slide

Trading Off Accuracy, Timeliness, and Uplink Usage in Online GPS Tracking

1

ABM Musa, James Biagioni, Jakob Eriksson

slide 2Ride sharing

GPS tracking applications

slide 3

GPS tracking applications

UIC Shuttle tracker

slide

GPS tracking applications

4 Chicago bus tracker

slide

GPS tracking applications

5Map construction

slide 6

GPS tracking applications

Fleet tracking

slide

GPS tracking applications

7

Pet and gadget tracking

slide 8

cellular uplink

GPS tracking

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Status quo

9

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250

300

0 50 100 150 200 250 300

Cou

nt (m

illion

)

Time interval (seconds)

Figure 1: Histogram of intervals between ⇠1.6B lo-cation reports from 25 providers, illustrating the pe-riodic nature of contemporary GPS tracking.

is necessary, often in the form of a “geofence”: reports aresent only when the device is outside a set perimeter. How-ever, most local processing thus far is surprisingly simplistic.For example, many transit agencies report real-time bus lo-cations once every 10-30 seconds. Since most o↵-the-shelfGPS receivers produce locations at 1 Hz, the local process-ing consists of periodically sampling this stream of GPS fixesfor transmission. The taxis in [13] report every 16 or 61seconds. Finally, providers of “live GPS tracking” systemson the web advertise reporting intervals in the 5–15 secondrange. Other than geo-fencing and temporal subsampling,very little local processing is currently in use. Related workfrom the academic literature is reviewed in §2.2.

2.1 Field Study of Large-Scale GPS TrackingWe use a dataset consisting of 1.6 billion GPS points from

25 di↵erent data providers, over the period Aug 2010–Aug2012, to learn more about typical GPS tracking patterns.For privacy reasons, individual traces are split into short,de-identified and disconnected probes consisting of a smallernumber of GPS points, before we receive them. Despite this,we can gain an accurate statistical picture by studying thesampling behavior exhibited within each individual probe.Figure 1 shows a histogram of time intervals between sam-ples, across all probes. A clear pattern of periodic reportingemerges, with tell-tale peaks at periods 1, 5, 15, 30, 60, 90,120, 180, 240 and 300 seconds. After removing these clearlyperiodic samples, 11.4% remain.

To better understand the origin of these remaining sam-ples, we manually inspected several dozen representativetraces from the four data producers with the largest frac-tion of non-periodic transmissions. One appeared to be alogistics company, with frequent visits to loading docks, anda target period of 300 seconds. Here, a majority of non-periodic transmissions coincided with likely stops, CAN-busevents such as ignition on/o↵ events, etc. Note that a tracewith a period P , mixed with intermittent additional reportswith a mean interval at or below P , will appear non-periodicin Figure 1. For example, samples at times 0, 60, 120 havestrict 60-second inter-sample spacings, but adding intermit-tent samples at times 17 and 99 produce inter-sample spac-ings 17, 43, 39, 21, a seemingly non-periodic sequence. Forsome data providers, this was a frequent occurrence. Thus,

the 11.4% reported above is an over-estimate of the propor-tion of non-periodic transmissions.For another data provider, the target period was 15 sec-

onds, and probes consisted mostly of highway travel. How-ever, every 2–3 transmissions was delayed by between 1–14 seconds, with occasional packet losses mixed in. Curi-ously, all delayed samples contained up-to-date information:a sample delayed by 5 seconds included 5 seconds more driv-ing. Our best model of this particular tracking system saysthat the link is brought down automatically after 30–45 sec-onds of “inactivity”, where UDP tra�c is not counted asactivity. The recurring delays can then be explained by thetracking software repeatedly re-establishing the connectionbefore transmitting the most recent location—most likelya programming error. After removing these from the non-periodic set, we are left with 9.3% of non-periodic samples.Similar behaviors were observed for other data providers.

