,24 @@ improves the position accuracy.
The GPS framework includes (circa) 30 satellites. These satellites
orbit around the Earth following known trajectories. While orbiting,
they broadcast periodic signals that encode a set of parameters,
-called \emph{ephemeris data}. The emphemeris data describe the
-satellites' orbits (see for example the trajectory of satelite $s_3$
+called \emph{ephemeris data}. The ephemeris data describe the
+satellites' orbits (see for example the trajectory of satellite $s_3$
in Figure~\ref{fig:globe}), and therefore allow the GPS receiver to
accurately determine their position in time. The satellite
trajectories are not constant in time, due to uncertainties and
disturbances, like corrections for collision avoidance.
+The hypothesis that the clocks of the receiver and the satellites are
+synchronized is not valid, so one extra satellite must be tracked and
+used for the trilateration procedure. The fourth satellite allows the
+receiver to compensate its time reference offset.
+
The ephemeris data expire after 30 minutes, i.e., after 30 minutes
they are not considered valid anymore. To correctly estimate the
current position, the receiver should ensure that the ephemeris data
are frequently updated. The transmission of the ephemeris data has a
duration of 30 seconds, and the satellites continuously broadcast new
-data. In order to ensure the correct aquisition of one data point, the
+data. In order to ensure the correct acquisition of one data point, the
receiver then has to fetch and decode the signal for a time that is in
the interval $[30,60)$ seconds (in the worst case, the receiver is
turned on right after the start of a new message transmission).
@@ -99,9 +104,9 @@ turned on right after the start of a new message transmission).
All the satellites transmit on the same frequency and then the
different signals are multiplexed using the Code Division Multiple
Access (CDMA) technique. Using CDMA, the signal has three components:
-(i) the carrier wave, (ii) the data waveform, and (iii) a spreding
+(i) the carrier wave, (ii) the data waveform, and (iii) a spreading
waveform. The spreading waveform is a deterministic signal, different
-for each satellite\footnote{Each satellite's spreding waveform is
+for each satellite\footnote{Each satellite's spreading waveform is
unique and has been chosen to be a \emph{golden code}, i.e., it
looks like white noise and it does not correlate with any other
satellite sequence.}, transmitted at much higher frequency with
@@ -113,7 +118,7 @@ between the satellite and the receiver. Assuming the satellite and the
receiver share a time reference, the receiver can measure the delay it
takes to receive the signal from the satellite. In
Figure~\ref{fig:globe} the delay to receive the message from satellite
-$s_1$ is denoted by $\Delta_1$. Multiplying this quantitiy by the
+$s_1$ is denoted by $\Delta_1$. Multiplying this quantity by the
speed of light $C$, the receiver can determine how far the signal has
been travelling, i.e., the distance from the satellite, indicated in
Figure~\ref{fig:globe} with $d_1$. For a generic satellite $x$, this
@@ -121,11 +126,6 @@ can be written as $d_{x} = \Delta_{x} \cdot C$. The set of the
distances the receiver measures from the visible satellites is called
\emph{ranging data}.
-The hypothesis that the clocks of the receiver and the satellites are
-synchronized is not valid, so one extra satellite must be tracked and
-used for the trilateration procedure. The fourth satellite allows the
-receiver to compensate its time reference offset.
-
Due to the satellites' and the receiver's movements, the doppler
effect will distort the signal reception. The effect is a shift in the
frequency spectrum of the signal. To fetch the signal, the receiver
@@ -134,8 +134,8 @@ shift. As a consequence, the process of fetching the signal directly
includes the measurement of the ranging data\footnote{Additionally,
the receiver can estimate its own speed from the measured doppler
effect and compensate for the satellite speed retrieved with the
- ephemeris data.}. This process takes some milleseconds (usually in
-the range from $2ms$ to $10ms$), depending mainly on signal stength.
+ ephemeris data.}. This process takes some milliseconds (usually in
+the range from $2ms$ to $10ms$), depending mainly on signal strength.
\subsection{GPS modeling}
\label{sec:gps:mod}
@@ -235,13 +235,19 @@ font=\footnotesize]
The GPS receiver provides a position estimate when it has collected
both the ephemeris and the ranging data for at least 4 satellites.
