Crowdsourced Trace Similarity with Smartphones

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.

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
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:

Ø System : Pentium IV 2.4 GHz.
Ø Hard Disk : 40 GB.
Ø Floppy Drive : 1.44 Mb.
Ø Monitor : 15 VGA Colour.
Ø Mouse : Logitech.
Ø Ram : 512 Mb.
Ø MOBILE : ANDROID

SOFTWARE REQUIREMENTS:

Ø Operating system : Windows XP.
Ø Coding Language : Java 1.7
Ø Tool Kit : Android 2.3
Ø 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

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