Querying Smartphone Networks with SmartTrace

1
Querying Smartphone Networks with SmartTrace
Demetris Zeinalipour Department of Computer
Science University of Cyprus
Colloquium Department of Computer Science,
University of Pittsburgh, Sennott Square -
Seminar Room 5317, 1400-1500, Friday, April
29th, 2011.
http//www.cs.ucy.ac.cy/dzeina/
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Acknowledgments

• "Disclosure-free GPS Trace Search in Smartphone
  Networks", D. Zeinalipour-Yazti, C. Laoudias, M.
  I. Andreou, D. Gunopulos, 12th Intl. Conf. on
  Mobile Data Management (MDM'11), IEEE Computer
  Society, Lulea, Sweden, June 6-9, 2011.
• SmartTrace Finding Similar Trajectories in
  Smartphone Networks without Disclosing the
  Traces, C. Costa, C. Laoudias, D.
  Zeinalipour-Yazti, D. Gunopulos Demo at the 27th
  IEEE Intl. Conf. on Data Engineering (ICDE11),
  Hannover, Germany, 2011.
• Other Related Work
• Distributed Spatio-Temporal Similarity Search,
  D. Zeinalipour-Yazti, et. al, In 15th ACM
  Conference on Information and Knowledge
  Management (CIKM06), Arlington, VA, USA, 2006.
• "Finding the K Highest-Ranked Answers in a
  Distributed Network", D. Zeinalipour-Yazti, , Z.
  Vagena, D. Gunopulos and V. Kalogeraki, V.
  Tsotras, M. Vlachos, N. Koudas, D. Srivastava,
  Computer Networks (ComNet), vol. 53, issue 9, pp.
  1431-1449, Elsevier Press, 2009.

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Smartphones

• Smartphone a mobile device (phone, tablet,
  slate) that offers more computing ability than a
  basic feature phone (e.g., one running JavaME)
  and a dumb phone.
• Computing Ability CPU, Memory Storage,
  Networking, Sensing.
• Example (Motorola Atrix 4G)
• Processing 1 GHz dual core
• RAM Flash Storage 1GB 48GB, respectively
• Networking WiFi, 3G (Mbps) / 4G (100Mbps1Gbps)
• Sensing Proximity, Ambient Light, Accelerometer,
  Microphone, Geographic Coordinates based on AGPS
  (fine), WiFi or Cellular Towers (coarse).

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Applications of Smartphones Sensors
Camera Find the right coupons on the right
moment!
Microphone Medical Stethoscope.
Compass / Accelerometer Augmented Reality
GPS/WIFI/Cell Smartphone Social Networks
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Road Traffic Mapping (RTM) Past
Mapping Road Traffic is traditionally carried out
with fixed cameras sensors mounted on roadsides
http//www.rta.nsw.gov.au/
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RTM with Smartphone Networks Future
Opportunistic (w/ user interaction) and
Participatory Sensing (w/out user interaction)
Mapping the Road traffic by collecting WiFi
signals.
Received Signal Strength (RSS) power present in
WiFi radio signal
?
?
G
?
?
A
B
Graphics courtesy of A .Thiagarajan et. al.
Vtrack Accurate, Energy-Aware Road Traffic
Delay Estimation using Mobile Phones, In
Sensys09, pages 85-98. ACM, (Best Paper) MITs
CarTel Group
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Collecting Trace Data on Smartphones

• Popular Smartphones are already collecting
  positional information (i.e., user-agnostic
  sensing)
• Example A (iPhone logs User Positional Data)
• iPhone collects Longitude / Latitude (or
  triangulated Cell Tower position) info locally on
  your smartphone (and iTunes backup).
• The unencrypted log file is even migrated between
  devices!
• Displaying your location history on a Map
  http//petewarden.github.com/iPhoneTracker/
• Example B (Android logs/uploads Access Point
  data)
• There are rumors that Google uses its Android OS
  for collecting (wardriving) positional info about
  WiFi Access Points (APs).
• When the phone detects a WiFi AP, it sends the
  BSSID (MAC address) of the router along with
  signal strength and GPS coordinates over to the
  Geolocation database at Google
• This enables a variety of interesting queries
  (e.g., find the location of your WiFi AP)
  http//samy.pl/androidmap/