Throughout, we were unable to find any evidence of spatialsampling (every so many meters), speed or bearing change-based sampling, or even a policy as simple as “don’t samplewhen we’re not moving”: all providers show several back-to-back transmissions with identical locations. Thus, bothanecdotally and quantitatively speaking, we believe thereis ample room for improvement to the status quo in onlinetracking. Below, we review the academic literature on thetopic.

2.2 GPS Tracking Literature ReviewDue to the high power consumption of GPS receivers and

their popular use in mobile, energy-limited devices, manyresearchers have focused on improving the energy e�ciencyof GPS tracking [9, 1, 7, 18, 12, 24, 20, 10]. By contrast, weassume that power is plentiful, or that the GPS is alreadyactive for a primary application. GPS trace compression [5,8, 14, 23, 17, 1, 15] may be used to produce compact repre-sentations of a trace. While these techniques can be helpfulin reducing the size of each transmission, they cannot reducethe number of transmissions without sacrificing timeliness.While we make use of existing GPS compression tech-

niques, the focus of our work is online tracking, where for-warding decisions are made as GPS points become available.In [22, 6] constant-rate sampling is compared to more sophis-ticated methods: sampling at constant distance intervals,dead-reckoning and map-based dead reckoning. [17] addshistorical traces to predict future movements. Our workbuilds upon the above, providing a unified extrapolator thatpredicts future movements, and three di↵erent samplers thatautomatically provide the desired performance in two out ofthree parameters: accuracy, cost and timeliness.

3. MOBILE “DATA USAGE” BILLINGWhile reducing bandwidth consumption is a worthy goal

on its own, the incentive behind such an e↵ort is often mon-etary. For cellular data service, wireless operators typicallyemploy tiered pricing plans, which impose various monthly“data usage” limits for fixed monthly fees. For businessagreements with a large number of tracking devices, theremay instead be a per-byte charge for the aggregate amountof data usage across all devices.In either case, the semantics of “data usage” are unclear.

For example, the publicly available information from AT&Tmakes no mention of what parts of a packet are countedagainst the monthly allowance. Does it include transport

c�UNIVERSITY OF ILLINOIS AT CHICAGO, TECHNICAL REPORT, JULY 2013

Observation from 1.6 billion GPS points

slide

Status quo

9

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Cou

nt (m

illion

)

Time interval (seconds)

Figure 1: Histogram of intervals between ⇠1.6B lo-cation reports from 25 providers, illustrating the pe-riodic nature of contemporary GPS tracking.

is necessary, often in the form of a “geofence”: reports aresent only when the device is outside a set perimeter. How-ever, most local processing thus far is surprisingly simplistic.For example, many transit agencies report real-time bus lo-cations once every 10-30 seconds. Since most o↵-the-shelfGPS receivers produce locations at 1 Hz, the local process-ing consists of periodically sampling this stream of GPS fixesfor transmission. The taxis in [13] report every 16 or 61seconds. Finally, providers of “live GPS tracking” systemson the web advertise reporting intervals in the 5–15 secondrange. Other than geo-fencing and temporal subsampling,very little local processing is currently in use. Related workfrom the academic literature is reviewed in §2.2.

2.1 Field Study of Large-Scale GPS TrackingWe use a dataset consisting of 1.6 billion GPS points from

25 di↵erent data providers, over the period Aug 2010–Aug2012, to learn more about typical GPS tracking patterns.For privacy reasons, individual traces are split into short,de-identified and disconnected probes consisting of a smallernumber of GPS points, before we receive them. Despite this,we can gain an accurate statistical picture by studying thesampling behavior exhibited within each individual probe.Figure 1 shows a histogram of time intervals between sam-ples, across all probes. A clear pattern of periodic reportingemerges, with tell-tale peaks at periods 1, 5, 15, 30, 60, 90,120, 180, 240 and 300 seconds. After removing these clearlyperiodic samples, 11.4% remain.