-Detecing data from more than 4 satellites improves the positioning
+Detecting data from more than 4 satellites improves the positioning
accuracy. As for power consumption, the receiver always consumes a
(negligible) idle power. On top of that, the sensor consumes
additional power when its radio is turned on, which is precisely the
cause of battery draining. This power has been experimentally shown to
be constant in time~\cite{bib:enloc-smartphones, bib:microsoft-leap}
+<<<<<<< HEAD
and usually around 400mW\footnote{This quantity apparently depends on the specific device. It can of course be changed according to the given use-case. Moreover for the simple evaluation of the trade-off it is not strictly relevant since we would be interested in the differential values. If instead is required an absolute estimation of the consumed energy, then a precise evaluation of this quantity is required.}. Our model needs to capture whether:
+=======
+and usually between 20mW and 400mW. We use the latter for our model,
+but this is just a constant that can be changed depending on the
+device. The important states that our model needs to capture are:
+>>>>>>> ee578b1defc31eed95f6615dbf639fb706f295b5
\begin{enumerate}
\item \emph{ephemeris data} are available or not;
\item \emph{ranging data} are available or not;
@@ -274,9 +280,9 @@ consecutively to the satellites' signal for long enough (transition
\texttt{get\_ephemeris}). The loss of availability happens either at
the expiration of the ephemeris data, or when the tracked satellites
disappear from the visible sky. In theory, the second event does not
-necessarly force an update of the ephemeris data. For instance, a
-satellite may disapper for a limited time interval and appear again
-before its ephemeris data's expiration. For simplicity (and without
+necessarily force an update of the ephemeris data. For instance, a
+satellite may disappear for a limited time interval and appear again
+before its ephemeris data expiration. For simplicity (and without
loss of information with respect to our model usage) we do not include
the specific tracking of different satellites in the model and,
consequently, we do not distinguish between these two cases. The
@@ -328,7 +334,7 @@ rectangle encloses the states in which the ranging data are available.
The yellow polygon shows the states in which the ephemeris data are
available.
-In general, each state accepts only the transitions that alterate the
+In general, each state accepts only the transitions that change the
properties that define it. For instance, if the antenna is off (states
No Info \textcircled{\scriptsize 1} and Warm Start Available
\textcircled{\scriptsize 6}), the automaton can accept the transition
@@ -343,7 +349,8 @@ Finally, two (discrete) equations complement the model dynamics. They
saturate the number of tracked satellites (both in terms of ephemeris
and ranging data) to the number of visible satellites, implementing
the fact that the receiver can only track visible satellites. These
-are, repsectively, \texttt{sat\_tracked\_ephemeris :=
+are, respectively, \texttt{sat\_tracked\_ephemeris :=
min\{sat\_tracked\_ephemeris, visible\_satellites\}} and
\texttt{sat\_tracked\_freq\&phase := min\{sat\_tracked\_freq\&phase,
visible\_satellites\}}.
+
diff --git a/paper/sections/04-fusion.tex b/paper/sections/04-fusion.tex
index 0a68af423d651e7b541f91874c91a4dc4f31ddc0..567c870bd1ed5ff7a9fdeb65b58f5ec10b0ae402 100644
--- a/paper/sections/04-fusion.tex
+++ b/paper/sections/04-fusion.tex
@@ -28,7 +28,7 @@ cubically in time. IMUs are therefore considered reliable only for
short time intervals. The idea of \emph{GPS-IMU} sensor fusion
algorithm is to exploit the IMUs to provide continuous low-power
positioning. At the same time, the GPS can be sampled at lower rate to
-compensate for the low frequency erors.
+compensate for the low frequency errors.
The GPS model devised in this paper captures the delays introduced by
the GPS receiver and its power consumption. This allows us to
@@ -54,11 +54,11 @@ at time $k$ and measure it in meters with respect to a reference
point, i.e., $p(k) \in \mathbb{R}^3 [m]$. We use $v(k)$ to indicate
the velocity at time $k$ measuring it in meters per seconds, i.e.,
$v(k) \in \mathbb{R}^3 [m/s]$. Finally, we denote with $q(k)$ the
-attitude at time $k$, using its (adimensional) quaternion
+attitude at time $k$, using its (dimensionless) quaternion
representation, i.e., $q(k)\in \mathbb{R}^4 [-]$.