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Collecting Trace Data on Smartphones
Mapping your iPhone locations with the popular
software (points are constrained to a grid, so
the exact location is not revealed in the
visualization)
Circle Size/Color indicates the frequency of
visits to a particularly spatial location
The availability of such data on a device enables
applications like SmartTrace, presented next.
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Presentation Outline

• Introduction
• System Model and Problem Formulation
• Background on Trajectory Similarity
• The SmartTrace Algorithm
• Experimental Evaluation
• Future Work
• Other Related Research Works

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System Model and Problem Formulation
Find the K most similar trajectories to Q without
pulling together all traces at QN
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Constrains and Objectives

• Dont Disclose the Users Trajectory to QN
• Social sites are already undergoing significant
  privacy restructuring (e.g., google buzz,
  facebook)
• Trajectories are large (270MB/year with 2s
  samples)
• Minimize Net Traffic and Local Processing
• 3G/4G and WiFi traffic i) depletes smartphone
  battery and ii) degrades network health
• In 2009 ATTs customers affected by iPhone
  release.

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Presentation Outline

• Introduction
• System Model and Problem Formulation
• Background on Trajectory Similarity
• The SmartTrace Algorithm
• Experimental Evaluation
• Future Work
• Other Related Research Works

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Trajectory Similarity Search

• Problem Compare the query with all the
  distributed sequences and return the k most
  similar sequences to the query.
• Similarity between two objects A, B is associated
  with a distance function (see next)

K
?
Query
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System Model and Problem Definition

• Lp-norms are the simplest way to compare
  trajectories (e.g., Euclidean, Manhattan, etc.)
• Lp-norms are fast (i.e., O(n)), but inaccurate.
• No Flexible matching in time. (miss out-of-phase)
• No Flexible matching in space. (miss outliers)

P1 Manhattan P2 Euclidean
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Longest Common Subsequence

• Longest Common Subsequence (LCSS) Given
  strings A and B, LCSS is the longest string that
  is a subsequence of both A and B
• extensively utilized in text similarity, e.g.,
• String CGATAATTGAGA
• Substring (contiguous) CGA
• SubSequence (not necess. conti.) AAGAA
• Find the LCSS of the following 1D-trajectory
• A 3, 2, 5, 7, 4, 8, 10, 7
• B 2, 5, 4, 7, 3, 10, 8, 6
• LCSS (2, 5, 4, 7) or (2, 5, 7, 10) or

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Longest Common Subsequence

• A Dynamic Programming algorithm for this problem
  requires O(AB) time.
• However we can compute it in O(d(AB)), if we
  limit the matching within a time window of d.

Time
Procesing a trajectory with size
Ai1.8MB, requires 111 seconds on a smartphone
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LCSS Definition

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LCSS(MBEQ, Ai) Bounding Above LCSS
Indexing multi-dimensional time-series with
support for multiple distance measures, M.
Vlachos, M. Hadjieleftheriou, D. Gunopulos, E.
Keogh, In KDD 2003.
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Presentation Outline

• Introduction
• System Model and Problem Formulation
• Background on Trajectory Similarity
• The SmartTrace Algorithm
• Experimental Evaluation
• Future Work
• Other Related Research Works

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SmartTrace Algorithm Outline

• An intelligent top-K processing algorithm for
  identifying the K most similar trajectories to Q
  in a distributed environment.

• Step A Conduct the cheap linear-time
  LCSS(MBEQ,Ai) computation on the smartphones to
  approximate the answer.
• Step B Exploit the approximation to identify the
  correct answer by iteratively asking specific
  nodes to conduct LCSS(Q, Ai).

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SmartTrace Algorithm (1/2)

• Input Query Trajectory Q, m Target Trajectories,
  Result Preference K (K ltlt m), Iteration Step
  Increment ?.
• Output K trajectories most similar to Q.
• At the query node QN
• Upper Bound (UB) Computation Instruct each of
  the m smartphones to invoke a computation of the
  linear-time LCSS(MBEQ,Ai) (i m).
• Collection of UB Receive the UBs of all m
  trajectories participating in the query and add
  those scores to the METADATA vector stored at QN.
  Let METADATA be sorted in descending order based
  on the UB scores.