To better understand the origin of these remaining sam-ples, we manually inspected several dozen representativetraces from the four data producers with the largest frac-tion of non-periodic transmissions. One appeared to be alogistics company, with frequent visits to loading docks, anda target period of 300 seconds. Here, a majority of non-periodic transmissions coincided with likely stops, CAN-busevents such as ignition on/o↵ events, etc. Note that a tracewith a period P , mixed with intermittent additional reportswith a mean interval at or below P , will appear non-periodicin Figure 1. For example, samples at times 0, 60, 120 havestrict 60-second inter-sample spacings, but adding intermit-tent samples at times 17 and 99 produce inter-sample spac-ings 17, 43, 39, 21, a seemingly non-periodic sequence. Forsome data providers, this was a frequent occurrence. Thus,

the 11.4% reported above is an over-estimate of the propor-tion of non-periodic transmissions.For another data provider, the target period was 15 sec-

onds, and probes consisted mostly of highway travel. How-ever, every 2–3 transmissions was delayed by between 1–14 seconds, with occasional packet losses mixed in. Curi-ously, all delayed samples contained up-to-date information:a sample delayed by 5 seconds included 5 seconds more driv-ing. Our best model of this particular tracking system saysthat the link is brought down automatically after 30–45 sec-onds of “inactivity”, where UDP tra�c is not counted asactivity. The recurring delays can then be explained by thetracking software repeatedly re-establishing the connectionbefore transmitting the most recent location—most likelya programming error. After removing these from the non-periodic set, we are left with 9.3% of non-periodic samples.Similar behaviors were observed for other data providers.

Throughout, we were unable to find any evidence of spatialsampling (every so many meters), speed or bearing change-based sampling, or even a policy as simple as “don’t samplewhen we’re not moving”: all providers show several back-to-back transmissions with identical locations. Thus, bothanecdotally and quantitatively speaking, we believe thereis ample room for improvement to the status quo in onlinetracking. Below, we review the academic literature on thetopic.

2.2 GPS Tracking Literature ReviewDue to the high power consumption of GPS receivers and

their popular use in mobile, energy-limited devices, manyresearchers have focused on improving the energy e�ciencyof GPS tracking [9, 1, 7, 18, 12, 24, 20, 10]. By contrast, weassume that power is plentiful, or that the GPS is alreadyactive for a primary application. GPS trace compression [5,8, 14, 23, 17, 1, 15] may be used to produce compact repre-sentations of a trace. While these techniques can be helpfulin reducing the size of each transmission, they cannot reducethe number of transmissions without sacrificing timeliness.While we make use of existing GPS compression tech-

niques, the focus of our work is online tracking, where for-warding decisions are made as GPS points become available.In [22, 6] constant-rate sampling is compared to more sophis-ticated methods: sampling at constant distance intervals,dead-reckoning and map-based dead reckoning. [17] addshistorical traces to predict future movements. Our workbuilds upon the above, providing a unified extrapolator thatpredicts future movements, and three di↵erent samplers thatautomatically provide the desired performance in two out ofthree parameters: accuracy, cost and timeliness.

3. MOBILE “DATA USAGE” BILLINGWhile reducing bandwidth consumption is a worthy goal

on its own, the incentive behind such an e↵ort is often mon-etary. For cellular data service, wireless operators typicallyemploy tiered pricing plans, which impose various monthly“data usage” limits for fixed monthly fees. For businessagreements with a large number of tracking devices, theremay instead be a per-byte charge for the aggregate amountof data usage across all devices.In either case, the semantics of “data usage” are unclear.

