Crowdsourced trace similarity with smartphones

1. Crowdsourced Trace Similarity with Smartphones
ABSTRACT:
Smartphones are nowadays equipped with a number of sensors, such as WiFi,
GPS, accelerometers, etc. This capability allows smartphone users to easily engage
in crowdsourced computing services, which contribute to the solution of complex
problems in a distributed manner. In this work, we leverage such a computing
paradigm to solve efficiently the following problem: comparing a query trace Q
against a crowd of traces generated and stored on distributed smartphones. Our
proposed framework, coined SmartTraceþ, provides an effective solution without
disclosing any part of the crowd traces to the query processor. SmartTraceþ, relies
on an in-situ data storage model and intelligent top-K query processing algorithms
that exploit distributed trajectory similarity measures, resilient to spatial and
temporal noise, in order to derive the most relevant answers to Q. We evaluate our
algorithms on both synthetic and real workloads. We describe our prototype
system developed on the Android OS. The solution is deployed over our own
SmartLab testbed of 25 smartphones. Our study reveals that computations over
SmartTraceþ result in substantial energy conservation; in addition, results can be
computed faster than competitive approaches.

2. EXISTING SYSTEM:
In our previous work, we have already paved the way toward trajectory processing
techniques in a distributed manner (i.e., without percolating each and every user
geolocation to a central authority.) However, those were both agnostic in terms of
energy and time constraints that arise in a smartphone network, but also in respect
to the trajectory trace disclosure issues (i.e., they assumed that the query processor
can arbitrarily access the distributed trajectories.)
DISADVANTAGES OF EXISTING SYSTEM:
Services assume that the user trajectories are stored on a centralized or cloud-like
infrastructure prior to query execution.
PROPOSED SYSTEM:
In this paper, we present a crowdsourced trace similarity search framework, called
SmartTraceþ, which enables the execution of queries in the form: “Report the users
that move more similar to Q, where Q is some query trace.” The notion of
similarity captures the traces (i.e., trajectories) that differ only slightly, in the
whole sequence, from the query Q. Our framework enables the execution of such
queries in both outdoor environments (using GPS) and indoor environments (using
WiFi Received-Signal- Strength), without disclosing the traces of participating
users to the querying node

3. ADVANTAGES OF PROPOSED SYSTEM:
1. Smartphones have both expensive communication mediums but also asymmetric
upload/download links, thus by continuously transferring data to the query
processor can both deplete the precious smartphone battery faster, increase user-
perceived delays, but can also quickly degrade the network health
2. Continuously disclosing user positional data to a central entity might
compromise user privacy in serious ways. This creates services that have recently
raised many concerns, especially for social networking services (e.g., Facebook,
Buzz, etc.) and smartphone services
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech.
 Ram : 512 Mb.
 MOBILE : ANDROID

4. SOFTWARE REQUIREMENTS:
 Operating system : Windows XP.
 Coding Language : Java 1.7
 Tool Kit : Android 2.3
 IDE : Eclipse
REFERENCE:
Demetrios Zeinalipour-Yazti, Member, IEEE, Christos Laoudias, Student Member,
IEEE, Constandinos Costa, Michail Vlachos, Maria I. Andreou, and Dimitrios
Gunopulos, Member, IEEE, “Crowdsourced Trace Similarity with Smartphones”,
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,
VOL. 25, NO. 6, JUNE 2013