The IMU provides the following measurements at time $k$: the
-acceleration and the angular rates. Both the accelaration $s(k)$ and
+acceleration and the angular rates. Both the acceleration $s(k)$ and
the angular rates are provided along the three axis, i.e.,
$s(k) \in \mathbb{R}^3 [m/s^2]$, and
$\omega(k)\in \mathbb{R}^3 [rad/s]$. Equation~\eqref{eq:integration}
@@ -134,14 +134,14 @@ affected by an additive white noise $e(k)$ with covariance $R$.
\tilde{p}_{\text{GPS}}(k) = p(k) + e(k)
\end{equation}
-The Kalman filter measures the discrepancy betwen the output of the
+The Kalman filter measures the discrepancy between the output of the
INS and the GPS and uses it to correct the position and biases
estimation. The correction is based on the \emph{Kalman gain},
computed online. This technique was proved to be optimal in case of
-gaussian disturbances and noises. In fact, the Kalman gain is computed
+Gaussian disturbances and noises. In fact, the Kalman gain is computed
according to the uncertainty of the quantities to be estimated,
formally defined as their covariance matrix, usually denoted with
-$\mathbf{P}$. The convariance matrix is also updated
+$\mathbf{P}$. The covariance matrix is also updated
online\footnote{If the system was linear, the covariance matrix would
be updated at every step with the same system dynamics and
covariances of disturbances and noises. This would apparently make
@@ -153,7 +153,7 @@ based on the linearized model of the system dynamics (i.e., how the
\emph{past} estimation uncertainty propagates to the following one),
and on the disturbances and noise models (i.e., their variance).
-We now provide details about how the kalman filter is implemented, and
+We now provide details about how the Kalman filter is implemented, and
how it corrects the estimates. We would like to obtain an estimate
$\hat{x}(k)$ of $x(k)$\footnote{In general, we use $\hat{\cdot}$ to
indicate the estimate of the variable represented by $\cdot$.}. We
@@ -176,9 +176,9 @@ v(k)-\hat{v}(k) \\
\epsilon (k)
\end{bmatrix}.
\end{equation}
-Here, the attitude error vector $\epsilon(k)$ is defined as the the
-small (Euler) angle sequence that rotates the attitude vector
-$\hat{q}(k)$ into
+Here, the attitude error vector $\epsilon(k) \in \mathbb{R}^3$ is
+defined as the the small (Euler) angle sequence that rotates the
+attitude vector $\hat{q}(k)$ into
$q(k)$\footnote{$q(k) = \Gamma(\hat{q}(k), \epsilon(k))$, where
$\Gamma(\hat{q}(k),\epsilon (k)) \triangleq \{ q \in \mathbb{S}^3 |
R_b^n (q(k)) = (I_3 - \left[\epsilon(k)\right]_{\times}) R_b^n
@@ -315,6 +315,3 @@ specified in Section~\ref{sec:gps}, we can now describe the dynamical
limitations that the GPS sensor imposes and the opportunities
available for optimizing its sampling.
-
-
-
diff --git a/paper/sections/05-control.tex b/paper/sections/05-control.tex
index fce3630dc06154a63090e72070607ef2ba747027..8c3043e75518e96a453088fc2714aebc3aa4be4a 100644
--- a/paper/sections/05-control.tex
+++ b/paper/sections/05-control.tex
@@ -46,19 +46,19 @@ data. The two occur at very different time scales, both in terms of
acquisition time and in terms of data validity.
\textbf{Ephemeris data:} The ephemeris data live in the time scale of
-\emph{minutes}, requiring betwen 30 and 59 seconds to be aquired and
-having a validity of 30 minutes from the aquisition. There are two
-implications of these facts. First, this induces a startup delay
-equivalent to the aquisition time of the ephemeris data. This is
+\emph{minutes}, requiring between 30 and 59 seconds to be acquired and
+having a validity of 30 minutes from the acquisition. There are two
+implications of these facts. First, this induces a start-up delay
+equivalent to the acquisition time of the ephemeris data. This is
referred to as \emph{Time To First Fix} (TTFF). Second, an effective
sampling strategy refreshes the ephemeris data at least every 30
minutes. This requires the antenna to be turned on for enough time to
capture the data and affects the sensor battery consumption.