For example, the publicly available information from AT&Tmakes no mention of what parts of a packet are countedagainst the monthly allowance. Does it include transport

c�UNIVERSITY OF ILLINOIS AT CHICAGO, TECHNICAL REPORT, JULY 2013

Observation from 1.6 billion GPS points

5

15

60

90

120

slide

Status quo

9

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Cou

nt (m

illion

)

Time interval (seconds)

Figure 1: Histogram of intervals between ⇠1.6B lo-cation reports from 25 providers, illustrating the pe-riodic nature of contemporary GPS tracking.

is necessary, often in the form of a “geofence”: reports aresent only when the device is outside a set perimeter. How-ever, most local processing thus far is surprisingly simplistic.For example, many transit agencies report real-time bus lo-cations once every 10-30 seconds. Since most o↵-the-shelfGPS receivers produce locations at 1 Hz, the local process-ing consists of periodically sampling this stream of GPS fixesfor transmission. The taxis in [13] report every 16 or 61seconds. Finally, providers of “live GPS tracking” systemson the web advertise reporting intervals in the 5–15 secondrange. Other than geo-fencing and temporal subsampling,very little local processing is currently in use. Related workfrom the academic literature is reviewed in §2.2.

2.1 Field Study of Large-Scale GPS TrackingWe use a dataset consisting of 1.6 billion GPS points from

25 di↵erent data providers, over the period Aug 2010–Aug2012, to learn more about typical GPS tracking patterns.For privacy reasons, individual traces are split into short,de-identified and disconnected probes consisting of a smallernumber of GPS points, before we receive them. Despite this,we can gain an accurate statistical picture by studying thesampling behavior exhibited within each individual probe.Figure 1 shows a histogram of time intervals between sam-ples, across all probes. A clear pattern of periodic reportingemerges, with tell-tale peaks at periods 1, 5, 15, 30, 60, 90,120, 180, 240 and 300 seconds. After removing these clearlyperiodic samples, 11.4% remain.

To better understand the origin of these remaining sam-ples, we manually inspected several dozen representativetraces from the four data producers with the largest frac-tion of non-periodic transmissions. One appeared to be alogistics company, with frequent visits to loading docks, anda target period of 300 seconds. Here, a majority of non-periodic transmissions coincided with likely stops, CAN-busevents such as ignition on/o↵ events, etc. Note that a tracewith a period P , mixed with intermittent additional reportswith a mean interval at or below P , will appear non-periodicin Figure 1. For example, samples at times 0, 60, 120 havestrict 60-second inter-sample spacings, but adding intermit-tent samples at times 17 and 99 produce inter-sample spac-ings 17, 43, 39, 21, a seemingly non-periodic sequence. Forsome data providers, this was a frequent occurrence. Thus,

the 11.4% reported above is an over-estimate of the propor-tion of non-periodic transmissions.For another data provider, the target period was 15 sec-

onds, and probes consisted mostly of highway travel. How-ever, every 2–3 transmissions was delayed by between 1–14 seconds, with occasional packet losses mixed in. Curi-ously, all delayed samples contained up-to-date information:a sample delayed by 5 seconds included 5 seconds more driv-ing. Our best model of this particular tracking system saysthat the link is brought down automatically after 30–45 sec-onds of “inactivity”, where UDP tra�c is not counted asactivity. The recurring delays can then be explained by thetracking software repeatedly re-establishing the connectionbefore transmitting the most recent location—most likelya programming error. After removing these from the non-periodic set, we are left with 9.3% of non-periodic samples.Similar behaviors were observed for other data providers.

Throughout, we were unable to find any evidence of spatialsampling (every so many meters), speed or bearing change-based sampling, or even a policy as simple as “don’t samplewhen we’re not moving”: all providers show several back-to-back transmissions with identical locations. Thus, bothanecdotally and quantitatively speaking, we believe thereis ample room for improvement to the status quo in onlinetracking. Below, we review the academic literature on thetopic.