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SmartTrace Algorithm (2/2)

• Full Computation Ask the ? 1 (? K) highest
  UB nodes to compute LCSS(Q,Ai) and then send back
  their ? full scores.
• Termination Condition If the next highest UB is
  smaller than the K-th largest full match then
  stop else goto step 3 in order to identify the
  next ? cand.
• (Tentative) Ship Matching If the termination
  condition has been met, tentatively ship the
  respective matches to QN, based on some local
  trace disclosure policy.

STOP
CONTINUE
A
A
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SmartTrace Execution
Query Find the K2 most similar trajectories to Q
Ask A4 A2 for the computation of LCSS
Stop if Kth LCSS gt Last UB
?Kth LCSS
?
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SmartTrace Protocol
Server (QN)
Participating Node
Querying Node
LCSS(MBEQ,Ai)
1
2
LCSS(Q,Ai)
3
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Presentation Outline

• Introduction
• System Model and Problem Formulation
• Background on Trajectory Similarity
• The SmartTrace Algorithm
• Experimental Evaluation
• Future Work
• Other Related Research Works

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Experimental Methodology

• Datasets Queries
• Oldenburg (Realistic) IAPG Institute, Germany
• Dataset
• 2,000 Car Trajectories moving in the city of
  Oldenburg.
• Trajectory Length 11,731 7,193 points
• Queryset
• Randomly sampled out of the original dataset with
  interpolated noise
• Trajectory Length 100 points.
• GeoLife (Real) Microsoft Research Asia
• Dataset
• 1,100 Human Trajectories over the city of Beijing
  in the time frame 2007-2009 (1 sample / 5 seconds
  or 1 sample / 10 meters)
• Trajectory Length 190,110 126,590 points
• Queryset
• Randomly sampled out of the original dataset with
  interpolated noise
• Trajectory Length 500 points

http//iapg.jade-hs.de/personen/brinkhoff/generato
r/
http//research.microsoft.com/en-us/projects/geoli
fe/
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Experimental Methodology

• Algorithms
• Centralized (C) 1) Ship Trajectories to QN 2)
  Conduct centralized LCSS(Q,Ai) computation
• Decentralized (D) 1) Ship Q to all nodes 2)
  Conduct the LCSS(Q,Ai) computation locally
• SmartTrace (ST) 1) Ship Q to all nodes 2)
  Conduct the linear-time LCSS(MBEQ,Ai)
  computation 3) Iteratively ask specific nodes to
  calculate LCSS(Q,Ai)
• Metrics
• Execution Time (T) The total time to answer the
  query.
• Amortized Energy (E) per Device average energy
  consumed by a smartphone for answering the query
  (based on Powertutor profile Univ. of Michigan)
• d and e (temporal and spatial matching)
  parameters are kept constant for all experiments.
  The values affect the matching granularity, which
  is similar for all algorithms.

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Experimental Results(Execution Time)
Result I ST and D are 1
order of magnitude faster than C. Expl ST and D
rely mainly on processing while C relies on data
transfer, which is slow! Result II
ST is faster than D (i.e., 17
and 8, respectively for the two datasets)
10x
Expl Attributed to the variable length of
trajectories (i.e., D always compares against
the longest trajectory while ST compares against
it only if it belongs to the candidate S-set)
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Experimental Results(Energy Consumption)

• Result III
• C is network-intensive while ST and D are
  cpu-intensive
• Expl ST and D have very little network activity
  (i.e., which accounts for 2.59mJ and 2.29mJ,
  respectively)
• Result IV
• - ST is 67 more energy efficient than D
• ST is 81 more energy efficient than C
• Expl ST doesnt execute LCSS(Q,Ai) on all nodes.

