A Two-Layer Approach for Energy Efficiency in Mobile Location - - PowerPoint PPT Presentation

A Two-Layer Approach for Energy Efficiency in Mobile Location Sensing Applications

Ling-Jyh Chen (Academia Sinica, Taiwan)

Introduction

• Mobile location sensing applications (MLSAs)
• Exploit Global Positioning System (GPS) technology to facilitate location-based services
• MLSA platforms are battery-powered and resource-constrained
• Tradeoff between information accuracy and energy efficiency
• Two-layer of MLSAs
• GPS tracking
• Data communication

Previous work (1)

• Energy efficiency on the MLSA GPS tracking
• Static duty-cycle (SDC)
• Turn GPS receivers ON and OFF at regular intervals
• Operate in a “blind” manner
• Dynamic duty-cycle (DDC)
• Adjust GPS duty cycles based on events triggered by additional sensors
• r analytical models

(ex: a lookup table of pre-learned radio patterns)

Previous work (2)

• Energy efficiency on the data communication
• Byte-level compression
• Compress MLSA data without considering its intrinsic properties
• Compressed data cannot be processed directly without decompression into

its raw form

• Spatio-temporal compression
• Achieve a good compression ratio at the expense of information loss

Our solution: Two-layer solution

• Energy efficiency on the MLSA GPS tracking:

Event-based GPS Tracking (EBT)

• Require minimal prerequisites for extra knowledge
• Energy efficiency on the data communication:

Inter-Frame Coding (IFC)

• Provide lossless compression and allow queries to operate on the compressed

data directly

• Simple, lightweight, and portable to off-the-shelf smart phones

Two-layer solution

• Event-based GPS Tracking (EBT)
• Inter-Frame Coding (IFC)

Event-based GPS Tracking (EBT)

EBT

Hybrid duty-cycle scheduling Location estimation

Event-based dynamic duty-cycle (DDC): Turn Detection Approach (TDA)

Static duty-cycle (SDC)

Uses G-sensor data to estimate location

EBT has two components

• Hybrid duty-cycle scheduling
• Static duty-cycle (SDC)
• Turn on GPS when GPS receiver has been in the OFF mode longer than a pre-

defined period (TDC)

• Event-based dynamic duty-cycle (DDC)
• Turn on GPS when detecting a significant change in the movement pattern
• Location estimation
• Uses the data of the G-sensor to estimate the location when GPS is off

Hybrid duty-cycle scheduling

Question (1) How to detect a turn?

• Turn Detection Approach (TDA): Significant changes in direction

result in significant changes in acceleration data

• Sliding standard deviation of acceleration in direction orthogonal to gravity and

trajectory’s direction (window size = w)

• Target Power Saving Ratio (α)
• If a sliding standard deviation is on the top (1 - α), there is a significant change in

acceleration data

• Set the sliding standard deviation on the top (1 - α) as turn detection threshold
• Poisson Sampling (rate = λ)
• Random view of acceleration distribution

Question (2) Queue management in TDA

• Infinite-Queue Approach (IQA)
• Provide a baseline, but infeasible
• FIFO Queue Approach(FQA)
• Finite queue length (L)
• Skewed view of acceleration distribution
• Dual-Queue Approach (DQA)

One is for standard deviations smaller than or equal to threshold (FIFO queue length = ⎡ α L⎤) The other one is for standard deviations greater than threshold (FIFO queue length = L - ⎡ α L⎤)

Location estimation: Estimate location when GPS is in the OFF mode

• Direction
• Obtained from the last successful

GPS lock

• Displacement
• Displacement measurement algorithm

(DMA) [6]

[6] T. Chen, W. Hu, and R. Sun. Displacement Measurement Algorithm Using Handheld Device with Accelerometer. In Asia-Pacific Conference on Wearable Computing Systems, 2010.

Two-layer solution

• Event-based GPS Tracking (EBT)
• Inter-Frame Coding (IFC)

Inter-Frame Coding (IFC)

• Spatial and temporal offsets are limited to
• Object’s mobility
• Trajectory’s sampling rate
• IFC exploits the spatial and temporal localities of contiguous spatio-temporal data to

reduce redundancy

Two types of data points in IFC

• I frame:

Index data points of a trajectory

• O frame:

Offsets of the subsequent data points

• An I frame is associated with n O frames
• n depends on sampling rate, speed, and

data compression ratio

Example of IFC (given n = 3)

• The second I frame is created because n O frames have been created for the first I frame.
• The third I frame is created because the longitude and latitude offsets exceed the maximum
• ffset value.