2.2 GPS Tracking Literature ReviewDue to the high power consumption of GPS receivers and

their popular use in mobile, energy-limited devices, manyresearchers have focused on improving the energy e�ciencyof GPS tracking [9, 1, 7, 18, 12, 24, 20, 10]. By contrast, weassume that power is plentiful, or that the GPS is alreadyactive for a primary application. GPS trace compression [5,8, 14, 23, 17, 1, 15] may be used to produce compact repre-sentations of a trace. While these techniques can be helpfulin reducing the size of each transmission, they cannot reducethe number of transmissions without sacrificing timeliness.While we make use of existing GPS compression tech-

niques, the focus of our work is online tracking, where for-warding decisions are made as GPS points become available.In [22, 6] constant-rate sampling is compared to more sophis-ticated methods: sampling at constant distance intervals,dead-reckoning and map-based dead reckoning. [17] addshistorical traces to predict future movements. Our workbuilds upon the above, providing a unified extrapolator thatpredicts future movements, and three di↵erent samplers thatautomatically provide the desired performance in two out ofthree parameters: accuracy, cost and timeliness.

3. MOBILE “DATA USAGE” BILLINGWhile reducing bandwidth consumption is a worthy goal

on its own, the incentive behind such an e↵ort is often mon-etary. For cellular data service, wireless operators typicallyemploy tiered pricing plans, which impose various monthly“data usage” limits for fixed monthly fees. For businessagreements with a large number of tracking devices, theremay instead be a per-byte charge for the aggregate amountof data usage across all devices.In either case, the semantics of “data usage” are unclear.

For example, the publicly available information from AT&Tmakes no mention of what parts of a packet are countedagainst the monthly allowance. Does it include transport

c�UNIVERSITY OF ILLINOIS AT CHICAGO, TECHNICAL REPORT, JULY 2013

Observation from 1.6 billion GPS points

5

15

60

90

120

slide

Status quo

9

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Cou

nt (m

illion

)

Time interval (seconds)

Figure 1: Histogram of intervals between ⇠1.6B lo-cation reports from 25 providers, illustrating the pe-riodic nature of contemporary GPS tracking.

is necessary, often in the form of a “geofence”: reports aresent only when the device is outside a set perimeter. How-ever, most local processing thus far is surprisingly simplistic.For example, many transit agencies report real-time bus lo-cations once every 10-30 seconds. Since most o↵-the-shelfGPS receivers produce locations at 1 Hz, the local process-ing consists of periodically sampling this stream of GPS fixesfor transmission. The taxis in [13] report every 16 or 61seconds. Finally, providers of “live GPS tracking” systemson the web advertise reporting intervals in the 5–15 secondrange. Other than geo-fencing and temporal subsampling,very little local processing is currently in use. Related workfrom the academic literature is reviewed in §2.2.

2.1 Field Study of Large-Scale GPS TrackingWe use a dataset consisting of 1.6 billion GPS points from

25 di↵erent data providers, over the period Aug 2010–Aug2012, to learn more about typical GPS tracking patterns.For privacy reasons, individual traces are split into short,de-identified and disconnected probes consisting of a smallernumber of GPS points, before we receive them. Despite this,we can gain an accurate statistical picture by studying thesampling behavior exhibited within each individual probe.Figure 1 shows a histogram of time intervals between sam-ples, across all probes. A clear pattern of periodic reportingemerges, with tell-tale peaks at periods 1, 5, 15, 30, 60, 90,120, 180, 240 and 300 seconds. After removing these clearlyperiodic samples, 11.4% remain.

To better understand the origin of these remaining sam-ples, we manually inspected several dozen representativetraces from the four data producers with the largest frac-tion of non-periodic transmissions. One appeared to be alogistics company, with frequent visits to loading docks, anda target period of 300 seconds. Here, a majority of non-periodic transmissions coincided with likely stops, CAN-busevents such as ignition on/o↵ events, etc. Note that a tracewith a period P , mixed with intermittent additional reportswith a mean interval at or below P , will appear non-periodicin Figure 1. For example, samples at times 0, 60, 120 havestrict 60-second inter-sample spacings, but adding intermit-tent samples at times 17 and 99 produce inter-sample spac-ings 17, 43, 39, 21, a seemingly non-periodic sequence. Forsome data providers, this was a frequent occurrence. Thus,

the 11.4% reported above is an over-estimate of the propor-tion of non-periodic transmissions.For another data provider, the target period was 15 sec-