-\textbf{Ranging data:} The ranging data are caracterized by a time
+\textbf{Ranging data:} The ranging data are characterized by a time
scale of the order of milliseconds. They require from 2 to 10
milliseconds to be acquired. This could be critical for real-time
-applications. The data validitiy is instantaneous, since they are used
+applications. The data validity is instantaneous, since they are used
as soon as they are received to compute the current position (and
moving will invalidate them). The time scale allows us to derive a
bound in the sensor sampling period. Sampling as frequently as the
@@ -75,7 +75,7 @@ information.
\subsection{Sampling Strategy Controller}
-With these considerations in mind, we designed a sampling stategy that
+With these considerations in mind, we designed a sampling strategy that
aims at keeping the ephemeris data updated, and sampling the GPS
sensor according to the uncertainty of the position estimation (in the
Kalman filter). We use the trace of the covariance matrix $P$, which
@@ -84,7 +84,6 @@ overcomes a predefined threshold $th$, we consider the position too
uncertain and request the GPS intervention. This is formally encoded
in the state machine shown in Figure~\ref{fig:controller}.
-
The logical controller sends a \textcolor{red}{\texttt{turn\_on}} signal when the system is starting, to collect the ephemeris data (State \textcircled{\scriptsize 2} in Figure~\ref{fig:controller}). Then, once the ephemeris data are available (which is defined by the very same transition of the sensor model), it starts cycling between states \textcircled{\scriptsize 3} and \textcircled{\scriptsize 4}, alternatively triggering the \textcolor{red}{\texttt{turn\_off}} and \textcolor{red}{\texttt{turn\_on}} signals. For readability, and consistently with the sensor model shown in Figure~\ref{fig:cyberDynamics}, the states in which the antenna is turned on are filled in green.
When the ephemeris data are about to expire (intuitively defined as $time>expiry\_time\_ephemeris-60$), or the sensor loses visibility of the tracked satellites, the controller goes back to State \textcircled{\scriptsize 2} and keeps the antenna on, to refresh the ephemeris data. If the ephemeris data are valid (and about to expire) the sensor can actually still be sampled, represented by taking the transition \texttt{sensor in position\_avaialable}.
diff --git a/paper/sections/06-results.tex b/paper/sections/06-results.tex
index 285a9e76e2c5a0399e8a240553f5673654b49271..55f085ad138eae98794d691bd4f8e9f341f739f3 100644
--- a/paper/sections/06-results.tex
+++ b/paper/sections/06-results.tex
@@ -42,7 +42,7 @@ tool. The nature of Modelica -- in terms of composability and
object-orientation -- makes the implementation easy to embed. We use
the Modelica implementation to show how the model captures the
relevant dynamics of the GPS receivers, comparing the results obtained
-with data obtained on real hardware. The implementation in Matlab ise
+with data obtained on real hardware. The implementation in Matlab is
used for data analysis of real GPS traces and in combination with
sensor fusion algorithm as described in Section~\ref{sec:fusion}, to
experiment with the accuracy-battery trade-off.
@@ -143,7 +143,7 @@ satellites\footnote{The model presented in this paper can be adapted
triggers the transition \texttt{get\_ephemeris} before the delay
that instead represents the action of listening to the
satellites. The modeling of how much time the device requires for
- fetching these informations from the internet and how much power
+ fetching this information from the internet and how much power
this procedure can take is non trivial because of the many different
(and often difficult to predict) involved components.}.
@@ -201,6 +201,7 @@ independently capture the tracking of individual satellites.
\label{sec:res:accuracy}
\begin{figure*}[t]
+\centering
\begin{minipage}{0.95\columnwidth}
\begin{tikzpicture}
\begin{axis}[%
@@ -279,7 +280,8 @@ independently capture the tracking of individual satellites.
\end{minipage}
\end{figure*}
\begin{figure*}
-\hspace{2mm}
+\centering
+\hspace{2mm} \vspace{0.1cm}
\begin{minipage}{0.95\columnwidth}
\begin{tikzpicture}
\begin{axis}[%
@@ -400,7 +402,7 @@ the biking scenario, we run 30 simulations with different triggering
thresholds for the sensor fusion algorithm (specifically, we use
$th \in \{ 4, 5, 6, 8, 10, 12 \}$). In principle, higher values of
the threshold guarantee a lower power consumption, at the price of
-larger position estimate errors (and viceversa).