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Experimental Results(Varying K Parameter)
Result V Performance results are the same when
the preference K is constraint within 1 of the
answer set (typical for top-K query processing
algorithms).
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Experimental Results(Varying the ? Parameter)

• The ? parameter defines how aggressively ST
  explores the top-k result set (Higher ? gt Faster
  Convergence)
• Theorem ST requires O(m/?) iterations in the
  worse case, where ? denotes the step increment
  and m the number of trajectories

Result VI (?-convergence) Our algorithm
convergences in 7.6 and 9.3 iterations, on
average, for the Oldenburg and Geolife datasets,
respectively.
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Prototype System (GPS)

• SmartTrace Implemented as a Client-Server
  text-based protocol
• Server implemented in JAVA (4,500 LOC)
• Client implemented in JAVA on Android (2,500 LOC
  XML files)

Query
Device B
Device C
SmartTrace Finding Similar Trajectories in
Smartphone Networks without Disclosing the
Traces, C. Costa, C. Laoudias, D.
Zeinalipour-Yazti, D. Gunopulos Demo at the 27th
IEEE Intl. Conf. on Data Engineering (ICDE11),
Hannover, Germany, 2011.
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Prototype System (GPS)
Answer With Trace
Privacy Setting
Answer
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Prototype System (RSS)
The SmartTrace algorithm works equally well for
indoor environments (using RSS)
?
?
G
?
?
A
B
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Presentation Outline

• Introduction
• System Model and Problem Formulation
• Background on Trajectory Similarity
• The SmartTrace Algorithm
• Experimental Evaluation
• Future Work
• Other Related Research Works

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Future Work

• Evaluate the SmartTrace prototype system over the
  SmartNet testbed we are developing.
• Develop extensions that do not require the
  iterative execution of LCSS(Q,Ai) but can
  postpone them to a final post-processing step.
• Develop new Similarity Measures for (Highly
  Dimensional) RSS Trajectories.
• Develop a killer application for our algorithm
  and deploy the executable APK on Google Market to
  gain further experiences with this. Possibly also
  develop a client for iPhone devices.

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SmartTrace Applications

• Our framework finds applications in a wide range
  of domains
• Intelligent Transportation Systems Find whether
  a new bus route is similar to the trajectories of
  K other users.
• Social Networks Find whether there is a cycling
  route from MOMA to the Julliard
• GeoLife, GPS-Waypoints, Sharemyroutes, etc. offer
  centralized counterparts.
• Habitant Monitoring Find zebras that moved more
  similarly to zebra X before it got injured.

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Presentation Outline

• Introduction
• System Model and Problem Formulation
• Background on Trajectory Similarity
• The SmartTrace Algorithm
• Experimental Evaluation
• Future Work
• Other Related Research Works

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SmartNet Programming Cloud

• Currently, there are no testbeds (like motelab,
  planetlab) for realistically emulating and
  prototyping Smartphone Network applications and
  protocols at a large scale.
• Currently applications are tested in emulators.
• Drawbacks
• Sensors are not emulated.
• It is difficult to concurrently
• re-program several devices
• between the devices.
• MobNet project (at UCY 2010-2012), will develop
  an innovative cloud testbed of mobile sensor
  devices using 50 Android devices.

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SmartNet Programming Cloud
SmartNet
Install APK, Upload File, Reboot,
Programming cloud for the development of
smartphone network applications protocols as
well as experimentation with real smartphone
devices.
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SmartNet Programming Cloud
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SmartP2P Peer-to-Peer Search in Smartphone
Networks
Finding objects (e.g., images, videos, etc.) in
a social neighborhood, without the necessity of
having the objects disclosed to the social
network provider.
"Multi-Objective Query Optimization in Smartphone
Networks" A. Konstantinidis, D.
Zeinalipour-Yazti, P. Andreou, G. Samaras, 12th
International Conference on Mobile Data
Management (MDM'11) (Short Paper), IEEE Computer
Society, Lulea, Sweden, June 6-9, 2011.
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PROXIMITY Finding Close-by Smartphones

• Problem Identifying geographically close-by
  devices continuously for all smartphones.
• Constraints
• Privacy Users do not want to expose their
  precise location (we utilize location obfuscation
  techniques)
• Complexity Computing the above answers for
  millions of devices requires takes time while the
  answer need to be ready every few seconds.

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PROXIMITY ???es? Ge?t?????? S?s?e???
Application Proximity Chat
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Querying Smartphone Networks with SmartTrace
Demetris Zeinalipour Department of Computer
Science University of Cyprus Thanks! Questions?
Colloquium Department of Computer Science,
University of Pittsburgh, Sennott Square -
Seminar Room 5317, 1400-1500, Friday, April
29th, 2011.
http//www.cs.ucy.ac.cy/dzeina/