onds, and probes consisted mostly of highway travel. How-ever, every 2–3 transmissions was delayed by between 1–14 seconds, with occasional packet losses mixed in. Curi-ously, all delayed samples contained up-to-date information:a sample delayed by 5 seconds included 5 seconds more driv-ing. Our best model of this particular tracking system saysthat the link is brought down automatically after 30–45 sec-onds of “inactivity”, where UDP tra�c is not counted asactivity. The recurring delays can then be explained by thetracking software repeatedly re-establishing the connectionbefore transmitting the most recent location—most likelya programming error. After removing these from the non-periodic set, we are left with 9.3% of non-periodic samples.Similar behaviors were observed for other data providers.

Throughout, we were unable to find any evidence of spatialsampling (every so many meters), speed or bearing change-based sampling, or even a policy as simple as “don’t samplewhen we’re not moving”: all providers show several back-to-back transmissions with identical locations. Thus, bothanecdotally and quantitatively speaking, we believe thereis ample room for improvement to the status quo in onlinetracking. Below, we review the academic literature on thetopic.

2.2 GPS Tracking Literature ReviewDue to the high power consumption of GPS receivers and

their popular use in mobile, energy-limited devices, manyresearchers have focused on improving the energy e�ciencyof GPS tracking [9, 1, 7, 18, 12, 24, 20, 10]. By contrast, weassume that power is plentiful, or that the GPS is alreadyactive for a primary application. GPS trace compression [5,8, 14, 23, 17, 1, 15] may be used to produce compact repre-sentations of a trace. While these techniques can be helpfulin reducing the size of each transmission, they cannot reducethe number of transmissions without sacrificing timeliness.While we make use of existing GPS compression tech-

niques, the focus of our work is online tracking, where for-warding decisions are made as GPS points become available.In [22, 6] constant-rate sampling is compared to more sophis-ticated methods: sampling at constant distance intervals,dead-reckoning and map-based dead reckoning. [17] addshistorical traces to predict future movements. Our workbuilds upon the above, providing a unified extrapolator thatpredicts future movements, and three di↵erent samplers thatautomatically provide the desired performance in two out ofthree parameters: accuracy, cost and timeliness.

3. MOBILE “DATA USAGE” BILLINGWhile reducing bandwidth consumption is a worthy goal

on its own, the incentive behind such an e↵ort is often mon-etary. For cellular data service, wireless operators typicallyemploy tiered pricing plans, which impose various monthly“data usage” limits for fixed monthly fees. For businessagreements with a large number of tracking devices, theremay instead be a per-byte charge for the aggregate amountof data usage across all devices.In either case, the semantics of “data usage” are unclear.

For example, the publicly available information from AT&Tmakes no mention of what parts of a packet are countedagainst the monthly allowance. Does it include transport

c�UNIVERSITY OF ILLINOIS AT CHICAGO, TECHNICAL REPORT, JULY 2013

Observation from 1.6 billion GPS points

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Cellular data usage

10

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Key problem

11

slide

Key problem

11

slide

Key problem

11

slide

Key problem

11

slide

Key problem

11

slide

Key problem

11

slide

Key problem

11

slide

Key problem

11

? ? ? ? ? ?

slide

Key problem

11

? ? ? ? ? ?Which coordinates to transmit?

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Key problem

11

? ? ? ? ? ?Which coordinates to transmit?