+larger position estimate errors (and \emph{viceversa}).
The acquisition of satellites signals is modeled as a random variable,
uniformly distributed in the intervals specified for the model. In the
@@ -415,6 +417,7 @@ signal. We also normalize (removing the minimum number encountered in
the simulations), to highlight the trade-off.
\begin{figure*}
+\centering
\begin{minipage}{0.95\columnwidth}
\begin{tikzpicture}
\begin{axis}[%
@@ -426,9 +429,11 @@ the simulations), to highlight the trade-off.
scaled y ticks = false,
y tick label style={/pgf/number format/fixed},
xlabel = {Energy [mJ]},
- ylabel = {Normalized Error},
+ ylabel = {Error},
legend style={at={(1.3,1.1)},anchor=south},
legend columns=3,
+ ytick = {0, 0.01, 0.02, 0.03},
+ yticklabels = {45493.25, 45493.26, 45493.27, 45493.28},
]
\pgfkeys{/pgf/number format/.cd,1000 sep={}}
\addplot[thick, only marks, mark=*, blue]
@@ -457,13 +462,12 @@ the simulations), to highlight the trade-off.
\addlegendentry{Sensor Fusion $th=12$}
\end{axis}
\end{tikzpicture}
-\caption{Trade-Off between Energy and Error when cycling. The error
-is normalized subtracting its minimum value.}
+\caption{Trade-Off between Energy and Error when cycling.}
\label{fig:bike-tradeoff}
\end{minipage}
-\hspace{1mm}
+\hfill
\begin{minipage}{0.95\columnwidth}
- \vspace{1.5cm}
+\vspace{1.25cm}
\begin{tikzpicture}
\begin{axis}[%
height = 0.6\textwidth,
@@ -473,7 +477,7 @@ is normalized subtracting its minimum value.}
scaled x ticks = false,
scaled y ticks = false,
xlabel = {Energy [mJ]},
- ylabel = {Normalized Error},
+ ylabel = {Error},
]
\pgfkeys{/pgf/number format/.cd,1000 sep={}}
\addplot[thick, only marks, mark=*, blue]
@@ -497,8 +501,7 @@ is normalized subtracting its minimum value.}
\end{axis}
\end{tikzpicture}
\caption{Trade-Off between Energy and Error when driving
-(with signal loss). The error is normalized subtracting its minimum
-value.}
+(with signal loss).}
\label{fig:car-tradeoff}
\end{minipage}
\end{figure*}
@@ -518,9 +521,9 @@ Second, higher triggering values instead (purple, yellow, and orange
points) the opposite behavior is experienced. The variance in terms
of energy consumption is smaller, since the antenna is turned on less
frequently (implying that there will be less uncertainty on the time
-spent fetching satellites' signals). The error here becomes lager and
-with higher variance, due to the need to exploit more often (less
-reliable) IMU data.
+spent fetching signals). The error here becomes lager and with higher
+variance, due to the need to exploit more often (less reliable) IMU
+data.
Finally, Figure~\ref{fig:car-tradeoff} shows the simulations
performed using the car data. Given the introduction of a varying
@@ -532,3 +535,4 @@ affect both the accuracy (as the GPS data wont be available until a
sufficient number of satellites become visible again) and the energy
consumption (as the sensor will have to be turned on for relatively
long time to reacquire the ephemeris data).
+
diff --git a/paper/sections/07-concl.tex b/paper/sections/07-concl.tex
index 5261d1d654b8d659fc92037a9e24a00d47a20c90..a05a9d609c95b554f1a4f3df83629aa0310b99eb 100644
--- a/paper/sections/07-concl.tex
+++ b/paper/sections/07-concl.tex
@@ -1,6 +1,6 @@
This paper presented a first-principle model of a GPS receiver, able
to capture the dynamics of the data acquisition. We used the model to
-enhance a sensor fusion algorithm that merges data from intertial
+enhance a sensor fusion algorithm that merges data from inertial
measurement sensors with the GPS trace, to improve the GPS battery
consumption. The paper presented the model, simulation results, and
experiments obtained with real GPS traces, and exposes the trade-off