Upper bound of n

• Spatial and temporal offsets are limited to
• Maximum value of the latitude and longitude offsets = MAXdist
• Maximum value of the time offset = MAXtime
• Maximum possible speed = Vmax m/s
• Trajectory data collection rate = s data points/s

Compression ratio ψ

• The best compression ratio is (Size_O/Size_I) when n approaches infinity,

but very large n value is infeasible

• Computationally expensive when n is very large: Data query involves two separate

database queries

• Loss of a single I frame may result in the loss of the original data
• Cannot achieve the theoretical compression ratio: Subsequent n points have oversized
• ffset values that cannot be represented by O frames

Evaluation

• Evaluation of EBT
• Evaluation of IFC

Experimental setup

• 50 trips of the TPE-CMS bus system using the VProbe application
• Collect smart phone sensory data: GPS trajectories, digital compass directions, and 3-axis

accelerations

• Platform: Acer Liquid, HTC Magic, Samsung Nexus S, and Sony Ericsson XPERIA X10

phones

• Configuration
• Data sampling rate
• GPS: 1 Hz
• Digital compass: 20 Hz
• 3-axis acceleration: 20 Hz
• Results are based on the average performance of 10 simulation
• Static Duty Cycle (TDC) = 60

seconds

• Queue Length (L) = 1000 samples
• Window Size (w) = 50 samples

Evaluation (1) Feasibility of using digital compasses

• Dataset
• Trajectories of 86,607 seconds
• 235 turn events are marked manually as ground truth
• Results
• 795 turn events are reported by the digital compass
• 115 turn events are detected correctly: accuracy of turn event detection is 48.94%
• 680 events are false-alarms: false positive ratio is 85.53%
• Digital compasses are very sensitive to magnetic and electrical fields

Evaluation (2) Evaluation of TDA

• EBT’s hit rate is more than 98%

when the TDA scheme is used (i.e., α < 1)

• Hit rate is approximately 71%

when α = 1 (i.e., under the SDC scheme)

• There are no significant

differences between the hit rates

• f the IQA, FQA and DQA

schemes

Evaluation (3) Power saving ratio achieved with different target power saving ratios with the three queue management schemes

• α’ increases with α, and the IQA

and DQA schemes perform better than FQA

• The reason is that FQA

implements the FIFO queue with a size limit of L = 1, 000, and the selected threshold Sthresh is not usually representative of the true distribution of turn events; hence, there is a large number of false alarms

Evaluation (4) Location estimation errors using different target power saving ratios

• The distance error increases with

α: loss of location accuracy is the the trade-off reduced energy consumption

• TDA scheme improves the location

accuracy significantly in EBT

• The average location estimation

error is

• About 120m when the TDA

scheme is not applied (i.e., α = 0)

• About 80m (i.e., a 33%

improvement) with the TDA scheme and α = 0.95

Evaluation (5) Power saving ratio achieved with different sampling rates and target power saving ratios

EBT achieves a good power saving ratio and detects nearly all the turn events under different values

Evaluation

• Evaluation of EBT
• Evaluation of IFC

Experimental setup

• We implement the IFC scheme using the open-source PostgreSQL database

(version 8.4.4) and the PostGIS spatial database extension (version 1.5.1)

• Data size
• I frame (32 bytes)
• Point data type (16 bytes) for

location information (x and y)

• Timestamp data type (8 bytes)

for time information (z)

• Integer data type (4 bytes) for

sequence numbers (i) and trajectory identifiers (u)

• O frame (10 bytes)
• Integer data type (4 bytes) for I

frame sequence number (i)

• Short Integer data type (2 bytes)

for offsets Dx, Dy, and Dz

Evaluation (6) Comparison of theoretical compression ratio and compression ratio achieved

• Two curves are nearly
• verlapped completely
• Compression ratio is lower

than 0.5 after the value of n becomes larger than 4

Evaluation (7) Comparison of the results of the IFC, OPW, TDTR, STTrace, Uniform Sample, and DP schemes

• We use an exhaustive set of

configurations to observe their Pareto frontiers between the average distance error and the compression ratio

• IFC scheme outperforms the
• ther schemes significantly and

always achieves the “Pareto

• ptimum”
• IFC scheme is lossless
• IFC’s distance error is zero

despite the different compression ratios achieved

Conclusion

• A two-layer approach to reduce the energy consumption of MLSAs
• MLSA GPS tracking layer: Event-based GPS tracking approach

(EBT)

• MLSA data communication layer: Inter-Frame Coding (IFC)
• Simulations based on a real dataset
• EBT achieves a good power saving ratio while maintaining acceptable

location estimation error

• IFC is lossless and effective in MLSA data compression
• The solution is simple and effective, and it is generalizable to other

mobile location sensing applications