Sampler

slide

Naive samplers‣ Periodic sampling

- Predictable data-usage - No guarantee on error ‣ Sampling at uniform distance

- Predictable error - No guarantee on data-usage and timeliness

12

slide

Adaptive Sampling

13

slide

GPS Extrapolation‣ Predicts the future locations

14

slide

GPS Extrapolation‣ Predicts the future locations

14

slide

GPS Extrapolation‣ Predicts the future locations

14

slide

GPS Extrapolation‣ Predicts the future locations

14

slide

GPS Extrapolation‣ Predicts the future locations

14

slide

GPS Extrapolation‣ Predicts the future locations

14

slide

GPS Extrapolation‣ Predicts the future locations

14

Actual GPS location Extrapolated location

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GPS Extrapolation

15

‣ Constant Location ‣ Constant Velocity ‣ Constant Acceleration ‣ Constant Deceleration ‣ Map based

slide

GPS Extrapolation

15

‣ Constant Location ‣ Constant Velocity ‣ Constant Acceleration ‣ Constant Deceleration ‣ Map based

Automatic Switching

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GPS Extrapolation

15

‣ Constant Location ‣ Constant Velocity ‣ Constant Acceleration ‣ Constant Deceleration ‣ Map based

Automatic Switching

‣ Unified

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Fixed delay in reporting

16

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

Fixed delay in reporting

16

5 seconds delay

Actual GPS location

slide

GPS compression/decompression

17

Actual GPS location

slide

GPS compression/decompression

17

Actual GPS location

slide

GPS compression/decompression

17

Actual GPS location

slide

GPS compression/decompression

17

Actual GPS location

slide

GPS compression/decompression

17

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the server

slide

GPS compression/decompression

17

Interpolation

Actual GPS locationActual GPS location transmitted to the serverInterpolated location

slide

Adaptive Sampling

18

slide

Adaptive Sampling

18

Data-usage

slide

Adaptive Sampling

18

ErrorData-usage

slide

Adaptive Sampling

18

ErrorData-usage Delay

slide

Adaptive Sampling

18

ErrorData-usage Delay

Error-delay Sampler

Max error (m)Fixed delay (s)

slide

Adaptive Sampling

18

ErrorData-usage Delay

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Adaptive Sampling

18

ErrorData-usage Delay

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

slide

Adaptive Sampling

18

ErrorData-usage Delay

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

slide

Adaptive Sampling

18

ErrorData-usage Delay

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

Error-budget Sampler

Max error (m)Budget (bytes/s)

slide

Adaptive Sampling

18

ErrorData-usage Delay

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

Error-budget Sampler

Max error (m)Budget (bytes/s)

Delay minimization

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Extrapolation

slide

System overview

19

Extrapolator

Sampler

Compressor

Extrapolator

Decompressor

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Interpolation

Extrapolation

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Error-delay sampler

20

Actual GPS location Actual GPS location transmitted to the server

Extrapolated location Interpolated location

Error-delay Sampler

Max error (m)Fixed delay (s)

Data usage minimization

slide

Datasets

21

OpenStreetMap: 12.4 million UIC shuttle: 1.9 million Microsoft: 500 thousands

slide

Error-delay sampler

22

0 10 20 30 40 50 60

0 20 40 60 80 100 120

Usa

ge (b

ytes

/sec

)

Maximum error (meters)

Straw manDelay=0sDelay=1s

Delay=8sDelay=16sDelay=32s

Delay=64sDelay=128s

Straw-man: Transmits at every error-bound meter

with constant-location extrapolator

slide

Error-delay sampler

22

0 10 20 30 40 50 60

0 20 40 60 80 100 120

Usa

ge (b

ytes

/sec

)

Maximum error (meters)

Straw manDelay=0sDelay=1s

Delay=8sDelay=16sDelay=32s

Delay=64sDelay=128s

Straw-man: Transmits at every error-bound meter

with constant-location extrapolator

slide

Error-delay sampler

22

0 10 20 30 40 50 60

0 20 40 60 80 100 120

Usa

ge (b

ytes

/sec

)

Maximum error (meters)

Straw manDelay=0sDelay=1s

Delay=8sDelay=16sDelay=32s

Delay=64sDelay=128s

Straw-man: Transmits at every error-bound meter

with constant-location extrapolator

slide

Error-delay sampler

22

0 10 20 30 40 50 60

0 20 40 60 80 100 120

Usa

ge (b

ytes

/sec

)

Maximum error (meters)

Straw manDelay=0sDelay=1s

Delay=8sDelay=16sDelay=32s

Delay=64sDelay=128s

Straw-man: Transmits at every error-bound meter

with constant-location extrapolator

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

extrapolation error = � ⇤ expected error

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

extrapolation error = � ⇤ expected error

long-term (based on historical statistics)

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

extrapolation error = � ⇤ expected error

long-term (based on historical statistics)

short-term (based on current balance)

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

extrapolation error = � ⇤ expected error

long-term (based on historical statistics)

short-term (based on current balance)

balance accumulation: Budget bytes every sec

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

extrapolation error = � ⇤ expected error

long-term (based on historical statistics)

short-term (based on current balance)

balance accumulation: Budget bytes every secbalance expenditure: cost of transmission

slide

Budget-delay sampler

23

Budget-delay Sampler

Budget (bytes/s)Fixed delay (s)

Error minimization

extrapolation error = � ⇤ expected error

long-term (based on historical statistics)

short-term (based on current balance)

�

0 0.5

1 1.5

2 2.5

3 3.5

4

-10000 -5000 0 5000 10000

Fact

or

Balance (bytes)

balance accumulation: Budget bytes every secbalance expenditure: cost of transmission

slide

Error-budget sampler

24

Error-budget Sampler

Max error (m)Budget (bytes/s)

Delay minimization

slide

Error-budget sampler

24

Mean budget (bytes/sec): long-term goal Max error bound (meters): every step

Error-budget Sampler

Max error (m)Budget (bytes/s)

Delay minimization

slide

Error-budget sampler

24

Mean budget (bytes/sec): long-term goal Max error bound (meters): every step

Error-budget Sampler

Max error (m)Budget (bytes/s)

Delay minimization

Combination of budget-delay and error-delay samplers

slide

End-to-end Results

25

slide

Error-delay sampler

26

10m error bound, 0s delay: 77% 10m error bound, 8s delay: 88% 10m error bound, 64s delay: 96%

Data usage reduction

With unified extrapolator

slide 27

2 bytes/sec budget, 0s delay: 81% 2 bytes/sec budget, 8s delay: 91% 2 bytes/sec budget, 64s delay: 99%

Error reduction

Budget-delay samplerWith unified extrapolator

slide

Unification of Samplers

28

slide

Unification of Samplers

28

slide 29

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*Error-delay

Budget-delayError-budget

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140U

sage

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

8s 32s 128s

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

Unification of SamplersUsage = A ⇤ 1

error

B ⇤ delayC +D

slide 29

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*Error-delay

Budget-delayError-budget

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140U

sage

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

8s 32s 128s

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

Unification of SamplersUsage = A ⇤ 1

error

B ⇤ delayC +D

slide 29

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*Error-delay

Budget-delayError-budget

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140U

sage

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

8s 32s 128s

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

Unification of SamplersUsage = A ⇤ 1

error

B ⇤ delayC +D

slide 29

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*Error-delay

Budget-delayError-budget

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140U

sage

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

8s 32s 128s

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

Unification of SamplersUsage = A ⇤ 1

error

B ⇤ delayC +D

slide 29

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*Error-delay

Budget-delayError-budget

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140U

sage

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

8s 32s 128s

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

Unification of SamplersUsage = A ⇤ 1

error

B ⇤ delayC +D

slide 29

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*Error-delay

Budget-delayError-budget

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140U

sage

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

8s 32s 128s

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usa

ge (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140Us

age

(byt

es/s

ec)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

0

1

2

3

4

20 40 60 80 100 120 140

Usag

e (b

ytes

/sec

)

Mean error (meters)

Fit, 8sFit, 32s

Fit, 128s

Usage, 8sUsage, 32s

Usage, 128s

Error, 8sError, 32s

Error, 128s

Delay, 8s*Delay, 32s*

Delay, 128s*

Unification of SamplersUsage = A ⇤ 1

error

B ⇤ delayC +D

slide

Thank you!

30

Questions?